Codespace effective palm tree r65vjp9rqw6cx9jw

agents/agent1.py

@@ -24,19 +24,94 @@
 }
 
 # ── 2. Tools ───────────────────────────────────────────────────────────────
+def get_weather(lat: float, lon: float) -> dict:
+    """
+    Return today's forecast:
+        { "high": °C, "low": °C, "conditions": str }
+    """
+    url = (
+        "https://api.open-meteo.com/v1/forecast"
+        f"?latitude={lat}&longitude={lon}"
+        "&daily=weathercode,temperature_2m_max,temperature_2m_min"
+        "&forecast_days=1&timezone=auto"
+    )
+    r = requests.get(url, timeout=15)
+    r.raise_for_status()
+    daily = r.json()["daily"]
+    return {
+        "high":       daily["temperature_2m_max"][0],
+        "low":        daily["temperature_2m_min"][0],
+        "conditions": WEATHER_CODES.get(daily["weathercode"][0], "Unknown"),
+    }
+
+def convert_c_to_f(c: float) -> float:
+    return c * 9 / 5 + 32
 
 # ── 3. LLM client ───────────────────────────────────────────────────────────
 llm = ChatOllama(model="llama3.2", temperature=0.0)
 
 # ── 4. System prompt ────────────────────────────────────────────────────────
+SYSTEM = textwrap.dedent("""
+You are an agent with two tools:
+
+get_weather(lat:float, lon:float)
+    → {"high": float, "low": float, "conditions": str}
+
+convert_c_to_f(c:float) → float
+
+When you plan, emit exactly:
+
+Thought: <your thought>
+Action: <tool name>
+Args: {"lat":X,"lon":Y}   or   {"c":Z}
+
+Do NOT output anything else.
+""").strip()
 
 # ── 5. TAO run helper ───────────────────────────────────────────────────────
+def run(question: str) -> str:
+    """Execute the two-step TAO dance once, printing the trace, returning the answer."""
+    messages = [
+        {"role": "system", "content": SYSTEM},
+        {"role": "user",   "content": question},
+    ]
+
+    print("\n--- Thought → Action → Observation → Final ---\n")
 
     # LLM chooses coordinates
+    reply1 = llm.invoke(messages)
+    plan1  = reply1.content.strip()
+    print(plan1 + "\n")
+
+    coords = json.loads(plan1.split("Args:")[1].strip())
+    obs1   = get_weather(**coords)
+    print(f"Observation: {obs1}\n")
+
 
     # LLM decides to convert °C → °F
+    messages += [
+        {"role": "assistant", "content": plan1},
+        {"role": "user",      "content": f"Observation: {obs1}"}
+    ]
+    reply2 = llm.invoke(messages)
+    plan2  = reply2.content.strip()
+    print(plan2 + "\n")
+
+    # Convert both temps
+    high_f = convert_c_to_f(obs1["high"])
+    low_f  = convert_c_to_f(obs1["low"])
+    obs2   = {"high_f": high_f, "low_f": low_f}
+    print(f"Observation: {obs2}\n")
+
 
     # Assemble final answer
+    final = (
+        f"Today will be **{obs1['conditions']}** with a high of "
+        f"**{high_f:.1f} °F** and a low of **{low_f:.1f} °F**."
+    )
+    print(f"Final: {final}\n")
+    return final
+
 
 # ── 6. Interactive loop ────────────────────────────────────────────────────
 if __name__ == "__main__":
@@ -48,3 +123,13 @@
             break
 
         # Build the question for the agent
+        query = (
+            f"What is the predicted weather today for {loc}? "
+            "Include conditions plus high/low in °F."
+        )
+
+        try:
+            run(query)          # prints the entire trace internally
+        except Exception as e:
+            print(f"⚠️  Error: {e}\n")
+

agents/agent5.py

@@ -20,16 +20,76 @@ def book_flight(flight_number: str):
 
 # Define the AI agent 
 
+# Defines the AI agents
+
+booking_agent = Agent(
+    role="Airline Booking Assistant",
+    goal="Help users book flights efficiently.",
+    backstory="You are an expert airline booking assistant, providing the best booking options with clear information.",
+    verbose=True,
+    llm=ollama_llm,
+)
+
+# New agent for travel planning tasks
+travel_agent = Agent(
+    role="Travel Assistant",
+    goal="Assist in planning and organizing travel details.",
+    backstory="You are skilled at planning and organizing travel itineraries efficiently.",
+    verbose=True,
+    llm=ollama_llm,
+)
+
+# New agent for customer service tasks
+customer_service_agent = Agent(
+    role="Customer Service Representative",
+    goal="Provide excellent customer service by handling user requests and presenting options.",
+    backstory="You are skilled at providing customer support and ensuring user satisfaction.",
+    verbose=True,
+    llm=ollama_llm,
+)
+
 # Define the tasks with expected outputs
 # Create a task to extract travel details (source, destination, date) using the LLM.
+extract_travel_info_task = Task(
+    description=(
+        "Extract destination and date from {user_request}"
+       # "Return your result as a JSON object formatted as: "
+       # "{\"destination\": \"<destination>\", \"date\": \"<date>\"}."
+    ),
+    agent=customer_service_agent,
+    expected_output="A JSON object containing 'destination', and 'date'."
+)
 
 # Create a task to find suitable flights
+find_flights_task = Task(
+    description="Find available flights for the extracted destination and date.",
+    agent=travel_agent,
+    expected_output="A JSON list of available flights, including flight number, airline, departure time, and price."
+)
 
 # Create a task to present flight options 
+present_flights_task = Task(
+    description="Present flight options in a user-friendly format and ask the user to choose one. The list of flight should not be in JSON format and should have a number by each choice.",
+    agent=customer_service_agent,
+    expected_output="The selected flight number chosen by the user. If no response, just use 1."
+)
 
 # Create a task to book the flight
+book_flight_task = Task(
+    description="Book the selected flight and confirm booking. If more than one option, just use the first one.",
+    agent=booking_agent,
+    function=lambda details: book_flight(details["flight_number"]),
+    expected_output="A confirmation message with flight number and booking status.",
+    verbose=True
+)
 
 # Create the Crew
+crew = Crew(
+    agents=[booking_agent, customer_service_agent, travel_agent],
+    tasks=[extract_travel_info_task, find_flights_task, present_flights_task, book_flight_task],
+    process="sequential",
+    verbose=False
+)
 
 # Run the process with user input
 user_input = "I need a flight to New York on August 11, 2025."

agents/curr_conv_agent.py

@@ -7,16 +7,87 @@
 # -----------------------------------------------------------------------------
 # MEMORY PERSISTENCE (with history)
 # -----------------------------------------------------------------------------
+MEMORY_FILE = "currency_memory.json"
+
+def load_memory():
+    """Load memory from disk, initializing history if missing."""
+    if os.path.exists(MEMORY_FILE):
+        mem = json.load(open(MEMORY_FILE))
+        # Ensure history key exists
+        if "history" not in mem:
+            mem["history"] = []
+        return mem
+    # Default memory structure
+    return {"last_amount": None, "last_from": None, "last_to": None, "history": []}
+
+def save_memory(mem):
+    """Persist memory (including history) back to disk."""
+    with open(MEMORY_FILE, "w") as f:
+        json.dump(mem, f)
+
+memory = load_memory()
 
 # -----------------------------------------------------------------------------
 # SMOLAGENTS TOOLS
 # -----------------------------------------------------------------------------
 
 # tool to get rates from a URL
+@tool
+def fetch_live_rate(from_currency: str, to_currency: str) -> float:
+    """
+    Retrieve a live exchange rate from the fawazahmed0 Exchange API.
+
+    Args:
+        from_currency (str): 3-letter source code, e.g. "USD"
+        to_currency   (str): 3-letter target code, e.g. "EUR"
+
+    Returns:
+        float: Target units per one source unit.
+
+    Raises:
+        RuntimeError: if the rate cannot be fetched.
+    """
+    base = from_currency.lower()
+    target = to_currency.lower()
+    urls = [
+        f"https://cdn.jsdelivr.net/npm/@fawazahmed0/currency-api@latest"
+        f"/v1/currencies/{base}.json",
+        f"https://latest.currency-api.pages.dev"
+        f"/v1/currencies/{base}.json"
+    ]
+    for url in urls:
+        try:
+            resp = requests.get(url, timeout=5)
+            resp.raise_for_status()
+            data = resp.json()
+            rates = data.get(base, {})
+            if target in rates:
+                return rates[target]
+        except Exception:
+            continue
+    raise RuntimeError(f"Failed to fetch rate from {from_currency} to {to_currency}")
 
 
 # tool to do basic calculations
 
+@tool
+def calculate(expression: str) -> float:
+    """
+    Evaluate a basic arithmetic expression safely.
+
+    Args:
+        expression (str): e.g. "100 * 0.85"
+
+    Returns:
+        float: numeric result.
+
+    Raises:
+        RuntimeError: if the expression is invalid.
+    """
+    try:
+        return eval(expression, {"__builtins__": {}})
+    except Exception as e:
+        raise RuntimeError(f"Calculation error: {e}")
 
 # -----------------------------------------------------------------------------
 # AGENT CONFIGURATION
@@ -29,6 +100,11 @@
     temperature=0.0,  # deterministic tool use
 )
 
+agent = CodeAgent(
+    tools=[fetch_live_rate, calculate],
+    model=model,
+    add_base_tools=False
+)
 
 # -----------------------------------------------------------------------------
 # QUERY PARSING + FILLING from MEMORY
@@ -46,7 +122,30 @@ def parse_and_fill(query: str):
     """
     q = query.strip()
     amt = frm = to = None
-
+
+    # 1) Full form: amount + source + "to" + target
+    m1 = re.match(r"(?:Convert\s+)?([\d.]+)\s*([A-Za-z]{3})\s+to\s+([A-Za-z]{3})$", q, re.IGNORECASE)
+    if m1:
+        amt, frm, to = m1.group(1), m1.group(2).upper(), m1.group(3).upper()
+    else:
+        # 2) Amount + source only
+        m2 = re.match(r"(?:Convert\s+)?([\d.]+)\s*([A-Za-z]{3})$", q, re.IGNORECASE)
+        if m2:
+            amt, frm = m2.group(1), m2.group(2).upper()
+            to = memory["last_to"]
+
+        # 3) Amount + "to" + target only
+        m3 = re.match(r"(?:Convert\s+)?([\d.]+)\s+to\s+([A-Za-z]{3})$", q, re.IGNORECASE)
+        if m3:
+            amt, to = m3.group(1), m3.group(2).upper()
+            frm = memory["last_from"]
+
+        # 4) Amount only
+        m4 = re.match(r"(?:Convert\s+)?([\d.]+)$", q, re.IGNORECASE)
+        if m4:
+            amt = m4.group(1)
+            frm = memory["last_from"]
+            to  = memory["last_to"]
 
     if not (amt and frm and to):
         raise ValueError(
@@ -79,6 +178,18 @@ def parse_and_fill(query: str):
 
         # Handle special commands - exit and history
 
+        if low in ("exit", "quit"):
+            print("Goodbye!")
+            break
+        if low in ("history", "show history"):
+            if not memory["history"]:
+                print("No conversion history yet.\n")
+            else:
+                print("Conversion History:")
+                for entry in memory["history"]:
+                    print(f"  • {entry['query']} → {entry['result']:.2f}")
+                print()
+            continue
 
         # Normal convert request
 
@@ -87,9 +198,12 @@ def parse_and_fill(query: str):
             prompt = f"Convert {amt} {frm} to {to}"
 
             # Run the agent (LLM will call fetch_live_rate & calculate)
-
+            result = agent.run(prompt)
 
             # Store and persist this interaction
+            memory["history"].append({"query": user_input, "amount": amt,
+                                      "from": frm, "to": to, "result": float(result)})
+            save_memory(memory)
 
             # Friendly output
             print(f"{amt} {frm} is approximately {float(result):.2f} {to}.\n")

agents/currency_memory.json

@@ -0,0 +1 @@
+{"last_amount": "500", "last_from": "JPY", "last_to": "INR", "history": [{"query": "Convert 100 USD to EUR", "amount": "100", "from": "USD", "to": "EUR", "result": 85.949297}, {"query": "Convert 300", "amount": "300", "from": "USD", "to": "EUR", "result": 257.703555}, {"query": "Convert 300 to INR", "amount": "300", "from": "USD", "to": "INR", "result": 26305.982415000002}, {"query": "Convert 150 JPY", "amount": "150", "from": "JPY", "to": "INR", "result": 11907.0}]}