Merge branch 'Shubhamsaboo:main' into ai-health

ai_agent_tutorials/ai_meeting_agent/meeting_agent.py

@@ -1,7 +1,6 @@
 import streamlit as st
-from crewai import Agent, Task, Crew, Process
-from langchain_openai import ChatOpenAI
-from langchain_anthropic import ChatAnthropic
+from crewai import Agent, Task, Crew, LLM
+from crewai.process import Process
 from crewai_tools import SerperDevTool
 import os
 
@@ -11,20 +10,16 @@ st.title("AI Meeting Preparation Agent 📝")
 
 # Sidebar for API keys
 st.sidebar.header("API Keys")
-openai_api_key = st.sidebar.text_input("OpenAI API Key", type="password")
 anthropic_api_key = st.sidebar.text_input("Anthropic API Key", type="password")
 serper_api_key = st.sidebar.text_input("Serper API Key", type="password")
 
 # Check if all API keys are set
-if openai_api_key and anthropic_api_key and serper_api_key:
-    # Set API keys as environment variables
-    os.environ["OPENAI_API_KEY"] = openai_api_key
+if anthropic_api_key and serper_api_key:
+    # # Set API keys as environment variables
     os.environ["ANTHROPIC_API_KEY"] = anthropic_api_key
     os.environ["SERPER_API_KEY"] = serper_api_key
 
-    # Initialize the AI models and tools
-    gpt4 = ChatOpenAI(model_name="gpt-4o-mini")
-    claude = ChatAnthropic(model_name="claude-3-5-sonnet-20240620")
+    claude = LLM(model="claude-3-5-sonnet-20240620", temperature= 0.7, api_key=anthropic_api_key)
     search_tool = SerperDevTool()
 
     # Input fields
@@ -41,7 +36,7 @@ if openai_api_key and anthropic_api_key and serper_api_key:
         backstory='You are an expert at quickly understanding complex business contexts and identifying critical information.',
         verbose=True,
         allow_delegation=False,
-        llm=gpt4,
+        llm=claude,
         tools=[search_tool]
     )
 
@@ -51,7 +46,7 @@ if openai_api_key and anthropic_api_key and serper_api_key:
         backstory='You are a seasoned industry analyst with a knack for spotting emerging trends and opportunities.',
         verbose=True,
         allow_delegation=False,
-        llm=gpt4,
+        llm=claude,
         tools=[search_tool]
     )
 

ai_agent_tutorials/ai_reasoning_agent/local_ai_reasoning_agent.py

@@ -0,0 +1,12 @@
+from phi.agent import Agent
+from phi.model.ollama import Ollama
+from phi.playground import Playground, serve_playground_app
+
+reasoning_agent = Agent(name="Reasoning Agent", model=Ollama(id="qwq:32b"), markdown=True)
+
+# UI for Reasoning agent
+app = Playground(agents=[reasoning_agent]).get_app()
+
+# Run the Playground app
+if __name__ == "__main__":
+    serve_playground_app("local_ai_reasoning_agent:app", reload=True)

ai_agent_tutorials/ai_startup_trend_analysis_agent/README.md

@@ -0,0 +1,42 @@
+## 📈 AI Startup Trend Analysis Agent 
+The AI Startup Trend Analysis Agent is tool for budding entrepreneurs that generates actionable insights by identifying nascent trends, potential market gaps, and growth opportunities in specific sectors. Entrepreneurs can use these data-driven insights to validate ideas, spot market opportunities, and make informed decisions about their startup ventures. It combines Newspaper4k and DuckDuckGo to scan and analyze startup-focused articles and market data. Using Claude 3.5 Sonnet, it processes this information to extract emerging patterns and enable entrepreneurs to identify promising startup opportunities.
+
+
+### Features
+- **User Prompt**: Entrepreneurs can input specific startup sectors or technologies of interest for research.
+- **News Collection**: This agent gathers recent startup news, funding rounds, and market analyses using DuckDuckGo.
+- **Summary Generation**: Concise summaries of verified information are generated using Newspaper4k.
+- **Trend Analysis**: The system identifies emerging patterns in startup funding, technology adoption, and market opportunities across analyzed stories.
+- **Streamlit UI**: The application features a user-friendly interface built with Streamlit for easy interaction.
+
+### How to Get Started
+1. **Clone the repository**:
+   ```bash
+   git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 
+   cd ai_agent_tutorials/ai_business_insider_agent
+   ```
+
+2. **Create and activate a virtual environment**:
+   ```bash
+   # For macOS/Linux
+   python -m venv venv
+   source venv/bin/activate
+
+   # For Windows
+   python -m venv venv
+   .\venv\Scripts\activate
+   ```
+
+3. **Install the required packages**:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+4. **Run the application**:
+   ```bash
+   streamlit run business_insider_agent.py
+   ```
+### Important Note
+- The system specifically uses Claude's API for advanced language processing. You can obtain your Anthropic API key from [Anthropic's website](https://www.anthropic.com/api).
+
+

ai_agent_tutorials/ai_startup_trend_analysis_agent/requirements.txt

@@ -0,0 +1,5 @@
+phidata==2.5.33
+streamlit==1.40.2
+duckduckgo_search==6.3.7
+newspaper4k==0.9.3.1
+lxml_html_clean==0.4.1

ai_agent_tutorials/ai_startup_trend_analysis_agent/startup_trends_agent.py

@@ -0,0 +1,99 @@
+import streamlit as st
+from phi.agent import Agent
+from phi.tools.duckduckgo import DuckDuckGo
+from phi.model.anthropic import Claude
+from phi.tools.newspaper4k import Newspaper4k
+from phi.tools import Tool
+import logging
+
+logging.basicConfig(level=logging.DEBUG)
+
+# Setting up Streamlit app
+st.title("AI Startup Trend Analysis Agent 📈")
+st.caption("Get the latest trend analysis and startup opportunities based on your topic of interest in a click!.")
+
+topic = st.text_input("Enter the area of interest for your Startup:")
+anthropic_api_key = st.sidebar.text_input("Enter Anthropic API Key", type="password")
+
+if st.button("Generate Analysis"):
+    if not anthropic_api_key:
+        st.warning("Please enter the required API key.")
+    else:
+        with st.spinner("Processing your request..."):
+            try:
+                # Initialize Anthropic model
+                anthropic_model = Claude(id ="claude-3-5-sonnet-20240620",api_key=anthropic_api_key)
+
+                # Define News Collector Agent - Duckduckgo_search tool enables an Agent to search the web for information.
+                search_tool = DuckDuckGo(search=True, news=True, fixed_max_results=5)
+                news_collector = Agent(
+                    name="News Collector",
+                    role="Collects recent news articles on the given topic",
+                    tools=[search_tool],
+                    model=anthropic_model,
+                    instructions=["Gather latest articles on the topic"],
+                    show_tool_calls=True,
+                    markdown=True,
+                )
+
+                # Define Summary Writer Agent
+                news_tool = Newspaper4k(read_article=True, include_summary=True)
+                summary_writer = Agent(
+                    name="Summary Writer",
+                    role="Summarizes collected news articles",
+                    tools=[news_tool],
+                    model=anthropic_model,
+                    instructions=["Provide concise summaries of the articles"],
+                    show_tool_calls=True,
+                    markdown=True,
+                )
+
+                # Define Trend Analyzer Agent
+                trend_analyzer = Agent(
+                    name="Trend Analyzer",
+                    role="Analyzes trends from summaries",
+                    model=anthropic_model,
+                    instructions=["Identify emerging trends and startup opportunities"],
+                    show_tool_calls=True,
+                    markdown=True,
+                )
+
+                # The multi agent Team setup of phidata:
+                agent_team = Agent(
+                    agents=[news_collector, summary_writer, trend_analyzer],
+                    instructions=[
+                        "First, search DuckDuckGo for recent news articles related to the user's specified topic.",
+                        "Then, provide the collected article links to the summary writer.",
+                        "Important: you must ensure that the summary writer receives all the article links to read.",
+                        "Next, the summary writer will read the articles and prepare concise summaries of each.",
+                        "After summarizing, the summaries will be passed to the trend analyzer.",
+                        "Finally, the trend analyzer will identify emerging trends and potential startup opportunities based on the summaries provided in a detailed Report form so that any young entreprenur can get insane value reading this easily"
+                    ],
+                    show_tool_calls=True,
+                    markdown=True,
+                )
+
+                # Executing the workflow
+                # Step 1: Collect news
+                news_response = news_collector.run(f"Collect recent news on {topic}")
+                articles = news_response.content
+
+                # Step 2: Summarize articles
+                summary_response = summary_writer.run(f"Summarize the following articles:\n{articles}")
+                summaries = summary_response.content
+
+                # Step 3: Analyze trends
+                trend_response = trend_analyzer.run(f"Analyze trends from the following summaries:\n{summaries}")
+                analysis = trend_response.content
+
+                # Display results - if incase you want to use this furthur, you can uncomment the below 2 lines to get the summaries too!
+                # st.subheader("News Summaries")
+                # # st.write(summaries)
+
+                st.subheader("Trend Analysis and Potential Startup Opportunities")
+                st.write(analysis)
+
+            except Exception as e:
+                st.error(f"An error occurred: {e}")
+else:
+    st.info("Enter the topic and API keys, then click 'Generate Analysis' to start.")