Update app.py

app.py

@@ -1,456 +1,563 @@
-import streamlit as st
-from llama_index.core.agent import ReActAgent
-from llama_index.llms.groq import Groq
-from llama_index.core.tools import FunctionTool
-from llama_index.tools.tavily_research.base import TavilyToolSpec
-import os
-import json
-import pandas as pd
-from datetime import datetime
-from dotenv import load_dotenv
-import time
-import base64
-import plotly.graph_objects as go
-import re
-
-# Load environment variables
-load_dotenv()
-
-# Initialize session state if not already done
-if 'conversation_history' not in st.session_state:
-    st.session_state.conversation_history = []
-if 'api_key' not in st.session_state:
-    st.session_state.api_key = ""
-if 'current_response' not in st.session_state:
-    st.session_state.current_response = None
-if 'feedback_data' not in st.session_state:
-    st.session_state.feedback_data = []
-if 'current_sources' not in st.session_state:
-    st.session_state.current_sources = []
-
-# Custom CSS for better UI
-st.markdown("""
-<style>
-    .main-header {
-        font-size: 2.5rem;
-        color: #4527A0;
-        text-align: center;
-        margin-bottom: 1rem;
-        font-weight: bold;
-    }
-    .sub-header {
-        font-size: 1.5rem;
-        color: #5E35B1;
-        margin-bottom: 0.5rem;
-    }
-    .team-header {
-        font-size: 1.2rem;
-        color: #673AB7;
-        font-weight: bold;
-        margin-top: 1rem;
-    }
-    .team-member {
-        font-size: 1rem;
-        margin-left: 1rem;
-        color: #7E57C2;
-    }
-    .api-section {
-        background-color: #EDE7F6;
-        padding: 1rem;
-        border-radius: 10px;
-        margin-bottom: 1rem;
-    }
-    .response-container {
-        background-color: #F3E5F5;
-        padding: 1rem;
-        border-radius: 5px;
-        margin-top: 1rem;
-    }
-    .footer {
-        text-align: center;
-        margin-top: 2rem;
-        font-size: 0.8rem;
-        color: #9575CD;
-    }
-    .error-msg {
-        color: #D32F2F;
-        font-weight: bold;
-    }
-    .success-msg {
-        color: #388E3C;
-        font-weight: bold;
-    }
-    .history-item {
-        padding: 0.5rem;
-        border-radius: 5px;
-        margin-bottom: 0.5rem;
-    }
-    .query-text {
-        font-weight: bold;
-        color: #303F9F;
-    }
-    .response-text {
-        color: #1A237E;
-    }
-    .feedback-container {
-        background-color: #E8EAF6;
-        padding: 1rem;
-        border-radius: 5px;
-        margin-top: 1rem;
-    }
-    .feedback-btn {
-        margin-right: 0.5rem;
-    }
-    .star-rating {
-        display: flex;
-        justify-content: center;
-        margin-top: 0.5rem;
-    }
-    .analytics-container {
-        background-color: #E1F5FE;
-        padding: 1rem;
-        border-radius: 5px;
-        margin-top: 1rem;
-    }
-    .sources-container {
-        background-color: #E0F7FA;
-        padding: 1rem;
-        border-radius: 5px;
-        margin-top: 1rem;
-    }
-    .source-item {
-        background-color: #B2EBF2;
-        padding: 0.5rem;
-        border-radius: 5px;
-        margin-bottom: 0.5rem;
-    }
-    .source-url {
-        font-style: italic;
-        color: #0277BD;
-        word-break: break-all;
-    }
-</style>
-""", unsafe_allow_html=True)
-
-# Main title and description
-st.markdown('<div class="main-header">TechMatrix AI Web Search Agent</div>', unsafe_allow_html=True)
-st.markdown('''
-This intelligent agent uses state-of-the-art LLM technology to search the web and provide comprehensive answers to your questions.
-Simply enter your query, and let our AI handle the rest!
-''')
-
-# Sidebar for team information
-with st.sidebar:
-    st.markdown('<div class="team-header">TechMatrix Solvers</div>', unsafe_allow_html=True)
-    
-    st.markdown('<div class="team-member">👑 Abhay Gupta (Team Leader)</div>', unsafe_allow_html=True)
-    st.markdown('[LinkedIn Profile](https://www.linkedin.com/in/abhay-gupta-197b17264/)')
-    
-    st.markdown('<div class="team-member">🧠 Mayank Das Bairagi</div>', unsafe_allow_html=True)
-    st.markdown('[LinkedIn Profile](https://www.linkedin.com/in/mayank-das-bairagi-18639525a/)')
-    
-    st.markdown('<div class="team-member">💻 Kripanshu Gupta</div>', unsafe_allow_html=True)
-    st.markdown('[LinkedIn Profile](https://www.linkedin.com/in/kripanshu-gupta-a66349261/)')
-    
-    st.markdown('<div class="team-member">🔍 Bhumika Patel</div>', unsafe_allow_html=True)
-    st.markdown('[LinkedIn Profile](https://www.linkedin.com/in/bhumika-patel-ml/)')
-    
-    st.markdown('---')
-    
-    # Advanced Settings
-    st.markdown('<div class="sub-header">Advanced Settings</div>', unsafe_allow_html=True)
-    model_option = st.selectbox(
-        'LLM Model',
-        ('gemma2-9b-it', 'llama3-8b-8192', 'mixtral-8x7b-32768'),
-        index=0
-    )
-    
-    search_depth = st.slider('Search Depth', min_value=1, max_value=5, value=3, 
-                            help="Higher values will search more thoroughly but take longer")
-    
-    # Clear history button
-    if st.button('Clear Conversation History'):
-        st.session_state.conversation_history = []
-        st.success('Conversation history cleared!')
-    
-    # Analytics section in sidebar
-    if st.session_state.feedback_data:
-        st.markdown('---')
-        st.markdown('<div class="sub-header">Response Analytics</div>', unsafe_allow_html=True)
-        
-        # Calculate average rating
-        ratings = [item['rating'] for item in st.session_state.feedback_data if 'rating' in item]
-        avg_rating = sum(ratings) / len(ratings) if ratings else 0
-        
-        # Create a chart
-        fig = go.Figure(go.Indicator(
-            mode="gauge+number",
-            value=avg_rating,
-            title={'text': "Average Rating"},
-            domain={'x': [0, 1], 'y': [0, 1]},
-            gauge={
-                'axis': {'range': [0, 5]},
-                'bar': {'color': "#6200EA"},
-                'steps': [
-                    {'range': [0, 2], 'color': "#FFD0D0"},
-                    {'range': [2, 3.5], 'color': "#FFFFCC"},
-                    {'range': [3.5, 5], 'color': "#D0FFD0"}
-                ]
-            }
-        ))
-        
-        fig.update_layout(height=250, margin=dict(l=20, r=20, t=30, b=20))
-        st.plotly_chart(fig, use_container_width=True)
-        
-        # Show feedback counts
-        feedback_counts = {"👍 Helpful": 0, "👎 Not Helpful": 0}
-        for item in st.session_state.feedback_data:
-            if 'feedback' in item:
-                if item['feedback'] == 'helpful':
-                    feedback_counts["👍 Helpful"] += 1
-                elif item['feedback'] == 'not_helpful':
-                    feedback_counts["👎 Not Helpful"] += 1
-        
-        st.markdown("### Feedback Summary")
-        for key, value in feedback_counts.items():
-            st.markdown(f"**{key}:** {value}")
-
-# API key input section
-st.markdown('<div class="sub-header">API Credentials</div>', unsafe_allow_html=True)
-with st.expander("Configure API Keys"):
-    st.markdown('<div class="api-section">', unsafe_allow_html=True)
-    api_key = st.text_input("Enter your Groq API key:", 
-                          type="password", 
-                          value=st.session_state.api_key,
-                          help="Get your API key from https://console.groq.com/keys")
-    
-    tavily_key = st.text_input("Enter your Tavily API key (optional):", 
-                             type="password",
-                             help="Get your Tavily API key from https://tavily.com/#api")
-    
-    if api_key:
-        st.session_state.api_key = api_key
-        os.environ['GROQ_API_KEY'] = api_key
-    
-    if tavily_key:
-        os.environ['TAVILY_API_KEY'] = tavily_key
-    st.markdown('</div>', unsafe_allow_html=True)
-
-# Function to create download link for text data
-def get_download_link(text, filename, link_text):
-    b64 = base64.b64encode(text.encode()).decode()
-    href = f'<a href="data:file/txt;base64,{b64}" download="{filename}">{link_text}</a>'
-    return href
-
-# Function to handle feedback submission
-def submit_feedback(feedback_type, query, response):
-    feedback_entry = {
-        "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
-        "query": query,
-        "response": response,
-        "feedback": feedback_type
-    }
-    st.session_state.feedback_data.append(feedback_entry)
-    return True
-
-# Function to submit rating
-def submit_rating(rating, query, response):
-    # Find if there's an existing entry for this query/response
-    for entry in st.session_state.feedback_data:
-        if entry.get('query') == query and entry.get('response') == response:
-            entry['rating'] = rating
-            return True
-    
-    # If not found, create a new entry
-    feedback_entry = {
-        "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
-        "query": query,
-        "response": response,
-        "rating": rating
-    }
-    st.session_state.feedback_data.append(feedback_entry)
-    return True
-
-# Function to extract URLs from text
-def extract_urls(text):
-    url_pattern = r'https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+'
-    return re.findall(url_pattern, text)
-
-# Setup search tools
-try:
-    if 'TAVILY_API_KEY' in os.environ and os.environ['TAVILY_API_KEY']:
-        search = TavilyToolSpec(api_key=os.environ['TAVILY_API_KEY'])
-    else:
-        # Fallback to a default key or inform the user
-        st.warning("Using default Tavily API key with limited quota. For better results, please provide your own key.")
-        search = TavilyToolSpec(api_key=os.getenv('TAVILY_API_KEY'))
-
-    def search_tool(prompt: str) -> list:
-        """Search the web for information about the given prompt."""
-        try:
-            search_results = search.search(prompt, max_results=search_depth)
-            # Store source URLs
-            sources = []
-            for result in search_results:
-                if hasattr(result, 'url') and result.url:
-                    sources.append({
-                        'title': result.title if hasattr(result, 'title') else "Unknown Source",
-                        'url': result.url
-                    })
-            
-            # Store in session state for later display
-            st.session_state.current_sources = sources
-            
-            return [result.text for result in search_results]
-        except Exception as e:
-            return [f"Error during search: {str(e)}"]
-
-    search_toolkit = FunctionTool.from_defaults(fn=search_tool)
-except Exception as e:
-    st.error(f"Error setting up search tools: {str(e)}")
-    search_toolkit = None
-
-# Query input
-query = st.text_input("What would you like to know?", 
-                     placeholder="Enter your question here...",
-                     help="Ask any question, and our AI will search the web for answers")
-
-# Search button
-search_button = st.button("🔍 Search")
-
-# Process the search when button is clicked
-if search_button and query:
-    # Check if API key is provided
-    if not st.session_state.api_key:
-        st.error("Please enter your Groq API key first!")
-    else:
-        try:
-            with st.spinner("🧠 Searching the web and analyzing results..."):
-                # Initialize the LLM and agent
-                llm = Groq(model=model_option)
-                agent = ReActAgent.from_tools([search_toolkit], llm=llm, verbose=True)
-                
-                # Clear current sources before the new search
-                st.session_state.current_sources = []
-                
-                # Get the response
-                start_time = time.time()
-                response = agent.chat(query)
-                end_time = time.time()
-                
-                # Extract any additional URLs from the response
-                additional_urls = extract_urls(response.response)
-                for url in additional_urls:
-                    if not any(source['url'] == url for source in st.session_state.current_sources):
-                        st.session_state.current_sources.append({
-                            'title': "Referenced Source",
-                            'url': url
-                        })
-                
-                # Store the response in session state
-                st.session_state.current_response = {
-                    "query": query,
-                    "response": response.response,
-                    "time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
-                    "duration": round(end_time - start_time, 2),
-                    "sources": st.session_state.current_sources
-                }
-                
-                # Add to conversation history
-                st.session_state.conversation_history.append(st.session_state.current_response)
-                
-                # Display success message
-                st.success(f"Found results in {round(end_time - start_time, 2)} seconds!")
-        except Exception as e:
-            st.error(f"An error occurred: {str(e)}")
-
-# Display current response if available
-if st.session_state.current_response:
-    with st.container():
-        st.markdown('<div class="response-container">', unsafe_allow_html=True)
-        st.markdown("### Response:")
-        st.write(st.session_state.current_response["response"])
-        
-        # Export options
-        col1, col2 = st.columns(2)
-        with col1:
-            st.markdown(
-                get_download_link(
-                    st.session_state.current_response["response"], 
-                    f"search_result_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt",
-                    "Download as Text"
-                ),
-                unsafe_allow_html=True
-            )
-        with col2:
-            # Create JSON with metadata
-            json_data = json.dumps({
-                "query": st.session_state.current_response["query"],
-                "response": st.session_state.current_response["response"],
-                "timestamp": st.session_state.current_response["time"],
-                "processing_time": st.session_state.current_response["duration"],
-                "sources": st.session_state.current_sources if "sources" in st.session_state.current_response else []
-            }, indent=4)
-            
-            st.markdown(
-                get_download_link(
-                    json_data,
-                    f"search_result_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
-                    "Download as JSON"
-                ),
-                unsafe_allow_html=True
-            )
-        st.markdown('</div>', unsafe_allow_html=True)
-        
-        # Display sources if available
-        if "sources" in st.session_state.current_response and st.session_state.current_response["sources"]:
-            with st.expander("View Sources", expanded=True):
-                st.markdown('<div class="sources-container">', unsafe_allow_html=True)
-                for i, source in enumerate(st.session_state.current_response["sources"]):
-                    st.markdown(f'<div class="source-item">', unsafe_allow_html=True)
-                    st.markdown(f"**Source {i+1}:** {source.get('title', 'Unknown Source')}")
-                    st.markdown(f'<div class="source-url"><a href="{source["url"]}" target="_blank">{source["url"]}</a></div>', unsafe_allow_html=True)
-                    st.markdown('</div>', unsafe_allow_html=True)
-                st.markdown('</div>', unsafe_allow_html=True)
-        
-        # Feedback section
-        st.markdown('<div class="feedback-container">', unsafe_allow_html=True)
-        st.markdown("### Was this response helpful?")
-        
-        col1, col2 = st.columns(2)
-        with col1:
-            if st.button("👍 Helpful", key="helpful_btn"):
-                if submit_feedback("helpful", st.session_state.current_response["query"], st.session_state.current_response["response"]):
-                    st.success("Thank you for your feedback!")
-        with col2:
-            if st.button("👎 Not Helpful", key="not_helpful_btn"):
-                if submit_feedback("not_helpful", st.session_state.current_response["query"], st.session_state.current_response["response"]):
-                    st.success("Thank you for your feedback! We'll work to improve our responses.")
-        
-        st.markdown("### Rate this response:")
-        rating = st.slider("", min_value=1, max_value=5, value=4, 
-                          help="Rate the quality of this response from 1 (poor) to 5 (excellent)")
-        
-        if st.button("Submit Rating"):
-            if submit_rating(rating, st.session_state.current_response["query"], st.session_state.current_response["response"]):
-                st.success("Rating submitted! Thank you for helping us improve.")
-        
-        st.markdown('</div>', unsafe_allow_html=True)
-
-# Display conversation history
-if st.session_state.conversation_history:
-    with st.expander("View Conversation History"):
-        for i, item in enumerate(reversed(st.session_state.conversation_history)):
-            st.markdown(f'<div class="history-item">', unsafe_allow_html=True)
-            st.markdown(f'<span class="query-text">Q: {item["query"]}</span> <small>({item["time"]})</small>', unsafe_allow_html=True)
-            st.markdown(f'<div class="response-text">A: {item["response"][:200]}{"..." if len(item["response"]) > 200 else ""}</div>', unsafe_allow_html=True)
-            st.markdown('</div>', unsafe_allow_html=True)
-            if i < len(st.session_state.conversation_history) - 1:
-                st.markdown('---')
-
-# Footer with attribution
-st.markdown('''
-<div class="footer">
-    <p>Powered by Groq + Llama-Index + Tavily Search | Created by TechMatrix Solvers | 2024</p>
-</div>
+import streamlit as st
+from llama_index.core.agent import ReActAgent
+from llama_index.llms.groq import Groq
+from llama_index.core.tools import FunctionTool
+from llama_index.tools.tavily_research.base import TavilyToolSpec
+import os
+import json
+import pandas as pd
+from datetime import datetime
+from dotenv import load_dotenv
+import time
+import base64
+import plotly.graph_objects as go
+import re
+from io import StringIO
+import sys
+
+# Load environment variables
+load_dotenv()
+
+# Initialize session state if not already done
+if 'conversation_history' not in st.session_state:
+    st.session_state.conversation_history = []
+if 'api_key' not in st.session_state:
+    st.session_state.api_key = ""
+if 'current_response' not in st.session_state:
+    st.session_state.current_response = None
+if 'feedback_data' not in st.session_state:
+    st.session_state.feedback_data = []
+if 'current_sources' not in st.session_state:
+    st.session_state.current_sources = []
+if 'thinking_process' not in st.session_state:
+    st.session_state.thinking_process = ""
+
+# Custom CSS for better UI
+st.markdown("""
+<style>
+    .main-header {
+        font-size: 2.5rem;
+        color: #4527A0;
+        text-align: center;
+        margin-bottom: 1rem;
+        font-weight: bold;
+    }
+    .sub-header {
+        font-size: 1.5rem;
+        color: #5E35B1;
+        margin-bottom: 0.5rem;
+    }
+    .team-header {
+        font-size: 1.2rem;
+        color: #673AB7;
+        font-weight: bold;
+        margin-top: 1rem;
+    }
+    .team-member {
+        font-size: 1rem;
+        margin-left: 1rem;
+        color: #7E57C2;
+    }
+    .api-section {
+        background-color: #EDE7F6;
+        padding: 1rem;
+        border-radius: 10px;
+        margin-bottom: 1rem;
+    }
+    .response-container {
+        background-color: #F3E5F5;
+        padding: 1rem;
+        border-radius: 5px;
+        margin-top: 1rem;
+    }
+    .footer {
+        text-align: center;
+        margin-top: 2rem;
+        font-size: 0.8rem;
+        color: #9575CD;
+    }
+    .error-msg {
+        color: #D32F2F;
+        font-weight: bold;
+    }
+    .success-msg {
+        color: #388E3C;
+        font-weight: bold;
+    }
+    .history-item {
+        padding: 0.5rem;
+        border-radius: 5px;
+        margin-bottom: 0.5rem;
+    }
+    .query-text {
+        font-weight: bold;
+        color: #303F9F;
+    }
+    .response-text {
+        color: #1A237E;
+    }
+    .feedback-container {
+        background-color: #E8EAF6;
+        padding: 1rem;
+        border-radius: 5px;
+        margin-top: 1rem;
+    }
+    .feedback-btn {
+        margin-right: 0.5rem;
+    }
+    .star-rating {
+        display: flex;
+        justify-content: center;
+        margin-top: 0.5rem;
+    }
+    .analytics-container {
+        background-color: #E1F5FE;
+        padding: 1rem;
+        border-radius: 5px;
+        margin-top: 1rem;
+    }
+    .sources-container {
+        background-color: #E0F7FA;
+        padding: 1rem;
+        border-radius: 5px;
+        margin-top: 1rem;
+    }
+    .source-item {
+        background-color: #B2EBF2;
+        padding: 0.5rem;
+        border-radius: 5px;
+        margin-bottom: 0.5rem;
+    }
+    .source-url {
+        font-style: italic;
+        color: #0277BD;
+        word-break: break-all;
+    }
+    .thinking-container {
+        background-color: #FFF8E1;
+        padding: 1rem;
+        border-radius: 5px;
+        margin-top: 1rem;
+        font-family: monospace;
+        white-space: pre-wrap;
+    }
+    .thinking-step {
+        padding: 0.5rem;
+        margin-bottom: 0.5rem;
+        border-left: 3px solid #FFB300;
+    }
+    .website-link {
+        display: inline-block;
+        margin: 0.3rem;
+        padding: 0.4rem 0.8rem;
+        background-color: #E3F2FD;
+        color: #1565C0;
+        border-radius: 20px;
+        font-size: 0.9rem;
+        text-decoration: none;
+        transition: background-color 0.3s;
+    }
+    .website-link:hover {
+        background-color: #BBDEFB;
+    }
+    .link-container {
+        margin: 1rem 0;
+        padding: 0.5rem;
+        background-color: #F5F5F5;
+        border-radius: 5px;
+        display: flex;
+        flex-wrap: wrap;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Main title and description
+st.markdown('<div class="main-header">TechMatrix AI Web Search Agent</div>', unsafe_allow_html=True)
+st.markdown('''
+This intelligent agent uses state-of-the-art LLM technology to search the web and provide comprehensive answers to your questions.
+Simply enter your query, and let our AI handle the rest!
+''')
+
+# Sidebar for team information
+with st.sidebar:
+    st.markdown('<div class="team-header">TechMatrix Solvers</div>', unsafe_allow_html=True)
+    
+    st.markdown('<div class="team-member">👑 Abhay Gupta (Team Leader)</div>', unsafe_allow_html=True)
+    st.markdown('[LinkedIn Profile](https://www.linkedin.com/in/abhay-gupta-197b17264/)')
+    
+    st.markdown('<div class="team-member">🧠 Mayank Das Bairagi</div>', unsafe_allow_html=True)
+    st.markdown('[LinkedIn Profile](https://www.linkedin.com/in/mayank-das-bairagi-18639525a/)')
+    
+    st.markdown('<div class="team-member">💻 Kripanshu Gupta</div>', unsafe_allow_html=True)
+    st.markdown('[LinkedIn Profile](https://www.linkedin.com/in/kripanshu-gupta-a66349261/)')
+    
+    st.markdown('<div class="team-member">🔍 Bhumika Patel</div>', unsafe_allow_html=True)
+    st.markdown('[LinkedIn Profile](https://www.linkedin.com/in/bhumika-patel-ml/)')
+    
+    st.markdown('---')
+    
+    # Advanced Settings
+    st.markdown('<div class="sub-header">Advanced Settings</div>', unsafe_allow_html=True)
+    model_option = st.selectbox(
+        'LLM Model',
+        ('gemma2-9b-it', 'llama3-8b-8192', 'mixtral-8x7b-32768', 'llama3-70b-8192', 'claude-3-5-sonnet-20240620'),
+        index=0,
+        help="Select from available Groq models - all free with no rate limits!"
+    )
+    
+    search_depth = st.slider('Search Depth', min_value=1, max_value=8, value=5, 
+                            help="Higher values will search more thoroughly but take longer")
+    
+    show_thinking = st.checkbox('Show AI Thinking Process', value=True,
+                               help="Display the step-by-step reasoning process of the AI")
+    
+    # Clear history button
+    if st.button('Clear Conversation History'):
+        st.session_state.conversation_history = []
+        st.success('Conversation history cleared!')
+    
+    # Analytics section in sidebar
+    if st.session_state.feedback_data:
+        st.markdown('---')
+        st.markdown('<div class="sub-header">Response Analytics</div>', unsafe_allow_html=True)
+        
+        # Calculate average rating
+        ratings = [item['rating'] for item in st.session_state.feedback_data if 'rating' in item]
+        avg_rating = sum(ratings) / len(ratings) if ratings else 0
+        
+        # Create a chart
+        fig = go.Figure(go.Indicator(
+            mode="gauge+number",
+            value=avg_rating,
+            title={'text': "Average Rating"},
+            domain={'x': [0, 1], 'y': [0, 1]},
+            gauge={
+                'axis': {'range': [0, 5]},
+                'bar': {'color': "#6200EA"},
+                'steps': [
+                    {'range': [0, 2], 'color': "#FFD0D0"},
+                    {'range': [2, 3.5], 'color': "#FFFFCC"},
+                    {'range': [3.5, 5], 'color': "#D0FFD0"}
+                ]
+            }
+        ))
+        
+        fig.update_layout(height=250, margin=dict(l=20, r=20, t=30, b=20))
+        st.plotly_chart(fig, use_container_width=True)
+        
+        # Show feedback counts
+        feedback_counts = {"👍 Helpful": 0, "👎 Not Helpful": 0}
+        for item in st.session_state.feedback_data:
+            if 'feedback' in item:
+                if item['feedback'] == 'helpful':
+                    feedback_counts["👍 Helpful"] += 1
+                elif item['feedback'] == 'not_helpful':
+                    feedback_counts["👎 Not Helpful"] += 1
+        
+        st.markdown("### Feedback Summary")
+        for key, value in feedback_counts.items():
+            st.markdown(f"**{key}:** {value}")
+
+# API key input section
+st.markdown('<div class="sub-header">API Credentials</div>', unsafe_allow_html=True)
+with st.expander("Configure API Keys"):
+    st.markdown('<div class="api-section">', unsafe_allow_html=True)
+    api_key = st.text_input("Enter your Groq API key:", 
+                          type="password", 
+                          value=st.session_state.api_key,
+                          help="Get your API key from https://console.groq.com/keys")
+    
+    tavily_key = st.text_input("Enter your Tavily API key (optional):", 
+                             type="password",
+                             help="Get your Tavily API key from https://tavily.com/#api")
+    
+    if api_key:
+        st.session_state.api_key = api_key
+        os.environ['GROQ_API_KEY'] = api_key
+    
+    if tavily_key:
+        os.environ['TAVILY_API_KEY'] = tavily_key
+    st.markdown('</div>', unsafe_allow_html=True)
+
+# Function to create download link for text data
+def get_download_link(text, filename, link_text):
+    b64 = base64.b64encode(text.encode()).decode()
+    href = f'<a href="data:file/txt;base64,{b64}" download="{filename}">{link_text}</a>'
+    return href
+
+# Function to handle feedback submission
+def submit_feedback(feedback_type, query, response):
+    feedback_entry = {
+        "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        "query": query,
+        "response": response,
+        "feedback": feedback_type
+    }
+    st.session_state.feedback_data.append(feedback_entry)
+    return True
+
+# Function to submit rating
+def submit_rating(rating, query, response):
+    # Find if there's an existing entry for this query/response
+    for entry in st.session_state.feedback_data:
+        if entry.get('query') == query and entry.get('response') == response:
+            entry['rating'] = rating
+            return True
+    
+    # If not found, create a new entry
+    feedback_entry = {
+        "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        "query": query,
+        "response": response,
+        "rating": rating
+    }
+    st.session_state.feedback_data.append(feedback_entry)
+    return True
+
+# Function to extract URLs from text
+def extract_urls(text):
+    url_pattern = r'https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+'
+    return re.findall(url_pattern, text)
+
+# Custom callback to capture agent's thinking process
+class ThinkingCapture:
+    def __init__(self):
+        self.thinking_steps = []
+        
+    def on_agent_step(self, agent_step):
+        # Capture the thought process
+        if hasattr(agent_step, 'thought') and agent_step.thought:
+            self.thinking_steps.append(f"Thought: {agent_step.thought}")
+        if hasattr(agent_step, 'action') and agent_step.action:
+            self.thinking_steps.append(f"Action: {agent_step.action}")
+        if hasattr(agent_step, 'observation') and agent_step.observation:
+            self.thinking_steps.append(f"Observation: {agent_step.observation}")
+        return agent_step
+    
+    def get_thinking_process(self):
+        return "\n".join(self.thinking_steps)
+
+# Setup search tools
+try:
+    if 'TAVILY_API_KEY' in os.environ and os.environ['TAVILY_API_KEY']:
+        search = TavilyToolSpec(api_key=os.environ['TAVILY_API_KEY'])
+    else:
+        # Fallback to a default key or inform the user
+        st.warning("Using default Tavily API key with limited quota. For better results, please provide your own key.")
+        search = TavilyToolSpec(api_key=os.getenv('TAVILY_API_KEY'))
+
+    def search_tool(prompt: str) -> list:
+        """Search the web for information about the given prompt."""
+        try:
+            search_results = search.search(prompt, max_results=search_depth)
+            # Store source URLs
+            sources = []
+            for result in search_results:
+                if hasattr(result, 'url') and result.url:
+                    sources.append({
+                        'title': result.title if hasattr(result, 'title') else "Unknown Source",
+                        'url': result.url
+                    })
+            
+            # Store in session state for later display
+            st.session_state.current_sources = sources
+            
+            return [result.text for result in search_results]
+        except Exception as e:
+            return [f"Error during search: {str(e)}"]
+
+    search_toolkit = FunctionTool.from_defaults(fn=search_tool)
+except Exception as e:
+    st.error(f"Error setting up search tools: {str(e)}")
+    search_toolkit = None
+
+# Query input
+query = st.text_input("What would you like to know?", 
+                     placeholder="Enter your question here...",
+                     help="Ask any question, and our AI will search the web for answers")
+
+# Search button
+search_button = st.button("🔍 Search")
+
+# Process the search when button is clicked
+if search_button and query:
+    # Check if API key is provided
+    if not st.session_state.api_key:
+        st.error("Please enter your Groq API key first!")
+    else:
+        try:
+            with st.spinner("🧠 Searching the web and analyzing results..."):
+                # Initialize the LLM and agent
+                llm = Groq(model=model_option)
+                
+                # Initialize the thinking capture
+                thinking_capture = ThinkingCapture()
+                
+                # Create the agent with step callbacks
+                agent = ReActAgent.from_tools(
+                    [search_toolkit], 
+                    llm=llm, 
+                    verbose=True,
+                    step_callbacks=[thinking_capture.on_agent_step]
+                )
+                
+                # Clear current sources before the new search
+                st.session_state.current_sources = []
+                
+                # Get the response
+                start_time = time.time()
+                response = agent.chat(query)
+                end_time = time.time()
+                
+                # Store the thinking process
+                st.session_state.thinking_process = thinking_capture.get_thinking_process()
+                
+                # Extract any additional URLs from the response
+                additional_urls = extract_urls(response.response)
+                for url in additional_urls:
+                    if not any(source['url'] == url for source in st.session_state.current_sources):
+                        st.session_state.current_sources.append({
+                            'title': "Referenced Source",
+                            'url': url
+                        })
+                
+                # Store the response in session state
+                st.session_state.current_response = {
+                    "query": query,
+                    "response": response.response,
+                    "time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+                    "duration": round(end_time - start_time, 2),
+                    "sources": st.session_state.current_sources,
+                    "thinking": st.session_state.thinking_process
+                }
+                
+                # Add to conversation history
+                st.session_state.conversation_history.append(st.session_state.current_response)
+                
+                # Display success message
+                st.success(f"Found results in {round(end_time - start_time, 2)} seconds!")
+        except Exception as e:
+            st.error(f"An error occurred: {str(e)}")
+
+# Display quick source links if available
+if st.session_state.current_sources:
+    st.markdown("### Source Websites:")
+    st.markdown('<div class="link-container">', unsafe_allow_html=True)
+    for i, source in enumerate(st.session_state.current_sources[:5]):  # Show top 5 sources
+        st.markdown(f'<a class="website-link" href="{source["url"]}" target="_blank">📄 {source.get("title", "Source "+str(i+1))[:30]}...</a>', unsafe_allow_html=True)
+    st.markdown('</div>', unsafe_allow_html=True)
+
+# Display current response if available
+if st.session_state.current_response:
+    with st.container():
+        st.markdown('<div class="response-container">', unsafe_allow_html=True)
+        st.markdown("### Response:")
+        st.write(st.session_state.current_response["response"])
+        
+        # Export options
+        col1, col2 = st.columns(2)
+        with col1:
+            st.markdown(
+                get_download_link(
+                    st.session_state.current_response["response"], 
+                    f"search_result_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt",
+                    "Download as Text"
+                ),
+                unsafe_allow_html=True
+            )
+        with col2:
+            # Create JSON with metadata
+            json_data = json.dumps({
+                "query": st.session_state.current_response["query"],
+                "response": st.session_state.current_response["response"],
+                "timestamp": st.session_state.current_response["time"],
+                "processing_time": st.session_state.current_response["duration"],
+                "sources": st.session_state.current_sources if "sources" in st.session_state.current_response else [],
+                "thinking_process": st.session_state.thinking_process if "thinking" in st.session_state.current_response else ""
+            }, indent=4)
+            
+            st.markdown(
+                get_download_link(
+                    json_data,
+                    f"search_result_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
+                    "Download as JSON with Sources"
+                ),
+                unsafe_allow_html=True
+            )
+        st.markdown('</div>', unsafe_allow_html=True)
+        
+        # Display thinking process if enabled
+        if show_thinking and "thinking" in st.session_state.current_response:
+            with st.expander("View AI Thinking Process", expanded=True):
+                st.markdown('<div class="thinking-container">', unsafe_allow_html=True)
+                
+                # Create a formatted display of the thinking steps
+                thinking_text = st.session_state.current_response["thinking"]
+                steps = thinking_text.split('\n')
+                
+                for step in steps:
+                    if step.strip():
+                        step_type = ""
+                        if step.startswith("Thought:"):
+                            step_type = "💭"
+                        elif step.startswith("Action:"):
+                            step_type = "🔍"
+                        elif step.startswith("Observation:"):
+                            step_type = "📊"
+                        
+                        st.markdown(f'<div class="thinking-step">{step_type} {step}</div>', unsafe_allow_html=True)
+                
+                st.markdown('</div>', unsafe_allow_html=True)
+        
+        # Display sources if available
+        if "sources" in st.session_state.current_response and st.session_state.current_response["sources"]:
+            with st.expander("View Detailed Sources", expanded=True):
+                st.markdown('<div class="sources-container">', unsafe_allow_html=True)
+                for i, source in enumerate(st.session_state.current_response["sources"]):
+                    st.markdown(f'<div class="source-item">', unsafe_allow_html=True)
+                    st.markdown(f"**Source {i+1}:** {source.get('title', 'Unknown Source')}")
+                    st.markdown(f'<div class="source-url"><a href="{source["url"]}" target="_blank">{source["url"]}</a></div>', unsafe_allow_html=True)
+                    st.markdown('</div>', unsafe_allow_html=True)
+                st.markdown('</div>', unsafe_allow_html=True)
+        
+        # Feedback section
+        st.markdown('<div class="feedback-container">', unsafe_allow_html=True)
+        st.markdown("### Was this response helpful?")
+        
+        col1, col2 = st.columns(2)
+        with col1:
+            if st.button("👍 Helpful", key="helpful_btn"):
+                if submit_feedback("helpful", st.session_state.current_response["query"], st.session_state.current_response["response"]):
+                    st.success("Thank you for your feedback!")
+        with col2:
+            if st.button("👎 Not Helpful", key="not_helpful_btn"):
+                if submit_feedback("not_helpful", st.session_state.current_response["query"], st.session_state.current_response["response"]):
+                    st.success("Thank you for your feedback! We'll work to improve our responses.")
+        
+        st.markdown("### Rate this response:")
+        rating = st.slider("", min_value=1, max_value=5, value=4, 
+                          help="Rate the quality of this response from 1 (poor) to 5 (excellent)")
+        
+        if st.button("Submit Rating"):
+            if submit_rating(rating, st.session_state.current_response["query"], st.session_state.current_response["response"]):
+                st.success("Rating submitted! Thank you for helping us improve.")
+        
+        st.markdown('</div>', unsafe_allow_html=True)
+
+# Display conversation history
+if st.session_state.conversation_history:
+    with st.expander("View Conversation History"):
+        for i, item in enumerate(reversed(st.session_state.conversation_history)):
+            st.markdown(f'<div class="history-item">', unsafe_allow_html=True)
+            st.markdown(f'<span class="query-text">Q: {item["query"]}</span> <small>({item["time"]})</small>', unsafe_allow_html=True)
+            st.markdown(f'<div class="response-text">A: {item["response"][:200]}{"..." if len(item["response"]) > 200 else ""}</div>', unsafe_allow_html=True)
+            st.markdown('</div>', unsafe_allow_html=True)
+            if i < len(st.session_state.conversation_history) - 1:
+                st.markdown('---')
+
+# Footer with attribution
+st.markdown('''
+<div class="footer">
+    <p>Powered by Groq + Llama-Index + Tavily Search | Created by TechMatrix Solvers | 2025</p>
+</div>
 ''', unsafe_allow_html=True)