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Refactor ai_news_generator to use CrewAI Flows #174

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49 changes: 44 additions & 5 deletions ai_news_generator/README.md
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# AI News generator
# AI News Generator with CrewAI Flows

This project leverages CrewAI and Cohere's Command-R:7B model to build an AI news generator!
This project leverages **CrewAI Flows** and Cohere's Command-R:7B model to build an advanced AI news generator with event-driven, agentic workflows!

## Installation and setup
## Features

- 🌊 **CrewAI Flows**: Event-driven workflow orchestration
- 🔄 **State Management**: Structured data flow between AI agents
- 🎯 **Task Chaining**: Sequential execution with @start and @listen decorators
- ⚡ **Optimized Performance**: Better control and coordination of AI agents
- 📊 **Real-time Feedback**: Visual flow execution progress

## Architecture

The application uses CrewAI Flows to create a structured, event-driven workflow:

```python
class AINewsGeneratorFlow(Flow[NewsGenerationState]):

@start()
def research_phase(self):
# Research agent conducts comprehensive topic research
# Updates state with research findings

@listen(research_phase)
def content_writing_phase(self, research_report: str):
# Writing agent transforms research into engaging content
# Uses structured state for data flow
```

### Flow Execution:
1. **Research Phase** (⚙️ `@start()`): Senior Research Analyst conducts comprehensive research
2. **Content Writing Phase** (🔊 `@listen(research_phase)`): Content Writer transforms research into engaging blog posts
3. **State Management**: Structured state ensures proper data flow between phases

## Installation and Setup

**Get API Keys**:
- [Serper API Key](https://serper.dev/)
- [Cohere API Key](https://dashboard.cohere.com/api-keys)


**Install Dependencies**:
Ensure you have Python 3.11 or later installed.
```bash
pip install crewai crewai-tools
pip install crewai crewai-tools streamlit python-dotenv pydantic
```

**Environment Setup**:
1. Copy `.env.example` to `.env`
2. Add your API keys to the `.env` file

**Run the Application**:
```bash
streamlit run app.py
```

---
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279 changes: 178 additions & 101 deletions ai_news_generator/app.py
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import os
import streamlit as st
from crewai import Agent, Task, Crew, LLM
from crewai.flow.flow import Flow, listen, start
from crewai_tools import SerperDevTool
from dotenv import load_dotenv
from pydantic import BaseModel
from typing import Optional

# Load environment variables
load_dotenv()
Expand All @@ -11,8 +14,20 @@
st.set_page_config(page_title="AI News Generator", page_icon="📰", layout="wide")

# Title and description
st.title("🤖 AI News Generator, powered by CrewAI and Cohere's Command R7B")
st.markdown("Generate comprehensive blog posts about any topic using AI agents.")
st.title("🤖 AI News Generator with CrewAI Flows")
st.markdown("Generate comprehensive blog posts using **CrewAI Flows** - an event-driven, agentic workflow powered by Cohere's Command R7B")

# Add Flow benefits section
st.markdown("### 🌊 Flow Benefits")
col1, col2, col3 = st.columns(3)
with col1:
st.markdown("**🔄 Event-Driven**\nTasks execute based on previous results")
with col2:
st.markdown("**🎯 State Management**\nStructured data flow between agents")
with col3:
st.markdown("**⚡ Optimized**\nBetter control and orchestration")

st.markdown("---")

# Sidebar
with st.sidebar:
Expand All @@ -35,6 +50,10 @@
# Make the generate button more prominent in the sidebar
generate_button = st.button("Generate Content", type="primary", use_container_width=True)

# Add Flow visualization
st.markdown("### Flow Visualization")
st.info("This application uses CrewAI Flows for structured, event-driven content generation:\n1. **Research Phase**: AI agent researches the topic\n2. **Writing Phase**: AI agent transforms research into engaging content")

# Add some helpful information
with st.expander("ℹ️ How to use"):
st.markdown("""
Expand All @@ -45,121 +64,179 @@
5. Download the result as a markdown file
""")

def generate_content(topic):
llm = LLM(
model="command-r",
temperature=0.7
)

search_tool = SerperDevTool(n_results=10)

# First Agent: Senior Research Analyst
senior_research_analyst = Agent(
role="Senior Research Analyst",
goal=f"Research, analyze, and synthesize comprehensive information on {topic} from reliable web sources",
backstory="You're an expert research analyst with advanced web research skills. "
"You excel at finding, analyzing, and synthesizing information from "
"across the internet using search tools. You're skilled at "
"distinguishing reliable sources from unreliable ones, "
"fact-checking, cross-referencing information, and "
"identifying key patterns and insights. You provide "
"well-organized research briefs with proper citations "
"and source verification. Your analysis includes both "
"raw data and interpreted insights, making complex "
"information accessible and actionable.",
allow_delegation=False,
verbose=True,
tools=[search_tool],
llm=llm
)

# Second Agent: Content Writer
content_writer = Agent(
role="Content Writer",
goal="Transform research findings into engaging blog posts while maintaining accuracy",
backstory="You're a skilled content writer specialized in creating "
"engaging, accessible content from technical research. "
"You work closely with the Senior Research Analyst and excel at maintaining the perfect "
"balance between informative and entertaining writing, "
"while ensuring all facts and citations from the research "
"are properly incorporated. You have a talent for making "
"complex topics approachable without oversimplifying them.",
allow_delegation=False,
verbose=True,
llm=llm
)
class NewsGenerationState(BaseModel):
topic: str = ""
research_report: str = ""
final_article: str = ""
temperature: float = 0.7

# Research Task
research_task = Task(
description=("""
1. Conduct comprehensive research on {topic} including:
- Recent developments and news
- Key industry trends and innovations
- Expert opinions and analyses
- Statistical data and market insights
2. Evaluate source credibility and fact-check all information
3. Organize findings into a structured research brief
4. Include all relevant citations and sources
"""),
expected_output="""A detailed research report containing:
- Executive summary of key findings
- Comprehensive analysis of current trends and developments
- List of verified facts and statistics
- All citations and links to original sources
- Clear categorization of main themes and patterns
Please format with clear sections and bullet points for easy reference.""",
agent=senior_research_analyst
)

# Writing Task
writing_task = Task(
description=("""
Using the research brief provided, create an engaging blog post that:
1. Transforms technical information into accessible content
2. Maintains all factual accuracy and citations from the research
3. Includes:
- Attention-grabbing introduction
- Well-structured body sections with clear headings
- Compelling conclusion
4. Preserves all source citations in [Source: URL] format
5. Includes a References section at the end
"""),
expected_output="""A polished blog post in markdown format that:
- Engages readers while maintaining accuracy
- Contains properly structured sections
- Includes Inline citations hyperlinked to the original source url
- Presents information in an accessible yet informative way
- Follows proper markdown formatting, use H1 for the title and H3 for the sub-sections""",
agent=content_writer
)

# Create Crew
crew = Crew(
agents=[senior_research_analyst, content_writer],
tasks=[research_task, writing_task],
verbose=True
)
class AINewsGeneratorFlow(Flow[NewsGenerationState]):

@start()
def research_phase(self):
"""Conduct comprehensive research on the given topic"""
llm = LLM(
model="command-r",
temperature=self.state.temperature
)

search_tool = SerperDevTool(n_results=10)

# Senior Research Analyst Agent
senior_research_analyst = Agent(
role="Senior Research Analyst",
goal=f"Research, analyze, and synthesize comprehensive information on {self.state.topic} from reliable web sources",
backstory="You're an expert research analyst with advanced web research skills. "
"You excel at finding, analyzing, and synthesizing information from "
"across the internet using search tools. You're skilled at "
"distinguishing reliable sources from unreliable ones, "
"fact-checking, cross-referencing information, and "
"identifying key patterns and insights. You provide "
"well-organized research briefs with proper citations "
"and source verification. Your analysis includes both "
"raw data and interpreted insights, making complex "
"information accessible and actionable.",
allow_delegation=False,
verbose=True,
tools=[search_tool],
llm=llm
)

# Research Task
research_task = Task(
description=("""
1. Conduct comprehensive research on {topic} including:
- Recent developments and news
- Key industry trends and innovations
- Expert opinions and analyses
- Statistical data and market insights
2. Evaluate source credibility and fact-check all information
3. Organize findings into a structured research brief
4. Include all relevant citations and sources
"""),
expected_output="""A detailed research report containing:
- Executive summary of key findings
- Comprehensive analysis of current trends and developments
- List of verified facts and statistics
- All citations and links to original sources
- Clear categorization of main themes and patterns
Please format with clear sections and bullet points for easy reference.""",
agent=senior_research_analyst
)

# Create Research Crew
research_crew = Crew(
agents=[senior_research_analyst],
tasks=[research_task],
verbose=True
)

# Execute research and store result
research_result = research_crew.kickoff(inputs={"topic": self.state.topic})
self.state.research_report = research_result.raw

return research_result.raw

@listen(research_phase)
def content_writing_phase(self, research_report: str):
"""Transform research findings into engaging blog content"""
llm = LLM(
model="command-r",
temperature=self.state.temperature
)

# Content Writer Agent
content_writer = Agent(
role="Content Writer",
goal="Transform research findings into engaging blog posts while maintaining accuracy",
backstory="You're a skilled content writer specialized in creating "
"engaging, accessible content from technical research. "
"You work closely with the Senior Research Analyst and excel at maintaining the perfect "
"balance between informative and entertaining writing, "
"while ensuring all facts and citations from the research "
"are properly incorporated. You have a talent for making "
"complex topics approachable without oversimplifying them.",
allow_delegation=False,
verbose=True,
llm=llm
)

# Writing Task
writing_task = Task(
description=("""
Using the research brief provided, create an engaging blog post that:
1. Transforms technical information into accessible content
2. Maintains all factual accuracy and citations from the research
3. Includes:
- Attention-grabbing introduction
- Well-structured body sections with clear headings
- Compelling conclusion
4. Preserves all source citations in [Source: URL] format
5. Includes a References section at the end

Research Brief: {research_report}
"""),
expected_output="""A polished blog post in markdown format that:
- Engages readers while maintaining accuracy
- Contains properly structured sections
- Includes Inline citations hyperlinked to the original source url
- Presents information in an accessible yet informative way
- Follows proper markdown formatting, use H1 for the title and H3 for the sub-sections""",
agent=content_writer
)

# Create Writing Crew
writing_crew = Crew(
agents=[content_writer],
tasks=[writing_task],
verbose=True
)

# Execute writing and store result
writing_result = writing_crew.kickoff(inputs={
"research_report": research_report,
"topic": self.state.topic
})
self.state.final_article = writing_result.raw

return writing_result.raw

return crew.kickoff(inputs={"topic": topic})
def generate_content(topic, temperature=0.7):
"""Generate content using the AI News Generator Flow"""
flow = AINewsGeneratorFlow()
flow.state.topic = topic
flow.state.temperature = temperature

result = flow.kickoff()
return result

# Main content area
if generate_button:
with st.spinner('Generating content... This may take a moment.'):
if generate_button and topic:
with st.spinner('Generating content using CrewAI Flows... This may take a moment.'):
try:
result = generate_content(topic)
result = generate_content(topic, temperature)
st.markdown("### Generated Content")
st.markdown(result)

# Add download button
st.download_button(
label="Download Content",
data=result.raw,
data=result,
file_name=f"{topic.lower().replace(' ', '_')}_article.md",
mime="text/markdown"
)

# Show flow execution info
with st.expander("Flow Execution Details"):
st.success("✅ Research Phase: Completed")
st.success("✅ Content Writing Phase: Completed")
st.info("Flow executed successfully using CrewAI Flows with structured state management and event-driven task chaining.")

except Exception as e:
st.error(f"An error occurred: {str(e)}")
elif generate_button and not topic:
st.warning("Please enter a topic before generating content.")

# Footer
st.markdown("---")
st.markdown("Built with CrewAI, Streamlit and powered by Cohere's Command R7B")
st.markdown("Built with **CrewAI Flows**, Streamlit and powered by Cohere's Command R7B")
6 changes: 6 additions & 0 deletions ai_news_generator/requirements.txt
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crewai>=0.83.0
crewai-tools>=0.17.0
streamlit>=1.40.0
python-dotenv>=1.0.0
pydantic>=2.0.0
litellm>=1.55.0