app.py

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>

''', unsafe_allow_html=True)