Create app.py

app.py

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+import streamlit as st
+import os
+from langchain_groq import ChatGroq
+from langchain.prompts import PromptTemplate
+from langchain.schema import HumanMessage
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# Configure Streamlit page
+st.set_page_config(
+    page_title="AI Research Assistant",
+    page_icon="🤖",
+    layout="wide"
+)
+
+# App title and description
+st.title("🤖 Agentic AI Research Assistant")
+st.markdown("Enter a topic and get a structured research summary with key subtopics!")
+
+# Sidebar for API key input
+with st.sidebar:
+    st.header("🔑 Configuration")
+    # Try to get API key from environment variable first
+    default_api_key = os.environ.get("GROQ_API_KEY", "")
+    
+    groq_api_key = st.text_input(
+        "Enter your Groq API Key:",
+        value=default_api_key,
+        type="password",
+        help="Get your free API key from https://console.groq.com/"
+    )
+    
+    # Model selection
+    model_choice = st.selectbox(
+        "Choose Model:",
+        ["llama-3.1-8b-instant", "mixtral-8x7b-32768"],
+        help="LLaMA3 is faster, Mixtral is more capable"
+    )
+
+def initialize_agent(api_key, model_name):
+    """Initialize the Groq LLM agent"""
+    try:
+        llm = ChatGroq(
+            groq_api_key=api_key,
+            model_name=model_name,
+            temperature=0.3,
+            max_tokens=1024
+        )
+        return llm
+    except Exception as e:
+        st.error(f"Error initializing agent: {str(e)}")
+        return None
+
+def create_research_prompt():
+    """Create the research prompt template"""
+    template = """
+You are an AI research assistant. Your task is to analyze a given topic and break it down into subtopics with summaries.
+
+TOPIC: {topic}
+
+INSTRUCTIONS:
+1. Break the topic into exactly 3 relevant subtopics
+2. For each subtopic, provide 3-5 bullet points summary
+3. Keep summaries concise and informative
+4. Focus on the most important and current aspects
+
+FORMAT YOUR RESPONSE EXACTLY LIKE THIS:
+
+## Subtopic 1: [Subtopic Name]
+• [Bullet point 1]
+• [Bullet point 2]
+• [Bullet point 3]
+• [Bullet point 4]
+• [Bullet point 5]
+
+## Subtopic 2: [Subtopic Name]
+• [Bullet point 1]
+• [Bullet point 2]
+• [Bullet point 3]
+• [Bullet point 4]
+
+## Subtopic 3: [Subtopic Name]
+• [Bullet point 1]
+• [Bullet point 2]
+• [Bullet point 3]
+• [Bullet point 4]
+• [Bullet point 5]
+
+Topic to analyze: {topic}
+"""
+    return PromptTemplate(template=template, input_variables=["topic"])
+
+def process_research_query(agent, topic):
+    """Process the research query using the agent"""
+    try:
+        # Create prompt
+        prompt_template = create_research_prompt()
+        formatted_prompt = prompt_template.format(topic=topic)
+        
+        # Get response from agent
+        with st.spinner("🔍 Researching and analyzing..."):
+            response = agent.invoke([HumanMessage(content=formatted_prompt)])
+            
+        return response.content
+    
+    except Exception as e:
+        st.error(f"Error processing query: {str(e)}")
+        return None
+
+def display_results(results):
+    """Display the research results in a formatted way"""
+    if results:
+        st.markdown("## 📊 Research Summary")
+        st.markdown(results)
+        
+        # Add download option
+        st.download_button(
+            label="📥 Download Summary",
+            data=results,
+            file_name="research_summary.md",
+            mime="text/markdown"
+        )
+
+def main():
+    # Check if API key is provided
+    if not groq_api_key:
+        st.warning("⚠️ Please enter your Groq API key in the sidebar to get started.")
+        st.markdown("""
+        ### How to get your Groq API key:
+        1. Visit [Groq Console](https://console.groq.com/)
+        2. Sign up for a free account
+        3. Navigate to API Keys section
+        4. Create a new API key
+        5. Copy and paste it in the sidebar
+        """)
+        return
+    
+    # Initialize the agent
+    agent = initialize_agent(groq_api_key, model_choice)
+    if not agent:
+        return
+    
+    # Main interface
+    col1, col2 = st.columns([2, 1])
+    
+    with col1:
+        # Topic input
+        topic = st.text_input(
+            "🎯 Enter your research topic:",
+            placeholder="e.g., Latest AI tools for teachers",
+            help="Be specific for better results"
+        )
+    
+    with col2:
+        st.markdown("<br>", unsafe_allow_html=True)  # Add space
+        research_button = st.button("🚀 Start Research", type="primary")
+    
+    # Process query when button is clicked
+    if research_button and topic:
+        if len(topic.strip()) < 3:
+            st.error("Please enter a more specific topic (at least 3 characters)")
+            return
+            
+        # Process the research query
+        results = process_research_query(agent, topic.strip())
+        
+        if results:
+            display_results(results)
+    
+    elif research_button and not topic:
+        st.error("Please enter a research topic first!")
+    
+    # Example topics
+    st.markdown("---")
+    st.markdown("### 💡 Example Topics:")
+    example_topics = [
+        "Latest AI tools for teachers",
+        "Sustainable energy solutions 2024", 
+        "Remote work productivity strategies",
+        "Cybersecurity trends for small businesses",
+        "Digital marketing for startups"
+    ]
+    
+    cols = st.columns(len(example_topics))
+    for i, example in enumerate(example_topics):
+        with cols[i]:
+            if st.button(f"📝 {example}", key=f"example_{i}"):
+                # Store the example topic in session state and rerun
+                st.session_state.example_topic = example
+                st.rerun()
+    
+    # Handle example topic selection
+    if 'example_topic' in st.session_state:
+        st.info(f"Example topic selected: {st.session_state.example_topic}")
+        if st.button("Use this example topic"):
+            # Process the example topic
+            results = process_research_query(agent, st.session_state.example_topic)
+            if results:
+                display_results(results)
+            # Clear the session state
+            del st.session_state.example_topic
+
+# Footer
+st.markdown("---")
+st.markdown(
+    "Built with ❤️ using [Streamlit](https://streamlit.io) and [LangChain](https://langchain.com) | "
+    "Powered by [Groq](https://groq.com)"
+)
+
+if __name__ == "__main__":
+    main()