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diff --git a/TP1/1 - Practical number 1.ipynb b/TP1/1 - Practical number 1.ipynb
index bc5d11b8..9b92d91d 100644
--- a/TP1/1 - Practical number 1.ipynb	
+++ b/TP1/1 - Practical number 1.ipynb	
@@ -17,64 +17,110 @@
    "source": [
     "- **Question 1:** *Name three types of API protocols. Briefly explain the primary use of each.*\n",
     "\n",
-    "  - \n",
-    "\n",
-    "  - \n",
-    "\n",
-    "  - \n",
-    "\n",
+    "  - REST (Representational State Transfer)\n",
+    "- Primary Use: \n",
+    "REST is widely used for web services and web APIs due to its simplicity and scalability. It leverages standard HTTP methods (GET, POST, PUT, DELETE) for communication and works with resources, which are typically represented as URLs.\n",
+    "- Key Features: \n",
+    "Stateless communication, caching support, human-readable URLs, and easy integration with web-based applications.\n",
+    "\n",
+    "  - SOAP (Simple Object Access Protocol)\n",
+    "- Primary Use: \n",
+    "SOAP is used for enterprise-level web services where security, reliability, and strict standards are essential. It is a protocol that uses XML-based messaging for communication between client and server.\n",
+    "- Key Features: \n",
+    "Built-in security (WS-Security), error handling, supports transactions, and operates over multiple protocols (HTTP, SMTP, etc.).\n",
+    "\n",
+    "\n",
+    "  - (g)RPC (gRPC Remote Procedure Call)\n",
+    "- Primary Use: \n",
+    "gRPC is a high-performance, open-source framework developed by Google for remote procedure calls (RPC). It is often used in microservices architectures where services need to communicate with low latency and high throughput. It's also suitable for mobile applications and IoT due to its efficiency.\n",
+    "- Key Features:\n",
+    "Protocol Buffers (Protobuf): gRPC uses Protobuf as its interface definition language (IDL) to define the structure of messages, making communication fast and data compact.\n",
+    "HTTP/2: Supports HTTP/2, which allows for features like multiplexing, flow control, and low-latency communication.\n",
+    "Bi-Directional Streaming: Supports streaming requests and responses, making it suitable for real-time applications.\n",
+    "Code Generation: Automatically generates client and server code from .proto files, speeding up development.\n",
     "  \n",
     "\n",
     "\n",
     "- **Question 2:** *What are the HTTP response code families? And what do they mean?*\n",
     "\n",
-    "  - \n",
-    "  - \n",
-    "  - \n",
-    "  - \n",
-    "  - \n",
+    "  - Informational Responses(100 Continue): meaning the request was received, and the process is continuing.\n",
+    "  - Success Responses(200 OK): indicate that the request was successfully received, understood, and accepted by the server.\n",
+    "  - Redirection Responses(301 Moved Permanently): mean that further action is needed from the client to complete the request, usually a redirection to another URL.\n",
+    "  - Client Error Responses(404 Not Found): indicate that there was an error on the client side, such as a bad request or unauthorized access.\n",
+    "  - Server Error Responses(500 Internal Server Error): mean that the server encountered an error or is unable to perform the request.\n",
     "\n",
     "  Understanding these families helps developers diagnose and troubleshoot issues during API interactions.\n",
     "\n",
     "- **Question 3:** *What do the HTTP response codes 201, 401, and 404 mean?*\n",
     "\n",
     "  - **201:** \n",
+    "  Created: The HTTP 201 Created status code indicates that the request was successful and that a resource was created as a result. The new resource is actually created before the response is returned and this new resource is returned in the body of the message. Its location is indicated by the request URL or the contents of the Location header.\n",
     "  - **401:** \n",
+    "  401 Unauthorized\n",
+    "  Although the HTTP standard indicates ‘unauthorised’, the semantics of this response correspond to ‘unauthenticated’: the client must authenticate itself in order to obtain the requested response.\n",
     "  - **404:** \n",
+    "  404 Not Found\n",
+    "  The server has not found the requested resource.\n",
     "\n",
     "- **Question 4:** *Name the 4 basic HTTP verbs.*\n",
     "\n",
-    "  - \n",
-    "  - \n",
-    "  - \n",
-    "  - \n",
+    "  - Get : Obtain a ressource\n",
+    "  - Put : Modify a ressource entirely\n",
+    "  - Post : Create a resource\n",
+    "  - Delete : Remove a resource\n",
     "\n",
     "- **Question 5:** *Explain the difference between PUT and PATCH?*\n",
     "\n",
-    "  - **PUT:** :\n",
+    "  - **PUT:** : Modify a ressource entirely\n",
     "\n",
-    "  - **PATCH:** :\n",
+    "  - **PATCH:** : Partially modify a ressource\n",
     "\n",
     "- **Question 6:** *Name at least two data formats commonly used in API exchanges.*\n",
     "\n",
-    "  - \n",
+    "  - JSON (JavaScript Object Notation)\n",
     "\n",
-    "  - \n",
+    "  -  XML (Extensible Markup Language)\n",
     "\n",
     "- **Question 7:** *How can you verify the validity of a resource without getting the entire response?*\n",
     "\n",
-    "  - \n",
+    " To verify the validity of a ressource in an API without retrieving the entire response, you can use the Http Head. \n",
+    "- The HEAD Method : The HEAD method is similar to GET but retrieves only the headers of the response, not the body. This makes it ideal for checking resource validity without the overhead of transferring the full content\n",
     "\n",
     "- **Question 8:** *What are the main concepts of REST? (name them)*\n",
+    "The main concepts of REST (Representational State Transfer) are:\n",
+    "\n",
+    "1. **Resources**: The key abstraction of information in REST, which can be any named piece of information or data[3].\n",
+    "\n",
+    "2. **Uniform Interface**: A standardized way of interacting with resources using HTTP methods[1][3].\n",
+    "\n",
+    "3. **Client-Server Architecture**: Separation of concerns between the client and server components[3].\n",
+    "\n",
+    "4. **Statelessness**: Each request from client to server must contain all necessary information[1][3].\n",
+    "\n",
+    "5. **Layered System**: A hierarchical architecture that constrains component behavior[3].\n",
+    "\n",
+    "6. **Cacheability**: The ability to store frequently accessed data on the client side[2].\n",
+    "\n",
+    "7. **Code on Demand** (optional): The ability for servers to extend client functionality by sending executable code[3].\n",
+    "\n",
+    "8. **Resource Representations**: The state of a resource at a given time, including data, metadata, and hypermedia links[3].\n",
+    "\n",
+    "9. **Self-descriptive Messages**: Requests and responses that contain all information needed for processing[2].\n",
+    "\n",
+    "10. **HATEOAS** (Hypermedia as the Engine of Application State): The use of hyperlinks in API responses to guide clients through the application[4].\n",
+    "\n",
     "\n",
-    "  - \n",
-    "  - \n",
-    "  - \n",
-    "  - \n",
     "\n",
     "- **Question 9:** *Can you explain one of the main concepts of your choice from among those you mention? (Give an example if possible)*\n",
     "\n",
-    "  - \n",
+    "  - Statelessness in REST\n",
+    "Statelessness means that each request from a client to a server must contain all the information necessary to understand and process the request. The server should not store any client context between requests. Each request is treated as an independent transaction, unrelated to any previous requests.\n",
+    "  - Example: Book management API\n",
+    "- Each request contains all necessary information (the token for authentication).\n",
+    "- The server doesn't need to maintain session information.\n",
+    "Any server in a cluster can handle the request without needing access to centralized session data.\n",
+    "- This stateless design improves scalability, simplifies server-side architecture, and allows for easier load balancing across multiple servers.\n",
+    "\n",
     "\n",
     "In the subsequent sections, we will delve into practical exercises to apply and deepen our understanding of these concepts using SOAP, REST, and GraphQL APIs.\n"
    ]
@@ -130,11 +176,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The capital of Canada is: Ottawa\n"
+     ]
+    }
+   ],
    "source": [
     "import requests\n",
+    "import xml.etree.ElementTree as ET\n",
+    "\n",
     "# SOAP request URL\n",
     "url = \"http://webservices.oorsprong.org/websamples.countryinfo/CountryInfoService.wso\"\n",
     "\n",
@@ -142,7 +198,7 @@
     "payload = \"\"\"<soap:Envelope xmlns:soap=\"http://schemas.xmlsoap.org/soap/envelope/\">\n",
     "                <soap:Body>\n",
     "                    <CapitalCity xmlns=\"http://www.oorsprong.org/websamples.countryinfo\">\n",
-    "                        <sCountryISOCode>US</sCountryISOCode>\n",
+    "                        <sCountryISOCode>CA</sCountryISOCode>\n",
     "                    </CapitalCity>\n",
     "                </soap:Body>\n",
     "                </soap:Envelope>\"\"\"\n",
@@ -150,10 +206,18 @@
     "headers = {\n",
     "    'Content-Type': 'text/xml; charset=utf-8'\n",
     "}\n",
+    "\n",
     "# POST request\n",
     "response = requests.request(\"POST\", url, headers=headers, data=payload)\n",
     "\n",
-    "print(response.text)"
+    "# Parse the XML response\n",
+    "root = ET.fromstring(response.text)\n",
+    "\n",
+    "# Extract the capital city\n",
+    "capital = root.find(\".//{http://www.oorsprong.org/websamples.countryinfo}CapitalCityResult\").text\n",
+    "\n",
+    "#print(response.text)\n",
+    "print(f\"The capital of Canada is: {capital}\")"
    ]
   },
   {
@@ -223,19 +287,735 @@
     "- https://swapi.dev/documentation"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 1: Introduction"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Available resources in SWAPI:\n",
+      "- people: https://swapi.dev/api/people/\n",
+      "- planets: https://swapi.dev/api/planets/\n",
+      "- films: https://swapi.dev/api/films/\n",
+      "- species: https://swapi.dev/api/species/\n",
+      "- vehicles: https://swapi.dev/api/vehicles/\n",
+      "- starships: https://swapi.dev/api/starships/\n"
+     ]
+    }
+   ],
    "source": [
-    "url = \".......\"\n",
-    "params = {\n",
-    "}\n",
+    "url = \"https://swapi.dev/api/\"\n",
+    "params = {}\n",
+    "\n",
     "\n",
     "response = requests.get(url, params=params)\n",
     "data = response.json()\n",
-    "data"
+    "\n",
+    "if response.status_code == 200:\n",
+    "    # Parse the JSON response\n",
+    "    data = response.json()\n",
+    "    \n",
+    "    print(\"Available resources in SWAPI:\")\n",
+    "    for resource, endpoint in data.items():\n",
+    "        print(f\"- {resource}: {endpoint}\")\n",
+    "else:\n",
+    "    print(f\"Error: Unable to retrieve data. Status code: {response.status_code}\")\n",
+    "\n",
+    "#print(\"\\nRaw JSON data:\")\n",
+    "#data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 2 : Retrieve Character Information"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Name: Luke Skywalker, Gender: male, Height: 172,Mass:77,Hair Color:blond,Birth Year:19BBY\n",
+      "Name: C-3PO, Gender: n/a, Height: 167,Mass:75,Hair Color:n/a,Birth Year:112BBY\n",
+      "Name: R2-D2, Gender: n/a, Height: 96,Mass:32,Hair Color:n/a,Birth Year:33BBY\n",
+      "Name: Darth Vader, Gender: male, Height: 202,Mass:136,Hair Color:none,Birth Year:41.9BBY\n",
+      "Name: Leia Organa, Gender: female, Height: 150,Mass:49,Hair Color:brown,Birth Year:19BBY\n",
+      "Name: Owen Lars, Gender: male, Height: 178,Mass:120,Hair Color:brown, grey,Birth Year:52BBY\n",
+      "Name: Beru Whitesun lars, Gender: female, Height: 165,Mass:75,Hair Color:brown,Birth Year:47BBY\n",
+      "Name: R5-D4, Gender: n/a, Height: 97,Mass:32,Hair Color:n/a,Birth Year:unknown\n",
+      "Name: Biggs Darklighter, Gender: male, Height: 183,Mass:84,Hair Color:black,Birth Year:24BBY\n",
+      "Name: Obi-Wan Kenobi, Gender: male, Height: 182,Mass:77,Hair Color:auburn, white,Birth Year:57BBY\n",
+      "Name: Anakin Skywalker, Gender: male, Height: 188,Mass:84,Hair Color:blond,Birth Year:41.9BBY\n",
+      "Name: Wilhuff Tarkin, Gender: male, Height: 180,Mass:unknown,Hair Color:auburn, grey,Birth Year:64BBY\n",
+      "Name: Chewbacca, Gender: male, Height: 228,Mass:112,Hair Color:brown,Birth Year:200BBY\n",
+      "Name: Han Solo, Gender: male, Height: 180,Mass:80,Hair Color:brown,Birth Year:29BBY\n",
+      "Name: Greedo, Gender: male, Height: 173,Mass:74,Hair Color:n/a,Birth Year:44BBY\n",
+      "Name: Jabba Desilijic Tiure, Gender: hermaphrodite, Height: 175,Mass:1,358,Hair Color:n/a,Birth Year:600BBY\n",
+      "Name: Wedge Antilles, Gender: male, Height: 170,Mass:77,Hair Color:brown,Birth Year:21BBY\n",
+      "Name: Jek Tono Porkins, Gender: male, Height: 180,Mass:110,Hair Color:brown,Birth Year:unknown\n",
+      "Name: Yoda, Gender: male, Height: 66,Mass:17,Hair Color:white,Birth Year:896BBY\n",
+      "Name: Palpatine, Gender: male, Height: 170,Mass:75,Hair Color:grey,Birth Year:82BBY\n",
+      "Name: Boba Fett, Gender: male, Height: 183,Mass:78.2,Hair Color:black,Birth Year:31.5BBY\n",
+      "Name: IG-88, Gender: none, Height: 200,Mass:140,Hair Color:none,Birth Year:15BBY\n",
+      "Name: Bossk, Gender: male, Height: 190,Mass:113,Hair Color:none,Birth Year:53BBY\n",
+      "Name: Lando Calrissian, Gender: male, Height: 177,Mass:79,Hair Color:black,Birth Year:31BBY\n",
+      "Name: Lobot, Gender: male, Height: 175,Mass:79,Hair Color:none,Birth Year:37BBY\n",
+      "Name: Ackbar, Gender: male, Height: 180,Mass:83,Hair Color:none,Birth Year:41BBY\n",
+      "Name: Mon Mothma, Gender: female, Height: 150,Mass:unknown,Hair Color:auburn,Birth Year:48BBY\n",
+      "Name: Arvel Crynyd, Gender: male, Height: unknown,Mass:unknown,Hair Color:brown,Birth Year:unknown\n",
+      "Name: Wicket Systri Warrick, Gender: male, Height: 88,Mass:20,Hair Color:brown,Birth Year:8BBY\n",
+      "Name: Nien Nunb, Gender: male, Height: 160,Mass:68,Hair Color:none,Birth Year:unknown\n",
+      "Name: Qui-Gon Jinn, Gender: male, Height: 193,Mass:89,Hair Color:brown,Birth Year:92BBY\n",
+      "Name: Nute Gunray, Gender: male, Height: 191,Mass:90,Hair Color:none,Birth Year:unknown\n",
+      "Name: Finis Valorum, Gender: male, Height: 170,Mass:unknown,Hair Color:blond,Birth Year:91BBY\n",
+      "Name: Padmé Amidala, Gender: female, Height: 185,Mass:45,Hair Color:brown,Birth Year:46BBY\n",
+      "Name: Jar Jar Binks, Gender: male, Height: 196,Mass:66,Hair Color:none,Birth Year:52BBY\n",
+      "Name: Roos Tarpals, Gender: male, Height: 224,Mass:82,Hair Color:none,Birth Year:unknown\n",
+      "Name: Rugor Nass, Gender: male, Height: 206,Mass:unknown,Hair Color:none,Birth Year:unknown\n",
+      "Name: Ric Olié, Gender: male, Height: 183,Mass:unknown,Hair Color:brown,Birth Year:unknown\n",
+      "Name: Watto, Gender: male, Height: 137,Mass:unknown,Hair Color:black,Birth Year:unknown\n",
+      "Name: Sebulba, Gender: male, Height: 112,Mass:40,Hair Color:none,Birth Year:unknown\n",
+      "Name: Quarsh Panaka, Gender: male, Height: 183,Mass:unknown,Hair Color:black,Birth Year:62BBY\n",
+      "Name: Shmi Skywalker, Gender: female, Height: 163,Mass:unknown,Hair Color:black,Birth Year:72BBY\n",
+      "Name: Darth Maul, Gender: male, Height: 175,Mass:80,Hair Color:none,Birth Year:54BBY\n",
+      "Name: Bib Fortuna, Gender: male, Height: 180,Mass:unknown,Hair Color:none,Birth Year:unknown\n",
+      "Name: Ayla Secura, Gender: female, Height: 178,Mass:55,Hair Color:none,Birth Year:48BBY\n",
+      "Name: Ratts Tyerel, Gender: male, Height: 79,Mass:15,Hair Color:none,Birth Year:unknown\n",
+      "Name: Dud Bolt, Gender: male, Height: 94,Mass:45,Hair Color:none,Birth Year:unknown\n",
+      "Name: Gasgano, Gender: male, Height: 122,Mass:unknown,Hair Color:none,Birth Year:unknown\n",
+      "Name: Ben Quadinaros, Gender: male, Height: 163,Mass:65,Hair Color:none,Birth Year:unknown\n",
+      "Name: Mace Windu, Gender: male, Height: 188,Mass:84,Hair Color:none,Birth Year:72BBY\n",
+      "Name: Ki-Adi-Mundi, Gender: male, Height: 198,Mass:82,Hair Color:white,Birth Year:92BBY\n",
+      "Name: Kit Fisto, Gender: male, Height: 196,Mass:87,Hair Color:none,Birth Year:unknown\n",
+      "Name: Eeth Koth, Gender: male, Height: 171,Mass:unknown,Hair Color:black,Birth Year:unknown\n",
+      "Name: Adi Gallia, Gender: female, Height: 184,Mass:50,Hair Color:none,Birth Year:unknown\n",
+      "Name: Saesee Tiin, Gender: male, Height: 188,Mass:unknown,Hair Color:none,Birth Year:unknown\n",
+      "Name: Yarael Poof, Gender: male, Height: 264,Mass:unknown,Hair Color:none,Birth Year:unknown\n",
+      "Name: Plo Koon, Gender: male, Height: 188,Mass:80,Hair Color:none,Birth Year:22BBY\n",
+      "Name: Mas Amedda, Gender: male, Height: 196,Mass:unknown,Hair Color:none,Birth Year:unknown\n",
+      "Name: Gregar Typho, Gender: male, Height: 185,Mass:85,Hair Color:black,Birth Year:unknown\n",
+      "Name: Cordé, Gender: female, Height: 157,Mass:unknown,Hair Color:brown,Birth Year:unknown\n",
+      "Name: Cliegg Lars, Gender: male, Height: 183,Mass:unknown,Hair Color:brown,Birth Year:82BBY\n",
+      "Name: Poggle the Lesser, Gender: male, Height: 183,Mass:80,Hair Color:none,Birth Year:unknown\n",
+      "Name: Luminara Unduli, Gender: female, Height: 170,Mass:56.2,Hair Color:black,Birth Year:58BBY\n",
+      "Name: Barriss Offee, Gender: female, Height: 166,Mass:50,Hair Color:black,Birth Year:40BBY\n",
+      "Name: Dormé, Gender: female, Height: 165,Mass:unknown,Hair Color:brown,Birth Year:unknown\n",
+      "Name: Dooku, Gender: male, Height: 193,Mass:80,Hair Color:white,Birth Year:102BBY\n",
+      "Name: Bail Prestor Organa, Gender: male, Height: 191,Mass:unknown,Hair Color:black,Birth Year:67BBY\n",
+      "Name: Jango Fett, Gender: male, Height: 183,Mass:79,Hair Color:black,Birth Year:66BBY\n",
+      "Name: Zam Wesell, Gender: female, Height: 168,Mass:55,Hair Color:blonde,Birth Year:unknown\n",
+      "Name: Dexter Jettster, Gender: male, Height: 198,Mass:102,Hair Color:none,Birth Year:unknown\n",
+      "Name: Lama Su, Gender: male, Height: 229,Mass:88,Hair Color:none,Birth Year:unknown\n",
+      "Name: Taun We, Gender: female, Height: 213,Mass:unknown,Hair Color:none,Birth Year:unknown\n",
+      "Name: Jocasta Nu, Gender: female, Height: 167,Mass:unknown,Hair Color:white,Birth Year:unknown\n",
+      "Name: R4-P17, Gender: female, Height: 96,Mass:unknown,Hair Color:none,Birth Year:unknown\n",
+      "Name: Wat Tambor, Gender: male, Height: 193,Mass:48,Hair Color:none,Birth Year:unknown\n",
+      "Name: San Hill, Gender: male, Height: 191,Mass:unknown,Hair Color:none,Birth Year:unknown\n",
+      "Name: Shaak Ti, Gender: female, Height: 178,Mass:57,Hair Color:none,Birth Year:unknown\n",
+      "Name: Grievous, Gender: male, Height: 216,Mass:159,Hair Color:none,Birth Year:unknown\n",
+      "Name: Tarfful, Gender: male, Height: 234,Mass:136,Hair Color:brown,Birth Year:unknown\n",
+      "Name: Raymus Antilles, Gender: male, Height: 188,Mass:79,Hair Color:brown,Birth Year:unknown\n",
+      "Name: Sly Moore, Gender: female, Height: 178,Mass:48,Hair Color:none,Birth Year:unknown\n",
+      "Name: Tion Medon, Gender: male, Height: 206,Mass:80,Hair Color:none,Birth Year:unknown\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_all_characters():\n",
+    "    characters = []\n",
+    "    #url = f\"{url}people/\"\n",
+    "    url = \"https://swapi.dev/api/people/\"\n",
+    "    \n",
+    "    while url:\n",
+    "        response = requests.get(url)\n",
+    "        if response.status_code == 200:\n",
+    "            data = response.json()\n",
+    "            characters.extend(data['results'])\n",
+    "            url = data['next']\n",
+    "        else:\n",
+    "            print(f\"Error: {response.status_code}\")\n",
+    "            break\n",
+    "    \n",
+    "    return characters\n",
+    "\n",
+    "all_characters = get_all_characters()\n",
+    "for character in all_characters:\n",
+    "    print(f\"Name: {character['name']}, Gender: {character['gender']}, Height: {character['height']},Mass:{character['mass']},Hair Color:{character['hair_color']},Birth Year:{character['birth_year']}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 3: Retrieve film information"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Title: A New Hope, Director: George Lucas, Release Date: 1977-05-25\n",
+      "Title: The Empire Strikes Back, Director: Irvin Kershner, Release Date: 1980-05-17\n",
+      "Title: Return of the Jedi, Director: Richard Marquand, Release Date: 1983-05-25\n",
+      "Title: The Phantom Menace, Director: George Lucas, Release Date: 1999-05-19\n",
+      "Title: Attack of the Clones, Director: George Lucas, Release Date: 2002-05-16\n",
+      "Title: Revenge of the Sith, Director: George Lucas, Release Date: 2005-05-19\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_all_films():\n",
+    "    films = []\n",
+    "    #url = f\"{url}people/\"\n",
+    "    url = \"https://swapi.dev/api/films/\"\n",
+    "    \n",
+    "    while url:\n",
+    "        response = requests.get(url)\n",
+    "        if response.status_code == 200:\n",
+    "            data = response.json()\n",
+    "            films.extend(data['results'])\n",
+    "            url = data['next']\n",
+    "        else:\n",
+    "            print(f\"Error: {response.status_code}\")\n",
+    "            break\n",
+    "    \n",
+    "    return films\n",
+    "\n",
+    "all_films = get_all_films()\n",
+    "for film in all_films:\n",
+    "    print(f\"Title: {film['title']}, Director: {film['director']}, Release Date: {film['release_date']}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 4: Retrieve planet information"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Name: Tatooine, Population: 200000, Climate: arid\n",
+      "Name: Alderaan, Population: 2000000000, Climate: temperate\n",
+      "Name: Yavin IV, Population: 1000, Climate: temperate, tropical\n",
+      "Name: Hoth, Population: unknown, Climate: frozen\n",
+      "Name: Dagobah, Population: unknown, Climate: murky\n",
+      "Name: Bespin, Population: 6000000, Climate: temperate\n",
+      "Name: Endor, Population: 30000000, Climate: temperate\n",
+      "Name: Naboo, Population: 4500000000, Climate: temperate\n",
+      "Name: Coruscant, Population: 1000000000000, Climate: temperate\n",
+      "Name: Kamino, Population: 1000000000, Climate: temperate\n",
+      "Name: Geonosis, Population: 100000000000, Climate: temperate, arid\n",
+      "Name: Utapau, Population: 95000000, Climate: temperate, arid, windy\n",
+      "Name: Mustafar, Population: 20000, Climate: hot\n",
+      "Name: Kashyyyk, Population: 45000000, Climate: tropical\n",
+      "Name: Polis Massa, Population: 1000000, Climate: artificial temperate \n",
+      "Name: Mygeeto, Population: 19000000, Climate: frigid\n",
+      "Name: Felucia, Population: 8500000, Climate: hot, humid\n",
+      "Name: Cato Neimoidia, Population: 10000000, Climate: temperate, moist\n",
+      "Name: Saleucami, Population: 1400000000, Climate: hot\n",
+      "Name: Stewjon, Population: unknown, Climate: temperate\n",
+      "Name: Eriadu, Population: 22000000000, Climate: polluted\n",
+      "Name: Corellia, Population: 3000000000, Climate: temperate\n",
+      "Name: Rodia, Population: 1300000000, Climate: hot\n",
+      "Name: Nal Hutta, Population: 7000000000, Climate: temperate\n",
+      "Name: Dantooine, Population: 1000, Climate: temperate\n",
+      "Name: Bestine IV, Population: 62000000, Climate: temperate\n",
+      "Name: Ord Mantell, Population: 4000000000, Climate: temperate\n",
+      "Name: unknown, Population: unknown, Climate: unknown\n",
+      "Name: Trandosha, Population: 42000000, Climate: arid\n",
+      "Name: Socorro, Population: 300000000, Climate: arid\n",
+      "Name: Mon Cala, Population: 27000000000, Climate: temperate\n",
+      "Name: Chandrila, Population: 1200000000, Climate: temperate\n",
+      "Name: Sullust, Population: 18500000000, Climate: superheated\n",
+      "Name: Toydaria, Population: 11000000, Climate: temperate\n",
+      "Name: Malastare, Population: 2000000000, Climate: arid, temperate, tropical\n",
+      "Name: Dathomir, Population: 5200, Climate: temperate\n",
+      "Name: Ryloth, Population: 1500000000, Climate: temperate, arid, subartic\n",
+      "Name: Aleen Minor, Population: unknown, Climate: unknown\n",
+      "Name: Vulpter, Population: 421000000, Climate: temperate, artic\n",
+      "Name: Troiken, Population: unknown, Climate: unknown\n",
+      "Name: Tund, Population: 0, Climate: unknown\n",
+      "Name: Haruun Kal, Population: 705300, Climate: temperate\n",
+      "Name: Cerea, Population: 450000000, Climate: temperate\n",
+      "Name: Glee Anselm, Population: 500000000, Climate: tropical, temperate\n",
+      "Name: Iridonia, Population: unknown, Climate: unknown\n",
+      "Name: Tholoth, Population: unknown, Climate: unknown\n",
+      "Name: Iktotch, Population: unknown, Climate: arid, rocky, windy\n",
+      "Name: Quermia, Population: unknown, Climate: unknown\n",
+      "Name: Dorin, Population: unknown, Climate: temperate\n",
+      "Name: Champala, Population: 3500000000, Climate: temperate\n",
+      "Name: Mirial, Population: unknown, Climate: unknown\n",
+      "Name: Serenno, Population: unknown, Climate: unknown\n",
+      "Name: Concord Dawn, Population: unknown, Climate: unknown\n",
+      "Name: Zolan, Population: unknown, Climate: unknown\n",
+      "Name: Ojom, Population: 500000000, Climate: frigid\n",
+      "Name: Skako, Population: 500000000000, Climate: temperate\n",
+      "Name: Muunilinst, Population: 5000000000, Climate: temperate\n",
+      "Name: Shili, Population: unknown, Climate: temperate\n",
+      "Name: Kalee, Population: 4000000000, Climate: arid, temperate, tropical\n",
+      "Name: Umbara, Population: unknown, Climate: unknown\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_all_planets():\n",
+    "    planets = []\n",
+    "    #url = f\"{url}people/\"\n",
+    "    url = \"https://swapi.dev/api/planets/\"\n",
+    "    \n",
+    "    while url:\n",
+    "        response = requests.get(url)\n",
+    "        if response.status_code == 200:\n",
+    "            data = response.json()\n",
+    "            planets.extend(data['results'])\n",
+    "            url = data['next']\n",
+    "        else:\n",
+    "            print(f\"Error: {response.status_code}\")\n",
+    "            break\n",
+    "    \n",
+    "    return planets\n",
+    "\n",
+    "all_planets = get_all_planets()\n",
+    "for planet in all_planets:\n",
+    "    print(f\"Name: {planet['name']}, Population: {planet['population']}, Climate: {planet['climate']}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 5: Search and Display"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import unittest\n",
+    "import requests\n",
+    "\n",
+    "def search_character(name):\n",
+    "    url = f\"https://swapi.dev/api/people/?search={name}\"\n",
+    "    response = requests.get(url)\n",
+    "    \n",
+    "    if response.status_code == 200:\n",
+    "        data = response.json()\n",
+    "        if data['count'] > 0:\n",
+    "            character = data['results'][0]\n",
+    "            return f\"Name: {character['name']}, Gender: {character['gender']}, Height: {character['height']}\"\n",
+    "        else:\n",
+    "            return \"Character not found\"\n",
+    "    else:\n",
+    "        return f\"Error: {response.status_code}\"\n",
+    "\n",
+    "# Unit tests\n",
+    "class TestSearchCharacter(unittest.TestCase):\n",
+    "    def test_existing_character(self):\n",
+    "        result = search_character(\"Luke Skywalker\")\n",
+    "        print(f\"Result for Luke Skywalker: {result}\")\n",
+    "        self.assertIn(\"Luke Skywalker\", result)\n",
+    "        self.assertIn(\"Gender:\", result)\n",
+    "        self.assertIn(\"Height:\", result)\n",
+    "\n",
+    "    def test_non_existing_character(self):\n",
+    "        result = search_character(\"Blandine\")\n",
+    "        self.assertEqual(result, \"Character not found\")\n",
+    "\n",
+    "    def test_partial_name(self):\n",
+    "        result = search_character(\"Skywalker\")\n",
+    "        self.assertIn(\"Skywalker\", result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "test_existing_character (__main__.TestSearchCharacter.test_existing_character) ... ok\n",
+      "test_non_existing_character (__main__.TestSearchCharacter.test_non_existing_character) ... ok\n",
+      "test_partial_name (__main__.TestSearchCharacter.test_partial_name) ... "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Result for Luke Skywalker: Name: Luke Skywalker, Gender: male, Height: 172\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ok\n",
+      "\n",
+      "----------------------------------------------------------------------\n",
+      "Ran 3 tests in 0.656s\n",
+      "\n",
+      "OK\n"
+     ]
+    }
+   ],
+   "source": [
+    "def run_tests():\n",
+    "    suite = unittest.TestLoader().loadTestsFromTestCase(TestSearchCharacter)\n",
+    "    runner = unittest.TextTestRunner(verbosity=2)\n",
+    "    runner.run(suite)\n",
+    "\n",
+    "# Run the tests\n",
+    "run_tests()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 6: Advanced Query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "species\n",
+      "Droid       3\n",
+      "Hutt        1\n",
+      "Rodian      1\n",
+      "Unknown    12\n",
+      "Wookie      1\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "def get_characters_from_film(film_title):\n",
+    "    films = get_all_films()\n",
+    "    target_film = next((film for film in films if film['title'].lower() == film_title.lower()), None)\n",
+    "    \n",
+    "    if not target_film:\n",
+    "        return \"Film not found\"\n",
+    "    \n",
+    "    characters_data = []\n",
+    "    for character_url in target_film['characters']:\n",
+    "        response = requests.get(character_url)\n",
+    "        if response.status_code == 200:\n",
+    "            character = response.json()\n",
+    "            characters_data.append({\n",
+    "                'name': character['name'],\n",
+    "                'gender': character['gender'],\n",
+    "                'species': requests.get(character['species'][0]).json()['name'] if character['species'] else 'Unknown'\n",
+    "            })\n",
+    "    \n",
+    "    df = pd.DataFrame(characters_data)\n",
+    "    return df.groupby('species')\n",
+    "\n",
+    "# Example usage\n",
+    "characters_df = get_characters_from_film(\"A New Hope\")\n",
+    "print(characters_df.size())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This result correspond to:\n",
+    "- Droïdes (Droid) : 3 personnages\n",
+    "Probablement C-3PO, R2-D2, et peut-être un autre droïde moins connu.\n",
+    "- Hutt : 1 personnage\n",
+    "Très probablement Jabba the Hutt, bien qu'il n'apparaisse que brièvement dans la version spéciale du film.\n",
+    "- Rodian : 1 personnage\n",
+    "Probablement Greedo, le chasseur de primes qui confronte Han Solo.\n",
+    "- Wookiee : 1 personnage\n",
+    "Sans aucun doute Chewbacca, le copilote de Han Solo.\n",
+    "- Unknown : 12 personnages\n",
+    "C'est le groupe le plus important, suggérant que beaucoup de personnages n'ont pas d'espèce spécifiquement identifiée dans l'API.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 7: Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1. Attack of the Clones: 40 characters\n",
+      "2. The Phantom Menace: 34 characters\n",
+      "3. Revenge of the Sith: 34 characters\n",
+      "4. Return of the Jedi: 20 characters\n",
+      "5. A New Hope: 18 characters\n",
+      "6. The Empire Strikes Back: 16 characters\n"
+     ]
+    }
+   ],
+   "source": [
+    "def rank_films_by_characters():\n",
+    "    films = get_all_films()\n",
+    "    film_rankings = [(film['title'], len(film['characters'])) for film in films]\n",
+    "    return sorted(film_rankings, key=lambda x: x[1], reverse=True)\n",
+    "\n",
+    "rankings = rank_films_by_characters()\n",
+    "for rank, (title, char_count) in enumerate(rankings, 1):\n",
+    "    print(f\"{rank}. {title}: {char_count} characters\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 8 (Bonus): Additional Endpoint"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Starships"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Name: Millennium Falcon, Model: YT-1300 light freighter, Manufacturer: Corellian Engineering Corporation, Cost: 100000 credits\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_starship_info(name):\n",
+    "    url = f\"https://swapi.dev/api/starships/?search={name}\"\n",
+    "    response = requests.get(url)\n",
+    "    \n",
+    "    if response.status_code == 200:\n",
+    "        data = response.json()\n",
+    "        if data['count'] > 0:\n",
+    "            starship = data['results'][0]\n",
+    "            return f\"Name: {starship['name']}, Model: {starship['model']}, Manufacturer: {starship['manufacturer']}, Cost: {starship['cost_in_credits']} credits\"\n",
+    "        else:\n",
+    "            return \"Starship not found\"\n",
+    "    else:\n",
+    "        return f\"Error: {response.status_code}\"\n",
+    "\n",
+    "# Example usage\n",
+    "print(get_starship_info(\"Millennium Falcon\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Vehicle information"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Name: Snowspeeder\n",
+      "Model: t-47 airspeeder\n",
+      "Manufacturer: Incom corporation\n",
+      "Cost: unknown credits\n",
+      "Length: 4.5 meters\n",
+      "Max Speed: 650\n",
+      "Crew: 2\n",
+      "Passengers: 0\n",
+      "\n",
+      "\n",
+      "All vehicles:\n",
+      "Name: Sand Crawler\n",
+      "Model: Digger Crawler\n",
+      "Manufacturer: Corellia Mining Corporation\n",
+      "Cost: 150000 credits\n",
+      "Length: 36.8  meters\n",
+      "Max Speed: 30\n",
+      "Crew: 46\n",
+      "Passengers: 30\n",
+      "\n",
+      "Name: T-16 skyhopper\n",
+      "Model: T-16 skyhopper\n",
+      "Manufacturer: Incom Corporation\n",
+      "Cost: 14500 credits\n",
+      "Length: 10.4  meters\n",
+      "Max Speed: 1200\n",
+      "Crew: 1\n",
+      "Passengers: 1\n",
+      "\n",
+      "Name: X-34 landspeeder\n",
+      "Model: X-34 landspeeder\n",
+      "Manufacturer: SoroSuub Corporation\n",
+      "Cost: 10550 credits\n",
+      "Length: 3.4  meters\n",
+      "Max Speed: 250\n",
+      "Crew: 1\n",
+      "Passengers: 1\n",
+      "\n",
+      "Name: TIE/LN starfighter\n",
+      "Model: Twin Ion Engine/Ln Starfighter\n",
+      "Manufacturer: Sienar Fleet Systems\n",
+      "Cost: unknown credits\n",
+      "Length: 6.4 meters\n",
+      "Max Speed: 1200\n",
+      "Crew: 1\n",
+      "Passengers: 0\n",
+      "\n",
+      "Name: Snowspeeder\n",
+      "Model: t-47 airspeeder\n",
+      "Manufacturer: Incom corporation\n",
+      "Cost: unknown credits\n",
+      "Length: 4.5 meters\n",
+      "Max Speed: 650\n",
+      "Crew: 2\n",
+      "Passengers: 0\n",
+      "\n",
+      "Name: TIE bomber\n",
+      "Model: TIE/sa bomber\n",
+      "Manufacturer: Sienar Fleet Systems\n",
+      "Cost: unknown credits\n",
+      "Length: 7.8 meters\n",
+      "Max Speed: 850\n",
+      "Crew: 1\n",
+      "Passengers: 0\n",
+      "\n",
+      "Name: AT-AT\n",
+      "Model: All Terrain Armored Transport\n",
+      "Manufacturer: Kuat Drive Yards, Imperial Department of Military Research\n",
+      "Cost: unknown credits\n",
+      "Length: 20 meters\n",
+      "Max Speed: 60\n",
+      "Crew: 5\n",
+      "Passengers: 40\n",
+      "\n",
+      "Name: AT-ST\n",
+      "Model: All Terrain Scout Transport\n",
+      "Manufacturer: Kuat Drive Yards, Imperial Department of Military Research\n",
+      "Cost: unknown credits\n",
+      "Length: 2 meters\n",
+      "Max Speed: 90\n",
+      "Crew: 2\n",
+      "Passengers: 0\n",
+      "\n",
+      "Name: Storm IV Twin-Pod cloud car\n",
+      "Model: Storm IV Twin-Pod\n",
+      "Manufacturer: Bespin Motors\n",
+      "Cost: 75000 credits\n",
+      "Length: 7 meters\n",
+      "Max Speed: 1500\n",
+      "Crew: 2\n",
+      "Passengers: 0\n",
+      "\n",
+      "Name: Sail barge\n",
+      "Model: Modified Luxury Sail Barge\n",
+      "Manufacturer: Ubrikkian Industries Custom Vehicle Division\n",
+      "Cost: 285000 credits\n",
+      "Length: 30 meters\n",
+      "Max Speed: 100\n",
+      "Crew: 26\n",
+      "Passengers: 500\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "\n",
+    "def get_vehicle_info(name=None):\n",
+    "    base_url = \"https://swapi.dev/api/vehicles/\"\n",
+    "    \n",
+    "    if name:\n",
+    "        # Search for a specific vehicle\n",
+    "        params = {'search': name}\n",
+    "        response = requests.get(base_url, params=params)\n",
+    "    else:\n",
+    "        # Get all vehicles\n",
+    "        response = requests.get(base_url)\n",
+    "    \n",
+    "    if response.status_code == 200:\n",
+    "        data = response.json()\n",
+    "        vehicles = data['results']\n",
+    "        \n",
+    "        if not vehicles:\n",
+    "            return \"No vehicles found.\"\n",
+    "        \n",
+    "        vehicle_info = []\n",
+    "        for vehicle in vehicles:\n",
+    "            info = f\"Name: {vehicle['name']}\\n\"\n",
+    "            info += f\"Model: {vehicle['model']}\\n\"\n",
+    "            info += f\"Manufacturer: {vehicle['manufacturer']}\\n\"\n",
+    "            info += f\"Cost: {vehicle['cost_in_credits']} credits\\n\"\n",
+    "            info += f\"Length: {vehicle['length']} meters\\n\"\n",
+    "            info += f\"Max Speed: {vehicle['max_atmosphering_speed']}\\n\"\n",
+    "            info += f\"Crew: {vehicle['crew']}\\n\"\n",
+    "            info += f\"Passengers: {vehicle['passengers']}\\n\"\n",
+    "            vehicle_info.append(info)\n",
+    "        \n",
+    "        return \"\\n\".join(vehicle_info)\n",
+    "    else:\n",
+    "        return f\"Error: Unable to retrieve vehicle data. Status code: {response.status_code}\"\n",
+    "\n",
+    "# Example usage\n",
+    "print(get_vehicle_info(\"Snowspeeder\"))\n",
+    "print(\"\\nAll vehicles:\")\n",
+    "print(get_vehicle_info())"
    ]
   },
   {
@@ -319,38 +1099,294 @@
     "- https://swapi.dev/documentation"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 1: Use of the playground Graphi/QL"
+   ]
+  },
+  {
+   "attachments": {
+    "image-2.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image-2.png](attachment:image-2.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---------------------------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 2: Retrieve Films with Character Information"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Film: A New Hope\n",
+      "Release Date: 1977-05-25\n",
+      "Director: George Lucas\n",
+      "Producers: Gary Kurtz, Rick McCallum\n",
+      "Characters:\n",
+      "  - Luke Skywalker (Species: Unknown)\n",
+      "  - C-3PO (Species: Droid)\n",
+      "  - R2-D2 (Species: Droid)\n",
+      "  - Darth Vader (Species: Unknown)\n",
+      "  - Leia Organa (Species: Unknown)\n",
+      "  - Owen Lars (Species: Unknown)\n",
+      "  - Beru Whitesun lars (Species: Unknown)\n",
+      "  - R5-D4 (Species: Droid)\n",
+      "  - Biggs Darklighter (Species: Unknown)\n",
+      "  - Obi-Wan Kenobi (Species: Unknown)\n",
+      "  - Wilhuff Tarkin (Species: Unknown)\n",
+      "  - Chewbacca (Species: Wookie)\n",
+      "  - Han Solo (Species: Unknown)\n",
+      "  - Greedo (Species: Rodian)\n",
+      "  - Jabba Desilijic Tiure (Species: Hutt)\n",
+      "  - Wedge Antilles (Species: Unknown)\n",
+      "  - Jek Tono Porkins (Species: Unknown)\n",
+      "  - Raymus Antilles (Species: Unknown)\n",
+      "\n",
+      "Film: The Empire Strikes Back\n",
+      "Release Date: 1980-05-17\n",
+      "Director: Irvin Kershner\n",
+      "Producers: Gary Kurtz, Rick McCallum\n",
+      "Characters:\n",
+      "  - Luke Skywalker (Species: Unknown)\n",
+      "  - C-3PO (Species: Droid)\n",
+      "  - R2-D2 (Species: Droid)\n",
+      "  - Darth Vader (Species: Unknown)\n",
+      "  - Leia Organa (Species: Unknown)\n",
+      "  - Obi-Wan Kenobi (Species: Unknown)\n",
+      "  - Chewbacca (Species: Wookie)\n",
+      "  - Han Solo (Species: Unknown)\n",
+      "  - Wedge Antilles (Species: Unknown)\n",
+      "  - Yoda (Species: Yoda's species)\n",
+      "  - Palpatine (Species: Unknown)\n",
+      "  - Boba Fett (Species: Unknown)\n",
+      "  - IG-88 (Species: Droid)\n",
+      "  - Bossk (Species: Trandoshan)\n",
+      "  - Lando Calrissian (Species: Unknown)\n",
+      "  - Lobot (Species: Unknown)\n",
+      "\n",
+      "Film: Return of the Jedi\n",
+      "Release Date: 1983-05-25\n",
+      "Director: Richard Marquand\n",
+      "Producers: Howard G. Kazanjian, George Lucas, Rick McCallum\n",
+      "Characters:\n",
+      "  - Luke Skywalker (Species: Unknown)\n",
+      "  - C-3PO (Species: Droid)\n",
+      "  - R2-D2 (Species: Droid)\n",
+      "  - Darth Vader (Species: Unknown)\n",
+      "  - Leia Organa (Species: Unknown)\n",
+      "  - Obi-Wan Kenobi (Species: Unknown)\n",
+      "  - Chewbacca (Species: Wookie)\n",
+      "  - Han Solo (Species: Unknown)\n",
+      "  - Jabba Desilijic Tiure (Species: Hutt)\n",
+      "  - Wedge Antilles (Species: Unknown)\n",
+      "  - Yoda (Species: Yoda's species)\n",
+      "  - Palpatine (Species: Unknown)\n",
+      "  - Boba Fett (Species: Unknown)\n",
+      "  - Lando Calrissian (Species: Unknown)\n",
+      "  - Ackbar (Species: Mon Calamari)\n",
+      "  - Mon Mothma (Species: Unknown)\n",
+      "  - Arvel Crynyd (Species: Unknown)\n",
+      "  - Wicket Systri Warrick (Species: Ewok)\n",
+      "  - Nien Nunb (Species: Sullustan)\n",
+      "  - Bib Fortuna (Species: Twi'lek)\n",
+      "\n",
+      "Film: The Phantom Menace\n",
+      "Release Date: 1999-05-19\n",
+      "Director: George Lucas\n",
+      "Producers: Rick McCallum\n",
+      "Characters:\n",
+      "  - C-3PO (Species: Droid)\n",
+      "  - R2-D2 (Species: Droid)\n",
+      "  - Obi-Wan Kenobi (Species: Unknown)\n",
+      "  - Anakin Skywalker (Species: Unknown)\n",
+      "  - Jabba Desilijic Tiure (Species: Hutt)\n",
+      "  - Yoda (Species: Yoda's species)\n",
+      "  - Palpatine (Species: Unknown)\n",
+      "  - Qui-Gon Jinn (Species: Unknown)\n",
+      "  - Nute Gunray (Species: Neimodian)\n",
+      "  - Finis Valorum (Species: Unknown)\n",
+      "  - Padmé Amidala (Species: Unknown)\n",
+      "  - Jar Jar Binks (Species: Gungan)\n",
+      "  - Roos Tarpals (Species: Gungan)\n",
+      "  - Rugor Nass (Species: Gungan)\n",
+      "  - Ric Olié (Species: Unknown)\n",
+      "  - Watto (Species: Toydarian)\n",
+      "  - Sebulba (Species: Dug)\n",
+      "  - Quarsh Panaka (Species: Unknown)\n",
+      "  - Shmi Skywalker (Species: Unknown)\n",
+      "  - Darth Maul (Species: Zabrak)\n",
+      "  - Ayla Secura (Species: Twi'lek)\n",
+      "  - Ratts Tyerel (Species: Aleena)\n",
+      "  - Dud Bolt (Species: Vulptereen)\n",
+      "  - Gasgano (Species: Xexto)\n",
+      "  - Ben Quadinaros (Species: Toong)\n",
+      "  - Mace Windu (Species: Unknown)\n",
+      "  - Ki-Adi-Mundi (Species: Cerean)\n",
+      "  - Kit Fisto (Species: Nautolan)\n",
+      "  - Eeth Koth (Species: Zabrak)\n",
+      "  - Adi Gallia (Species: Tholothian)\n",
+      "  - Saesee Tiin (Species: Iktotchi)\n",
+      "  - Yarael Poof (Species: Quermian)\n",
+      "  - Plo Koon (Species: Kel Dor)\n",
+      "  - Mas Amedda (Species: Chagrian)\n",
+      "\n",
+      "Film: Attack of the Clones\n",
+      "Release Date: 2002-05-16\n",
+      "Director: George Lucas\n",
+      "Producers: Rick McCallum\n",
+      "Characters:\n",
+      "  - C-3PO (Species: Droid)\n",
+      "  - R2-D2 (Species: Droid)\n",
+      "  - Owen Lars (Species: Unknown)\n",
+      "  - Beru Whitesun lars (Species: Unknown)\n",
+      "  - Obi-Wan Kenobi (Species: Unknown)\n",
+      "  - Anakin Skywalker (Species: Unknown)\n",
+      "  - Yoda (Species: Yoda's species)\n",
+      "  - Palpatine (Species: Unknown)\n",
+      "  - Boba Fett (Species: Unknown)\n",
+      "  - Nute Gunray (Species: Neimodian)\n",
+      "  - Padmé Amidala (Species: Unknown)\n",
+      "  - Jar Jar Binks (Species: Gungan)\n",
+      "  - Watto (Species: Toydarian)\n",
+      "  - Shmi Skywalker (Species: Unknown)\n",
+      "  - Ayla Secura (Species: Twi'lek)\n",
+      "  - Mace Windu (Species: Unknown)\n",
+      "  - Ki-Adi-Mundi (Species: Cerean)\n",
+      "  - Kit Fisto (Species: Nautolan)\n",
+      "  - Plo Koon (Species: Kel Dor)\n",
+      "  - Mas Amedda (Species: Chagrian)\n",
+      "  - Gregar Typho (Species: Unknown)\n",
+      "  - Cordé (Species: Unknown)\n",
+      "  - Cliegg Lars (Species: Unknown)\n",
+      "  - Poggle the Lesser (Species: Geonosian)\n",
+      "  - Luminara Unduli (Species: Mirialan)\n",
+      "  - Barriss Offee (Species: Mirialan)\n",
+      "  - Dormé (Species: Human)\n",
+      "  - Dooku (Species: Human)\n",
+      "  - Bail Prestor Organa (Species: Human)\n",
+      "  - Jango Fett (Species: Unknown)\n",
+      "  - Zam Wesell (Species: Clawdite)\n",
+      "  - Dexter Jettster (Species: Besalisk)\n",
+      "  - Lama Su (Species: Kaminoan)\n",
+      "  - Taun We (Species: Kaminoan)\n",
+      "  - Jocasta Nu (Species: Human)\n",
+      "  - R4-P17 (Species: Unknown)\n",
+      "  - Wat Tambor (Species: Skakoan)\n",
+      "  - San Hill (Species: Muun)\n",
+      "  - Shaak Ti (Species: Togruta)\n",
+      "  - Sly Moore (Species: Unknown)\n",
+      "\n",
+      "Film: Revenge of the Sith\n",
+      "Release Date: 2005-05-19\n",
+      "Director: George Lucas\n",
+      "Producers: Rick McCallum\n",
+      "Characters:\n",
+      "  - Luke Skywalker (Species: Unknown)\n",
+      "  - C-3PO (Species: Droid)\n",
+      "  - R2-D2 (Species: Droid)\n",
+      "  - Darth Vader (Species: Unknown)\n",
+      "  - Leia Organa (Species: Unknown)\n",
+      "  - Owen Lars (Species: Unknown)\n",
+      "  - Beru Whitesun lars (Species: Unknown)\n",
+      "  - Obi-Wan Kenobi (Species: Unknown)\n",
+      "  - Anakin Skywalker (Species: Unknown)\n",
+      "  - Wilhuff Tarkin (Species: Unknown)\n",
+      "  - Chewbacca (Species: Wookie)\n",
+      "  - Yoda (Species: Yoda's species)\n",
+      "  - Palpatine (Species: Unknown)\n",
+      "  - Nute Gunray (Species: Neimodian)\n",
+      "  - Padmé Amidala (Species: Unknown)\n",
+      "  - Ayla Secura (Species: Twi'lek)\n",
+      "  - Mace Windu (Species: Unknown)\n",
+      "  - Ki-Adi-Mundi (Species: Cerean)\n",
+      "  - Kit Fisto (Species: Nautolan)\n",
+      "  - Eeth Koth (Species: Zabrak)\n",
+      "  - Adi Gallia (Species: Tholothian)\n",
+      "  - Saesee Tiin (Species: Iktotchi)\n",
+      "  - Plo Koon (Species: Kel Dor)\n",
+      "  - Poggle the Lesser (Species: Geonosian)\n",
+      "  - Luminara Unduli (Species: Mirialan)\n",
+      "  - Dooku (Species: Human)\n",
+      "  - Bail Prestor Organa (Species: Human)\n",
+      "  - R4-P17 (Species: Unknown)\n",
+      "  - Shaak Ti (Species: Togruta)\n",
+      "  - Grievous (Species: Kaleesh)\n",
+      "  - Tarfful (Species: Wookie)\n",
+      "  - Raymus Antilles (Species: Unknown)\n",
+      "  - Sly Moore (Species: Unknown)\n",
+      "  - Tion Medon (Species: Pau'an)\n"
+     ]
+    }
+   ],
    "source": [
     "import requests\n",
-    "\n",
+    " \n",
     "url = \"https://swapi-graphql.netlify.app/.netlify/functions/index\"\n",
-    "body = \"\"\"\n",
-    "query {\n",
+    "query = \"\"\"\n",
+    "{\n",
     "  allFilms {\n",
     "    edges {\n",
     "      node {\n",
+    "        id\n",
     "        title\n",
+    "        releaseDate\n",
+    "        director\n",
+    "        producers\n",
+    "        characterConnection {\n",
+    "          edges {\n",
+    "            node {\n",
+    "              name\n",
+    "              species {\n",
+    "                name\n",
+    "              }\n",
+    "            }\n",
+    "          }\n",
+    "        }\n",
     "      }\n",
     "    }\n",
     "  }\n",
     "}\n",
     "\"\"\"\n",
-    "\n",
-    "response = requests.get(url=url, json={\"query\": body})\n",
-    "print(\"response status code: \", response.status_code)\n",
+    " \n",
+    "response = requests.post(url, json={'query': query})\n",
     "if response.status_code == 200:\n",
-    "  print(\"response : \", response.json())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---------------------------"
+    "    data = response.json()\n",
+    "    for film in data['data']['allFilms']['edges']:\n",
+    "        print(f\"\\nFilm: {film['node']['title']}\")\n",
+    "        print(f\"Release Date: {film['node']['releaseDate']}\")\n",
+    "        print(f\"Director: {film['node']['director']}\")\n",
+    "        print(f\"Producers: {', '.join(film['node']['producers'])}\")\n",
+    "        print(\"Characters:\")\n",
+    "        for character in film['node']['characterConnection']['edges']:\n",
+    "            char_name = character['node']['name']\n",
+    "            species = character['node']['species']['name'] if character['node']['species'] else \"Unknown\"\n",
+    "            print(f\"  - {char_name} (Species: {species})\")\n",
+    "else:\n",
+    "    print(\"Error:\", response.text)"
    ]
   }
  ],
@@ -370,7 +1406,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.2"
+   "version": "3.12.1"
   },
   "orig_nbformat": 4,
   "vscode": {
diff --git a/TP2 and  3/.DS_Store b/TP2 and  3/.DS_Store
new file mode 100644
index 00000000..d57bf994
Binary files /dev/null and b/TP2 and  3/.DS_Store differ
diff --git a/TP2 and  3/.gitignore b/TP2 and  3/.gitignore
new file mode 100644
index 00000000..024f0840
--- /dev/null
+++ b/TP2 and  3/.gitignore	
@@ -0,0 +1,10 @@
+venv/
+*.pyc
+__pycache__/
+.env
+src/data/
+src/models/
+credentials.json
+serviceAccountKey.json
+.json
+
diff --git a/TP2 and  3/README.md b/TP2 and  3/README.md
index f8db5758..0d7bf6b2 100644
--- a/TP2 and  3/README.md	
+++ b/TP2 and  3/README.md	
@@ -1,84 +1,40 @@
 # EPF-API-TP
 
-- **Question 1:** _Which Python library/framework is often used to create fast, simple REST APIs?_
+Here are the answers to your quiz questions based on the context provided, along with explanations for each choice:
 
-  - Django
+### Answers to Quiz Questions
 
-  - Flask
+1. **Which Python library/framework is often used to create fast, simple REST APIs?**
+   - **Answer:** FastAPI
+   - **Explanation:** FastAPI is specifically designed for building APIs quickly and efficiently, leveraging Python type hints for automatic data validation and documentation. While Django and Flask can also be used to create APIs, FastAPI is known for its speed and performance.
 
-  - FastAPI
+2. **What's the main difference between Django, Flask, and FastAPI in terms of performance and speed?**
+   - **Answer:** FastAPI is renowned for its increased speed and performance compared with Django and Flask.
+   - **Explanation:** FastAPI is built on asynchronous capabilities which allow it to handle many requests concurrently, making it faster than both Django (which is synchronous) and Flask (which can be synchronous or asynchronous but is generally slower).
 
-  - All of the above
+3. **What is an endpoint in the context of REST APIs?**
+   - **Answer:** A specific URL to which a request can be sent to interact with the API.
+   - **Explanation:** An endpoint is a URL that represents a resource or action in an API. Clients send requests to these URLs to perform operations like retrieving or modifying data.
 
-- **Question 2:** _What's the main difference between Django, Flask and FastAPI in terms of performance and speed?_
+4. **What are the main HTTP verbs used to define REST API methods?**
+   - **Answer:** GET, POST, PUT, PATCH, DELETE
+   - **Explanation:** These verbs correspond to the CRUD operations (Create, Read, Update, Delete) commonly used in RESTful APIs.
 
-  - Django is generally faster than Flask and FastAPI.
+5. **In the context of REST APIs, what does the term "middleware" mean?**
+   - **Answer:** Intermediate software that processes the request before it reaches the main application.
+   - **Explanation:** Middleware functions as a bridge between different applications or services, processing requests and responses as they pass through.
 
-  - Flask outperforms Django and FastAPI.
+6. **Which Python library is often used to serialize and deserialize JSON data in the context of REST APIs?**
+   - **Answer:** json.dumps() and json.loads()
+   - **Explanation:** The built-in `json` module in Python provides `dumps()` for converting Python objects to JSON strings and `loads()` for converting JSON strings back into Python objects.
 
-  - FastAPI is renowned for its increased speed and performance compared with Django and Flask.
+7. **What is the main use of the HTTP "PUT" method in the context of REST APIs?**
+   - **Answer:** Update an existing resource, or create one if it doesn't exist.
+   - **Explanation:** The PUT method is typically used to update resources at a specific URL. If no resource exists at that URL, it can also create one.
 
-  - Django, Flask and FastAPI have equivalent performance.
-
-- **Question 3:** What is an endpoint in the context of REST APIs?\*
-
-  - A unique IP address associated with an API.
-
-  - A breakpoint in the code where the API can be interrupted.
-
-  - A specific URL to which a request can be sent to interact with the API.
-
-  - A unique identifier assigned to each incoming request.
-
-- **Question 4:** _What are the main HTTP verbs used to define REST API methods?_
-
-  - GET, POST, PUT, PATCH, DELETE
-
-  - SEND, RECEIVE, UPDATE, REMOVE
-
-  - READ, WRITE, MODIFY, DELETE
-
-  - FETCH, INSERT, UPDATE, DELETE
-
-- **Question 5:** _In the context of REST APIs, what does the term "middleware" mean?_
-
-  - A component that processes data sent by the user.
-
-  - An external library used to speed up API development.
-
-  - Intermediate software that processes the request before it reaches the main application.
-
-  - A method for securing data stored in the database.
-
-- **Question 6:** _Which Python library is often used to serialize and deserialize JSON data in the context of REST APIs?_
-
-  - JSONify
-
-  - PyJSON
-
-  - json.dumps() and json.loads()
-
-  - serializeJSON
-
-- **Question 7:** _What is the main use of the HTTP "PUT" method in the context of REST APIs?_
-
-  - Create a new resource.
-
-  - Update an existing resource, or create one if it doesn't exist.
-
-  - Delete a resource.
-
-  - Read a specific resource.
-
-- **Question 8:** In FastAPI, how do you define an endpoint to handle a POST request with JSON data?\*
-
-  - @app.post("/endpoint")
-
-  - @app.get("/endpoint")
-
-  - @app.request("/endpoint")
-
-  - @app.update("/endpoint")
+8. **In FastAPI, how do you define an endpoint to handle a POST request with JSON data?**
+   - **Answer:** @app.post("/endpoint")
+   - **Explanation:** This decorator specifies that the function below it will handle POST requests sent to the specified endpoint.
 
 # Creating an API with FastAPI
 
diff --git a/TP2 and  3/config/.DS_Store b/TP2 and  3/config/.DS_Store
new file mode 100644
index 00000000..9ab06a58
Binary files /dev/null and b/TP2 and  3/config/.DS_Store differ
diff --git a/TP2 and  3/config/dev/config.json b/TP2 and  3/config/dev/config.json
new file mode 100644
index 00000000..c407e169
--- /dev/null
+++ b/TP2 and  3/config/dev/config.json	
@@ -0,0 +1,7 @@
+{
+    "ENV_NAME": "development",
+    "DEBUG": true,
+    "DATABASE_URI": "sqlite:///dev.db",
+    "API_URL": "http://localhost:8080"
+}
+
diff --git a/TP2 and  3/config/prd/config.json b/TP2 and  3/config/prd/config.json
new file mode 100644
index 00000000..9e4235d7
--- /dev/null
+++ b/TP2 and  3/config/prd/config.json	
@@ -0,0 +1,7 @@
+{
+    "ENV_NAME": "production",
+    "DEBUG": false,
+    "DATABASE_URI": "postgresql://user:password@prod-db:5432/app",
+    "API_URL": "https://api.myapp.com"
+}
+
diff --git a/TP2 and  3/services/.DS_Store b/TP2 and  3/services/.DS_Store
new file mode 100644
index 00000000..3f7019b8
Binary files /dev/null and b/TP2 and  3/services/.DS_Store differ
diff --git a/TP2 and  3/services/epf-flower-data-science/.gitignore b/TP2 and  3/services/epf-flower-data-science/.gitignore
new file mode 100644
index 00000000..f006cdff
--- /dev/null
+++ b/TP2 and  3/services/epf-flower-data-science/.gitignore	
@@ -0,0 +1,10 @@
+venv/
+*.pyc
+__pycache__/
+.env
+src/data/
+src/models/
+credentials.json
+serviceAccountKey.json
+
+
diff --git a/TP2 and  3/services/epf-flower-data-science/main.py b/TP2 and  3/services/epf-flower-data-science/main.py
index bcdba253..b56c0397 100644
--- a/TP2 and  3/services/epf-flower-data-science/main.py	
+++ b/TP2 and  3/services/epf-flower-data-science/main.py	
@@ -1,8 +1,14 @@
+import os
 import uvicorn
-
 from src.app import get_application
 
+# Set the path to kaggle.json explicitly
+kaggle_json_path = os.path.join(os.path.dirname(__file__), "kaggle.json")
+
+# Set the environment variable for the Kaggle API to the directory of kaggle.json
+os.environ['KAGGLE_CONFIG_DIR'] = os.path.dirname(kaggle_json_path)
+
 app = get_application()
 
 if __name__ == "__main__":
-    uvicorn.run("main:app", debug=True, reload=True, port=8080)
+    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)
\ No newline at end of file
diff --git a/TP2 and  3/services/epf-flower-data-science/requirements.txt b/TP2 and  3/services/epf-flower-data-science/requirements.txt
index d9ff2fe3..eb7675e3 100644
--- a/TP2 and  3/services/epf-flower-data-science/requirements.txt	
+++ b/TP2 and  3/services/epf-flower-data-science/requirements.txt	
@@ -5,3 +5,5 @@ fastapi-utils==0.2.1
 pydantic==1.10
 opendatasets
 pytest
+firebase-admin
+
diff --git a/TP2 and  3/services/epf-flower-data-science/src/__init__.py b/TP2 and  3/services/epf-flower-data-science/src/__init__.py
new file mode 100644
index 00000000..0e5cf459
--- /dev/null
+++ b/TP2 and  3/services/epf-flower-data-science/src/__init__.py	
@@ -0,0 +1,3 @@
+from src.services.parameters import get_parameters, update_parameters
+
+__all__ = ['get_parameters', 'update_parameters']
diff --git a/TP2 and  3/config/dev b/TP2 and  3/services/epf-flower-data-science/src/api/__init__.py
similarity index 100%
rename from TP2 and  3/config/dev
rename to TP2 and  3/services/epf-flower-data-science/src/api/__init__.py
diff --git a/TP2 and  3/services/epf-flower-data-science/src/api/router.py b/TP2 and  3/services/epf-flower-data-science/src/api/router.py
index 15529962..1eefc102 100644
--- a/TP2 and  3/services/epf-flower-data-science/src/api/router.py	
+++ b/TP2 and  3/services/epf-flower-data-science/src/api/router.py	
@@ -1,8 +1,17 @@
-"""API Router for Fast API."""
 from fastapi import APIRouter
-
+from .routes import parameters
+from fastapi.responses import RedirectResponse
 from src.api.routes import hello
+from src.api.routes import data
+from .routes import authentication
 
 router = APIRouter()
 
+@router.get("/", include_in_schema=False)
+async def root():
+    return RedirectResponse(url="/docs")
+
 router.include_router(hello.router, tags=["Hello"])
+router.include_router(data.router, prefix="/data", tags=["Dataset"])
+router.include_router(parameters.router, tags=["parameters"])
+router.include_router(authentication.router, tags=["authentication"])
diff --git a/TP2 and  3/services/epf-flower-data-science/src/api/routes/authentication.py b/TP2 and  3/services/epf-flower-data-science/src/api/routes/authentication.py
index e69de29b..df196e25 100644
--- a/TP2 and  3/services/epf-flower-data-science/src/api/routes/authentication.py	
+++ b/TP2 and  3/services/epf-flower-data-science/src/api/routes/authentication.py	
@@ -0,0 +1,73 @@
+
+from fastapi import APIRouter, HTTPException, Depends
+from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
+from firebase_admin import auth, credentials, initialize_app
+from typing import List
+from pathlib import Path
+import firebase_admin
+
+router = APIRouter()
+
+# Initialize Firebase Admin SDK if not already initialized
+if not firebase_admin._apps:
+    cred_path = Path(__file__).parent.parent.parent / "config" / "serviceAccountKey.json"
+    cred = credentials.Certificate(str(cred_path))
+    firebase_admin.initialize_app(cred, {
+    'projectId': 'myproject-0412025',
+    'authDomain': 'myproject-0412025-94c7f.firebaseapp.com',
+})
+
+
+#oauth2_scheme = OAuth2PasswordBearer(tokenUrl="https://identitytoolkit.googleapis.com/v1/accounts:signInWithPassword")
+oauth2_scheme = OAuth2PasswordBearer(
+    tokenUrl="https://myproject-0412025-94c7f.firebaseapp.com/__/auth/action"
+)
+
+
+@router.post("/register")
+async def register_user(email: str, password: str):
+    try:
+        user = auth.create_user(
+            email=email,
+            password=password,
+            email_verified=False,
+            disabled=False
+        )
+        # Send email verification
+        verification_link = auth.generate_email_verification_link(email)
+        # You'll need to implement email sending here
+        return {
+            "message": f"User {email} created successfully. Please check your email for verification.",
+            "uid": user.uid
+        }
+    except auth.EmailAlreadyExistsError:
+        raise HTTPException(status_code=400, detail="Email already exists")
+    except Exception as e:
+        raise HTTPException(status_code=400, detail="Failed to register user. Please contact support.")
+
+
+@router.post("/login")
+async def login(form_data: OAuth2PasswordRequestForm = Depends()):
+    try:
+        user = auth.get_user_by_email(form_data.username)
+        custom_token = auth.create_custom_token(user.uid)
+        return {
+            "access_token": custom_token,
+            "token_type": "bearer",
+            "uid": user.uid
+        }
+    except auth.UserNotFoundError:
+        raise HTTPException(status_code=401, detail="User not found")
+    except Exception as e:
+        raise HTTPException(status_code=401, detail="Invalid credentials")
+
+@router.post("/logout")
+async def logout(token: str = Depends(oauth2_scheme)):
+    try:
+        decoded_token = auth.verify_id_token(token)
+        auth.revoke_refresh_tokens(decoded_token['uid'])
+        return {"message": "Successfully logged out"}
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
diff --git a/TP2 and  3/services/epf-flower-data-science/src/api/routes/data.py b/TP2 and  3/services/epf-flower-data-science/src/api/routes/data.py
index e69de29b..4a034178 100644
--- a/TP2 and  3/services/epf-flower-data-science/src/api/routes/data.py	
+++ b/TP2 and  3/services/epf-flower-data-science/src/api/routes/data.py	
@@ -0,0 +1,133 @@
+import os
+from fastapi import APIRouter
+import pandas as pd
+from kaggle.api.kaggle_api_extended import KaggleApi
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+import joblib
+from pydantic import BaseModel
+from fastapi import APIRouter, HTTPException
+from src.schemas.message import Parameter
+from src.services.parameters import get_parameters, update_parameters
+
+
+# Constants
+KAGGLE_CONFIG_DIR = os.path.expanduser("~/.kaggle")
+DATASET_PATH = "src/data/Iris.csv"
+PROCESSED_DATASET_PATH = "src/data/processed_iris.csv"
+MODEL_FILE_PATH = "src/data/random_forest_model.pkl"
+ENCODER_FILE_PATH = "src/data/label_encoder.pkl"
+MODEL_FILE_PATH = "src/data/random_forest_model.pkl"
+ENCODER_FILE_PATH = "src/data/label_encoder.pkl"
+
+class IrisFeatures(BaseModel):
+    SepalLengthCm: float
+    SepalWidthCm: float
+    PetalLengthCm: float
+    PetalWidthCm: float
+
+
+router = APIRouter()
+
+@router.get("/download-dataset")
+async def download_dataset():
+    """Downloads the Iris dataset from Kaggle."""
+    try:
+        api = KaggleApi()
+        api.authenticate()
+        api.dataset_download_files('uciml/iris', path='src/data/', unzip=True)
+        return {"message": "Dataset downloaded successfully."}
+    except Exception as e:
+        return {"error": str(e)}
+
+@router.get("/load-dataset")
+async def load_dataset():
+    """Loads the Iris dataset and returns it as JSON."""
+    try:
+        df = pd.read_csv(DATASET_PATH)
+        return {"data": df.to_dict(orient="records")}
+    except Exception as e:
+        return {"error": str(e)}
+
+@router.get("/process-dataset")
+async def process_dataset():
+    """Processes the Iris dataset."""
+    try:
+        df = pd.read_csv(DATASET_PATH)
+        if 'Species' not in df.columns:
+            return {"error": "La colonne 'Species' n'existe pas dans le dataset."}
+        if df.isnull().sum().any():
+            return {"error": "Le dataset contient des valeurs manquantes."}
+
+        label_encoder = LabelEncoder()
+        df['Species'] = label_encoder.fit_transform(df['Species'])
+
+        scaler = StandardScaler()
+        df_features = df.drop(columns=['Id', 'Species'])
+        X_scaled = scaler.fit_transform(df_features)
+        processed_df = pd.DataFrame(X_scaled, columns=df_features.columns)
+
+        processed_df.to_csv(PROCESSED_DATASET_PATH, index=False)
+        return {
+            "message": "Dataset processed successfully.",
+            "example_data": processed_df.head(5).to_dict(orient="records")
+        }
+    except Exception as e:
+        return {"error": str(e)}
+
+@router.get("/split-dataset")
+async def split_dataset():
+    """Splits the Iris dataset into training and testing sets."""
+    try:
+        df = pd.read_csv(PROCESSED_DATASET_PATH)
+        train, test = train_test_split(df, test_size=0.2, random_state=42)
+        return {
+            "train": train.to_dict(orient="records"),
+            "test": test.to_dict(orient="records")
+        }
+    except Exception as e:
+        return {"error": str(e)}
+    
+@router.post("/train-model")
+async def train_model():
+    """Trains the classification model using the Iris dataset."""
+    try:
+        df = pd.read_csv(DATASET_PATH)
+        if df.empty:
+            return {"error": "Le dataset est vide."}
+
+        label_encoder = LabelEncoder()
+        y_encoded = label_encoder.fit_transform(df['Species'])
+
+        processed_df = pd.read_csv(PROCESSED_DATASET_PATH)
+        if processed_df.empty:
+            return {"error": "Le dataset prétraité est vide."}
+
+        model = RandomForestClassifier(n_estimators=100, random_state=42)
+        model.fit(processed_df, y_encoded)
+
+        joblib.dump(model, MODEL_FILE_PATH)
+        joblib.dump(label_encoder, ENCODER_FILE_PATH)
+
+        return {"message": "Model trained and saved successfully."}
+    except Exception as e:
+        return {"error": str(e)}
+
+@router.post("/predict")
+async def predict(features: IrisFeatures):
+    """Makes predictions using the trained model."""
+    try:
+        if not os.path.exists(MODEL_FILE_PATH) or not os.path.exists(ENCODER_FILE_PATH):
+            raise HTTPException(status_code=404, detail="Model or encoder file not found. Please train the model first.")
+
+        model = joblib.load(MODEL_FILE_PATH)
+        label_encoder = joblib.load(ENCODER_FILE_PATH)
+
+        input_data = pd.DataFrame([features.dict()])
+        prediction_encoded = model.predict(input_data)
+        predicted_species = label_encoder.inverse_transform(prediction_encoded)
+
+        return {"predicted_species": predicted_species[0]}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}")
\ No newline at end of file
diff --git a/TP2 and  3/services/epf-flower-data-science/src/api/routes/hello.py b/TP2 and  3/services/epf-flower-data-science/src/api/routes/hello.py
index 13d59d72..103775ec 100644
--- a/TP2 and  3/services/epf-flower-data-science/src/api/routes/hello.py	
+++ b/TP2 and  3/services/epf-flower-data-science/src/api/routes/hello.py	
@@ -3,7 +3,6 @@
 
 router = APIRouter()
 
-
 @router.get("/hello/{name}", name="Demo route", response_model=MessageResponse)
 def hello(name: str) -> MessageResponse:
     return MessageResponse(message=f"Hello {name}, from fastapi test route !")
diff --git a/TP2 and  3/services/epf-flower-data-science/src/api/routes/parameters.py b/TP2 and  3/services/epf-flower-data-science/src/api/routes/parameters.py
index e69de29b..03b962fc 100644
--- a/TP2 and  3/services/epf-flower-data-science/src/api/routes/parameters.py	
+++ b/TP2 and  3/services/epf-flower-data-science/src/api/routes/parameters.py	
@@ -0,0 +1,62 @@
+from fastapi import APIRouter, HTTPException
+from firebase_admin import firestore
+from pydantic import BaseModel
+
+router = APIRouter()
+db = firestore.client()
+
+class Parameters(BaseModel):
+    n_estimators: int = 100
+    criterion: str = "gini"
+
+@router.get("/parameters")
+async def get_parameters():
+    """Récupère les paramètres depuis Firestore"""
+    try:
+        doc_ref = db.collection("parameters").document("parameters")
+        doc = doc_ref.get()
+        
+        if doc.exists:
+            return doc.to_dict()
+        else:
+            raise HTTPException(
+                status_code=404,
+                detail="Parameters not found in Firestore"
+            )
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Error retrieving parameters: {str(e)}"
+        )
+
+@router.put("/parameters")
+async def update_parameters(params: Parameters):
+    """Met à jour les paramètres existants dans Firestore"""
+    try:
+        doc_ref = db.collection("parameters").document("parameters")
+        doc_ref.update({
+            "n_estimators": params.n_estimators,
+            "criterion": params.criterion
+        })
+        return {"message": "Parameters updated successfully"}
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to update parameters: {str(e)}"
+        )
+
+@router.post("/parameters")
+async def add_parameters(params: Parameters):
+    """Ajoute de nouveaux paramètres dans Firestore"""
+    try:
+        doc_ref = db.collection("parameters").document("parameters")
+        doc_ref.set({
+            "n_estimators": params.n_estimators,
+            "criterion": params.criterion
+        })
+        return {"message": "Parameters added successfully"}
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to add parameters: {str(e)}"
+        )
diff --git a/TP2 and  3/services/epf-flower-data-science/src/app.py b/TP2 and  3/services/epf-flower-data-science/src/app.py
index d90875c3..88077f52 100644
--- a/TP2 and  3/services/epf-flower-data-science/src/app.py	
+++ b/TP2 and  3/services/epf-flower-data-science/src/app.py	
@@ -1,24 +1,99 @@
-from fastapi import FastAPI
+from fastapi import FastAPI, Request, HTTPException
+from fastapi.exceptions import RequestValidationError
 from starlette.middleware.cors import CORSMiddleware
-
 from src.api.router import router
+from firebase_admin import auth
+from fastapi.responses import JSONResponse
+import redis
+import time
+
+
+# Rate limiter class implementation
+class RateLimiter:
+    def __init__(self):
+        self.redis_client = redis.Redis(host='localhost', port=6379, db=0)
+        self.rate_limit = 100  # requests
+        self.time_window = 60  # seconds
 
+    async def check_rate_limit(self, user_id: str):
+        current = int(time.time())
+        key = f"rate_limit:{user_id}:{current // self.time_window}"
+        
+        count = self.redis_client.incr(key)
+        if count == 1:
+            self.redis_client.expire(key, self.time_window)
+        
+        if count > self.rate_limit:
+            raise HTTPException(
+                status_code=429,
+                detail="Too many requests. Please try again later."
+            )
 
 def get_application() -> FastAPI:
-    application = FastAPI(
-        title="epf-flower-data-science",
-        description="""Fast API""",
-        version="1.0.0",
-        redoc_url=None,
+    app = FastAPI(
+        title="EPF Flower Data Science",
+        description="EPF Flower Data Science for DAIPA Blandine",
+        version="1.0",
     )
 
-    application.add_middleware(
+    # Configure CORS with authorized domains from Firebase
+    app.add_middleware(
         CORSMiddleware,
-        allow_origins=["*"],
+        allow_origins=[
+            "localhost",
+            "myproject-0412025-94c7f.firebaseapp.com",
+            "myproject-0412025-94c7f.web.app",
+            "0.0.0.0"
+        ],
         allow_credentials=True,
         allow_methods=["*"],
         allow_headers=["*"],
     )
 
-    application.include_router(router)
-    return application
+    # Add rate limiting middleware
+    rate_limiter = RateLimiter()
+
+    @app.middleware("http")
+    async def rate_limit_middleware(request: Request, call_next):
+        if token := request.headers.get("Authorization"):
+            try:
+                user = auth.verify_id_token(token.split(" ")[1])
+                await rate_limiter.check_rate_limit(user["uid"])
+            except Exception as e:
+                return JSONResponse(
+                    status_code=401,
+                    content={"detail": "Invalid authentication credentials"}
+                )
+        response = await call_next(request)
+        return response
+
+    # Add custom error handlers
+    @app.exception_handler(404)
+    async def custom_404_handler(request: Request, exc):
+        return JSONResponse(
+            status_code=404,
+            content={
+                "message": "Resource not found. Please check the URL and try again.",
+                "path": str(request.url),
+                "app_name": "EPF Flower Data Science for DAIPA Blandine"
+            }
+        )
+
+    # Add validation error handler
+    @app.exception_handler(RequestValidationError)
+    async def validation_exception_handler(request: Request, exc):
+        return JSONResponse(
+            status_code=422,
+            content={
+                "message": "Validation error in request",
+                "details": str(exc)
+            }
+        )
+
+    # Include routers with version prefix
+    app.include_router(router, prefix="/v1")
+
+    return app
+
+# Create application instance
+app = get_application()
diff --git a/TP2 and  3/services/epf-flower-data-science/src/config/config_loader.py b/TP2 and  3/services/epf-flower-data-science/src/config/config_loader.py
new file mode 100644
index 00000000..84610b12
--- /dev/null
+++ b/TP2 and  3/services/epf-flower-data-science/src/config/config_loader.py	
@@ -0,0 +1,12 @@
+import os
+import json
+
+def load_config(env: str):
+    config_path = f"./config/{env}/config.json"
+    with open(config_path, "r") as f:
+        return json.load(f)
+
+current_env = os.getenv("API", "dev")
+config = load_config(current_env)
+print(f"Environnement actuel : {config['API']}")
+
diff --git a/TP2 and  3/services/epf-flower-data-science/src/config/model_parameters.json b/TP2 and  3/services/epf-flower-data-science/src/config/model_parameters.json
index e69de29b..39d7373a 100644
--- a/TP2 and  3/services/epf-flower-data-science/src/config/model_parameters.json	
+++ b/TP2 and  3/services/epf-flower-data-science/src/config/model_parameters.json	
@@ -0,0 +1,8 @@
+{
+    "model_type": "RandomForestClassifier",
+    "parameters": {
+      "n_estimators": 260,
+      "max_depth": 5,
+      "random_state": 42
+    }
+  }
\ No newline at end of file
diff --git a/TP2 and  3/services/epf-flower-data-science/src/data/Iris.csv b/TP2 and  3/services/epf-flower-data-science/src/data/Iris.csv
new file mode 100644
index 00000000..1bf42f25
--- /dev/null
+++ b/TP2 and  3/services/epf-flower-data-science/src/data/Iris.csv	
@@ -0,0 +1,151 @@
+Id,SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species
+1,5.1,3.5,1.4,0.2,Iris-setosa
+2,4.9,3.0,1.4,0.2,Iris-setosa
+3,4.7,3.2,1.3,0.2,Iris-setosa
+4,4.6,3.1,1.5,0.2,Iris-setosa
+5,5.0,3.6,1.4,0.2,Iris-setosa
+6,5.4,3.9,1.7,0.4,Iris-setosa
+7,4.6,3.4,1.4,0.3,Iris-setosa
+8,5.0,3.4,1.5,0.2,Iris-setosa
+9,4.4,2.9,1.4,0.2,Iris-setosa
+10,4.9,3.1,1.5,0.1,Iris-setosa
+11,5.4,3.7,1.5,0.2,Iris-setosa
+12,4.8,3.4,1.6,0.2,Iris-setosa
+13,4.8,3.0,1.4,0.1,Iris-setosa
+14,4.3,3.0,1.1,0.1,Iris-setosa
+15,5.8,4.0,1.2,0.2,Iris-setosa
+16,5.7,4.4,1.5,0.4,Iris-setosa
+17,5.4,3.9,1.3,0.4,Iris-setosa
+18,5.1,3.5,1.4,0.3,Iris-setosa
+19,5.7,3.8,1.7,0.3,Iris-setosa
+20,5.1,3.8,1.5,0.3,Iris-setosa
+21,5.4,3.4,1.7,0.2,Iris-setosa
+22,5.1,3.7,1.5,0.4,Iris-setosa
+23,4.6,3.6,1.0,0.2,Iris-setosa
+24,5.1,3.3,1.7,0.5,Iris-setosa
+25,4.8,3.4,1.9,0.2,Iris-setosa
+26,5.0,3.0,1.6,0.2,Iris-setosa
+27,5.0,3.4,1.6,0.4,Iris-setosa
+28,5.2,3.5,1.5,0.2,Iris-setosa
+29,5.2,3.4,1.4,0.2,Iris-setosa
+30,4.7,3.2,1.6,0.2,Iris-setosa
+31,4.8,3.1,1.6,0.2,Iris-setosa
+32,5.4,3.4,1.5,0.4,Iris-setosa
+33,5.2,4.1,1.5,0.1,Iris-setosa
+34,5.5,4.2,1.4,0.2,Iris-setosa
+35,4.9,3.1,1.5,0.1,Iris-setosa
+36,5.0,3.2,1.2,0.2,Iris-setosa
+37,5.5,3.5,1.3,0.2,Iris-setosa
+38,4.9,3.1,1.5,0.1,Iris-setosa
+39,4.4,3.0,1.3,0.2,Iris-setosa
+40,5.1,3.4,1.5,0.2,Iris-setosa
+41,5.0,3.5,1.3,0.3,Iris-setosa
+42,4.5,2.3,1.3,0.3,Iris-setosa
+43,4.4,3.2,1.3,0.2,Iris-setosa
+44,5.0,3.5,1.6,0.6,Iris-setosa
+45,5.1,3.8,1.9,0.4,Iris-setosa
+46,4.8,3.0,1.4,0.3,Iris-setosa
+47,5.1,3.8,1.6,0.2,Iris-setosa
+48,4.6,3.2,1.4,0.2,Iris-setosa
+49,5.3,3.7,1.5,0.2,Iris-setosa
+50,5.0,3.3,1.4,0.2,Iris-setosa
+51,7.0,3.2,4.7,1.4,Iris-versicolor
+52,6.4,3.2,4.5,1.5,Iris-versicolor
+53,6.9,3.1,4.9,1.5,Iris-versicolor
+54,5.5,2.3,4.0,1.3,Iris-versicolor
+55,6.5,2.8,4.6,1.5,Iris-versicolor
+56,5.7,2.8,4.5,1.3,Iris-versicolor
+57,6.3,3.3,4.7,1.6,Iris-versicolor
+58,4.9,2.4,3.3,1.0,Iris-versicolor
+59,6.6,2.9,4.6,1.3,Iris-versicolor
+60,5.2,2.7,3.9,1.4,Iris-versicolor
+61,5.0,2.0,3.5,1.0,Iris-versicolor
+62,5.9,3.0,4.2,1.5,Iris-versicolor
+63,6.0,2.2,4.0,1.0,Iris-versicolor
+64,6.1,2.9,4.7,1.4,Iris-versicolor
+65,5.6,2.9,3.6,1.3,Iris-versicolor
+66,6.7,3.1,4.4,1.4,Iris-versicolor
+67,5.6,3.0,4.5,1.5,Iris-versicolor
+68,5.8,2.7,4.1,1.0,Iris-versicolor
+69,6.2,2.2,4.5,1.5,Iris-versicolor
+70,5.6,2.5,3.9,1.1,Iris-versicolor
+71,5.9,3.2,4.8,1.8,Iris-versicolor
+72,6.1,2.8,4.0,1.3,Iris-versicolor
+73,6.3,2.5,4.9,1.5,Iris-versicolor
+74,6.1,2.8,4.7,1.2,Iris-versicolor
+75,6.4,2.9,4.3,1.3,Iris-versicolor
+76,6.6,3.0,4.4,1.4,Iris-versicolor
+77,6.8,2.8,4.8,1.4,Iris-versicolor
+78,6.7,3.0,5.0,1.7,Iris-versicolor
+79,6.0,2.9,4.5,1.5,Iris-versicolor
+80,5.7,2.6,3.5,1.0,Iris-versicolor
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diff --git a/TP2 and  3/services/epf-flower-data-science/src/data/database.sqlite b/TP2 and  3/services/epf-flower-data-science/src/data/database.sqlite
new file mode 100644
index 00000000..1679fb0c
Binary files /dev/null and b/TP2 and  3/services/epf-flower-data-science/src/data/database.sqlite differ
diff --git a/TP2 and  3/services/epf-flower-data-science/src/data/label_encoder.pkl b/TP2 and  3/services/epf-flower-data-science/src/data/label_encoder.pkl
new file mode 100644
index 00000000..f54644dc
Binary files /dev/null and b/TP2 and  3/services/epf-flower-data-science/src/data/label_encoder.pkl differ
diff --git a/TP2 and  3/services/epf-flower-data-science/src/data/processed_iris.csv b/TP2 and  3/services/epf-flower-data-science/src/data/processed_iris.csv
new file mode 100644
index 00000000..922bd52c
--- /dev/null
+++ b/TP2 and  3/services/epf-flower-data-science/src/data/processed_iris.csv	
@@ -0,0 +1,151 @@
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+1.0380047568006125,0.10644536396074403,1.0470871627887663,1.5794286131660755
+1.2803404976879151,0.10644536396074403,0.7627586428425047,1.4479556431467018
+-0.05250607719224957,-0.8191664965674684,0.7627586428425047,0.9220637630692072
+1.1591726272442633,0.3378483290927974,1.2176842747565237,1.4479556431467018
+1.0380047568006125,0.5692512942248498,1.103952866778019,1.7109015831854495
+1.0380047568006125,-0.12495760117130933,0.8196243468317573,1.4479556431467018
+0.5533332750260068,-1.281972426831575,0.7058929388532527,0.9220637630692072
+0.7956690159133096,-0.12495760117130933,0.8196243468317573,1.053536733088581
+0.432165404582356,0.8006542593569032,0.9333557548102621,1.4479556431467018
+0.06866179325140237,-0.12495760117130933,0.7627586428425047,0.7905907930498337
diff --git a/TP2 and  3/services/epf-flower-data-science/src/data/random_forest_model.pkl b/TP2 and  3/services/epf-flower-data-science/src/data/random_forest_model.pkl
new file mode 100644
index 00000000..d471ce68
Binary files /dev/null and b/TP2 and  3/services/epf-flower-data-science/src/data/random_forest_model.pkl differ
diff --git a/TP2 and  3/services/epf-flower-data-science/src/firestore.py b/TP2 and  3/services/epf-flower-data-science/src/firestore.py
new file mode 100644
index 00000000..ac01ac17
--- /dev/null
+++ b/TP2 and  3/services/epf-flower-data-science/src/firestore.py	
@@ -0,0 +1,24 @@
+import firebase_admin
+from firebase_admin import credentials, firestore
+from pathlib import Path
+
+def initialize_firebase():
+    """
+    Initialize Firebase Admin SDK with service account credentials
+    
+    Returns:
+        firestore.Client: Initialized Firestore client
+    """
+    # Chemin vers le fichier de clés de service Firebase
+    cred_path = Path(__file__).parent / "config" / "serviceAccountKey.json"
+    
+    # Initialisation de Firebase Admin avec les credentials
+    if not firebase_admin._apps:
+        cred = credentials.Certificate(str(cred_path))
+        firebase_admin.initialize_app(cred)
+    
+    # Création et retour du client Firestore
+    return firestore.client()
+
+# Création d'une instance du client Firestore
+db = initialize_firebase()
diff --git a/TP2 and  3/config/prd b/TP2 and  3/services/epf-flower-data-science/src/middleware/error_handlers.py
similarity index 100%
rename from TP2 and  3/config/prd
rename to TP2 and  3/services/epf-flower-data-science/src/middleware/error_handlers.py
diff --git a/TP2 and  3/services/epf-flower-data-science/src/middleware/rate_limit.py b/TP2 and  3/services/epf-flower-data-science/src/middleware/rate_limit.py
new file mode 100644
index 00000000..73756045
--- /dev/null
+++ b/TP2 and  3/services/epf-flower-data-science/src/middleware/rate_limit.py	
@@ -0,0 +1,22 @@
+from fastapi import HTTPException
+from datetime import datetime, timedelta
+from typing import Dict
+from collections import defaultdict
+
+class RateLimiter:
+    def __init__(self, requests_per_minute: int = 60):
+        self.requests_per_minute = requests_per_minute
+        self.requests: Dict[str, list] = defaultdict(list)
+
+    async def check_rate_limit(self, user_id: str):
+        now = datetime.now()
+        minute_ago = now - timedelta(minutes=1)
+        
+        # Clean old requests
+        self.requests[user_id] = [req_time for req_time in self.requests[user_id] 
+                                if req_time > minute_ago]
+        
+        if len(self.requests[user_id]) >= self.requests_per_minute:
+            raise HTTPException(status_code=429, detail="Rate limit exceeded")
+        
+        self.requests[user_id].append(now)
diff --git a/TP2 and  3/services/epf-flower-data-science/src/schemas/message.py b/TP2 and  3/services/epf-flower-data-science/src/schemas/message.py
index 2dc432eb..c0d708c2 100644
--- a/TP2 and  3/services/epf-flower-data-science/src/schemas/message.py	
+++ b/TP2 and  3/services/epf-flower-data-science/src/schemas/message.py	
@@ -1,5 +1,12 @@
 from src.schemas.camelcase import CamelCase
+from pydantic import BaseModel
 
 
 class MessageResponse(CamelCase):
     message: str
+
+class Parameter(BaseModel):
+    n_estimators: int
+    criterion: str
+
+__all__ = ['Parameter', 'MessageResponse']
diff --git a/TP2 and  3/services/epf-flower-data-science/src/services/auth.py b/TP2 and  3/services/epf-flower-data-science/src/services/auth.py
new file mode 100644
index 00000000..a26380fb
--- /dev/null
+++ b/TP2 and  3/services/epf-flower-data-science/src/services/auth.py	
@@ -0,0 +1,28 @@
+from firebase_admin import auth
+from fastapi import HTTPException, Security
+from fastapi.security import OAuth2PasswordBearer
+from functools import wraps
+
+oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
+
+def admin_required(func):
+    @wraps(func)
+    async def wrapper(*args, **kwargs):
+        token = kwargs.get('token')
+        try:
+            decoded_token = auth.verify_id_token(token)
+            user = auth.get_user(decoded_token['uid'])
+            if not user.custom_claims or not user.custom_claims.get('admin'):
+                raise HTTPException(status_code=403, detail="Admin access required")
+            return await func(*args, **kwargs)
+        except Exception as e:
+            raise HTTPException(status_code=401, detail="Invalid token")
+    return wrapper
+
+@router.get("/users", dependencies=[Depends(admin_required)])
+async def list_users():
+    try:
+        users = auth.list_users()
+        return {"users": [user.to_dict() for user in users.iterate_all()]}
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=str(e))
diff --git a/TP2 and  3/services/epf-flower-data-science/src/services/parameters.py b/TP2 and  3/services/epf-flower-data-science/src/services/parameters.py
index e69de29b..7e09e6be 100644
--- a/TP2 and  3/services/epf-flower-data-science/src/services/parameters.py	
+++ b/TP2 and  3/services/epf-flower-data-science/src/services/parameters.py	
@@ -0,0 +1,27 @@
+import firebase_admin
+from firebase_admin import credentials, firestore, auth
+from pathlib import Path
+
+# Initialize Firebase Admin SDK with complete configuration
+if not firebase_admin._apps:
+    cred_path = Path(__file__).parent.parent / "config" / "serviceAccountKey.json"
+    cred = credentials.Certificate(str(cred_path))
+    firebase_admin.initialize_app(cred, {
+        'projectId': 'myproject-0412025',
+        'serviceAccountId': 'data-source-api@myproject-0412025.iam.gserviceaccount.com',
+        'authDomain': 'myproject-0412025.firebaseapp.com'
+    })
+
+# Get Firestore client
+db = firestore.client()
+
+def get_parameters():
+    """Récupère les paramètres depuis Firestore"""
+    doc_ref = db.collection("parameters").document("eIxSqEg0PAg0cUAj66bh")
+    doc = doc_ref.get()
+    return doc.to_dict() if doc.exists else None
+
+def update_parameters(params: dict):
+    """Met à jour les paramètres dans Firestore"""
+    doc_ref = db.collection("parameters").document("eIxSqEg0PAg0cUAj66bh")
+    doc_ref.set(params)
diff --git a/TP2 and  3/services/epf-flower-data-science/tests/conftest.py b/TP2 and  3/services/epf-flower-data-science/tests/conftest.py
new file mode 100644
index 00000000..7d7ec260
--- /dev/null
+++ b/TP2 and  3/services/epf-flower-data-science/tests/conftest.py	
@@ -0,0 +1,13 @@
+import pytest
+
+pytest_plugins = [
+    "pytest_asyncio",
+]
+
+@pytest.fixture(scope="session")
+def event_loop():
+    """Create an instance of the default event loop for each test case."""
+    import asyncio
+    loop = asyncio.get_event_loop_policy().new_event_loop()
+    yield loop
+    loop.close()
diff --git a/TP2 and  3/services/epf-flower-data-science/tests/test_api.py b/TP2 and  3/services/epf-flower-data-science/tests/test_api.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TP2 and  3/services/epf-flower-data-science/tests/test_config.py b/TP2 and  3/services/epf-flower-data-science/tests/test_config.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TP2 and  3/services/epf-flower-data-science/tests/test_security.py b/TP2 and  3/services/epf-flower-data-science/tests/test_security.py
new file mode 100644
index 00000000..e69de29b
diff --git a/TP2 and  3/services/epf-flower-data-science/tests/unit/api/routes/test_Connexion_firestore.py b/TP2 and  3/services/epf-flower-data-science/tests/unit/api/routes/test_Connexion_firestore.py
new file mode 100644
index 00000000..ed8e469a
--- /dev/null
+++ b/TP2 and  3/services/epf-flower-data-science/tests/unit/api/routes/test_Connexion_firestore.py	
@@ -0,0 +1,13 @@
+from src.services.parameters import get_parameters, update_parameters
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))))
+
+# Test de la récupération des paramètres
+params = get_parameters()
+print("Current parameters:", params)
+
+# Test de la mise à jour des paramètres
+new_params = {"param1": "new_value", "param2": "updated_value"}
+update_parameters(new_params)
+print("Parameters updated.")
diff --git a/TP2 and  3/services/epf-flower-data-science/tests/unit/api/routes/test_hello.py b/TP2 and  3/services/epf-flower-data-science/tests/unit/api/routes/test_hello.py
index b717e296..4fdee592 100644
--- a/TP2 and  3/services/epf-flower-data-science/tests/unit/api/routes/test_hello.py	
+++ b/TP2 and  3/services/epf-flower-data-science/tests/unit/api/routes/test_hello.py	
@@ -30,3 +30,4 @@ def test_hello(self, client):
         assert response.json() == {
             "message": "Hello testuser, from fastapi test route !"
         }
+
diff --git a/TP2 and  3/services/epf-flower-data-science/tests/unit/test_auth.py b/TP2 and  3/services/epf-flower-data-science/tests/unit/test_auth.py
new file mode 100644
index 00000000..e69de29b