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TechMatrix_AI_Web_Search_Agent

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Update app.py

3cc8504 verified over 1 year ago

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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()

# Model rate limits information
MODEL_LIMITS = {
    "allam-2-7b": {
        "rpm": 30,
        "rpd": 7000,
        "tpm": 6000,
        "tpd": "No limit"
    },
    "deepseek-r1-distill-llama-70b": {
        "rpm": 30,
        "rpd": 1000,
        "tpm": 6000,
        "tpd": "No limit"
    },
    "deepseek-r1-distill-qwen-32b": {
        "rpm": 30,
        "rpd": 1000,
        "tpm": 6000,
        "tpd": "No limit"
    },
    "gemma2-9b-it": {
        "rpm": 30,
        "rpd": 14400,
        "tpm": 15000,
        "tpd": 500000
    },
    "llama-3.1-8b-instant": {
        "rpm": 30,
        "rpd": 14400,
        "tpm": 6000,
        "tpd": 500000
    },
    "llama-3.2-11b-vision-preview": {
        "rpm": 30,
        "rpd": 7000,
        "tpm": 7000,
        "tpd": 500000
    },
    "llama-3.2-1b-preview": {
        "rpm": 30,
        "rpd": 7000,
        "tpm": 7000,
        "tpd": 500000
    },
    "llama-3.2-3b-preview": {
        "rpm": 30,
        "rpd": 7000,
        "tpm": 7000,
        "tpd": 500000
    },
    "llama-3.2-90b-vision-preview": {
        "rpm": 15,
        "rpd": 3500,
        "tpm": 7000,
        "tpd": 250000
    },
    "llama-3.3-70b-specdec": {
        "rpm": 30,
        "rpd": 1000,
        "tpm": 6000,
        "tpd": 100000
    },
    "llama-3.3-70b-versatile": {
        "rpm": 30,
        "rpd": 1000,
        "tpm": 6000,
        "tpd": 100000
    },
    "llama-guard-3-8b": {
        "rpm": 30,
        "rpd": 14400,
        "tpm": 15000,
        "tpd": 500000
    },
    "llama3-70b-8192": {
        "rpm": 30,
        "rpd": 14400,
        "tpm": 6000,
        "tpd": 500000
    },
    "llama3-8b-8192": {
        "rpm": 30,
        "rpd": 14400,
        "tpm": 6000,
        "tpd": 500000
    },
    "mistral-saba-24b": {
        "rpm": 30,
        "rpd": 1000,
        "tpm": 6000,
        "tpd": 500000
    },
    "qwen-2.5-32b": {
        "rpm": 30,
        "rpd": 1000,
        "tpm": 6000,
        "tpd": "No limit"
    },
    "qwen-2.5-coder-32b": {
        "rpm": 30,
        "rpd": 1000,
        "tpm": 6000,
        "tpd": "No limit"
    },
    "qwen-qwq-32b": {
        "rpm": 30,
        "rpd": 1000,
        "tpm": 6000,
        "tpd": "No limit"
    },
    "claude-3-5-sonnet-20240620": {
        "rpm": 30,
        "rpd": 14400,
        "tpm": 15000,
        "tpd": 500000
    },
    "mixtral-8x7b-32768": {
        "rpm": 30,
        "rpd": 14400,
        "tpm": 15000,
        "tpd": 500000
    }
}

# 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;
    }
    .model-limits-container {
        background-color: #E8F5E9;
        padding: 1rem;
        border-radius: 5px;
        margin-top: 0.5rem;
        margin-bottom: 1rem;
    }
    .limit-pill {
        display: inline-block;
        margin: 0.2rem;
        padding: 0.3rem 0.6rem;
        background-color: #C8E6C9;
        color: #2E7D32;
        border-radius: 20px;
        font-size: 0.8rem;
    }
    .limit-table {
        width: 100%;
        border-collapse: collapse;
        margin-top: 0.5rem;
        font-size: 0.9rem;
    }
    .limit-table th, .limit-table td {
        padding: 0.4rem;
        text-align: left;
        border-bottom: 1px solid #E0E0E0;
    }
    .limit-table th {
        background-color: #E8F5E9;
        color: #2E7D32;
        font-weight: bold;
    }
</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)
    
    # Available models
    available_models = [
        'gemma2-9b-it', 
        'llama3-8b-8192', 
        'mixtral-8x7b-32768', 
        'llama3-70b-8192', 
        'claude-3-5-sonnet-20240620',
        'llama-3.1-8b-instant',
        'llama-3.2-3b-preview',
        'llama-3.3-70b-versatile',
        'qwen-2.5-32b',
        'mistral-saba-24b'
    ]
    
    model_option = st.selectbox(
        'LLM Model',
        available_models,
        index=0,
        help="Select from available Groq models"
    )
    
    # Display the rate limits for the selected model
    if model_option in MODEL_LIMITS:
        limits = MODEL_LIMITS[model_option]
        st.markdown('<div class="model-limits-container">', unsafe_allow_html=True)
        st.markdown(f"#### Rate Limits for {model_option}")
        
        # Create a table for the limits
        st.markdown("""
        <table class="limit-table">
            <tr>
                <th>Limit Type</th>
                <th>Value</th>
            </tr>
            <tr>
                <td>Requests per Minute</td>
                <td>{rpm}</td>
            </tr>
            <tr>
                <td>Requests per Day</td>
                <td>{rpd}</td>
            </tr>
            <tr>
                <td>Tokens per Minute</td>
                <td>{tpm}</td>
            </tr>
            <tr>
                <td>Tokens per Day</td>
                <td>{tpd}</td>
            </tr>
        </table>
        """.format(
            rpm=limits['rpm'],
            rpd=limits['rpd'],
            tpm=limits['tpm'],
            tpd=limits['tpd']
        ), unsafe_allow_html=True)
        
        st.markdown('</div>', unsafe_allow_html=True)
    
    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)

	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()

	# Model rate limits information
	MODEL_LIMITS = {
	"allam-2-7b": {
	"rpm": 30,
	"rpd": 7000,
	"tpm": 6000,
	"tpd": "No limit"
	},
	"deepseek-r1-distill-llama-70b": {
	"rpm": 30,
	"rpd": 1000,
	"tpm": 6000,
	"tpd": "No limit"
	},
	"deepseek-r1-distill-qwen-32b": {
	"rpm": 30,
	"rpd": 1000,
	"tpm": 6000,
	"tpd": "No limit"
	},
	"gemma2-9b-it": {
	"rpm": 30,
	"rpd": 14400,
	"tpm": 15000,
	"tpd": 500000
	},
	"llama-3.1-8b-instant": {
	"rpm": 30,
	"rpd": 14400,
	"tpm": 6000,
	"tpd": 500000
	},
	"llama-3.2-11b-vision-preview": {
	"rpm": 30,
	"rpd": 7000,
	"tpm": 7000,
	"tpd": 500000
	},
	"llama-3.2-1b-preview": {
	"rpm": 30,
	"rpd": 7000,
	"tpm": 7000,
	"tpd": 500000
	},
	"llama-3.2-3b-preview": {
	"rpm": 30,
	"rpd": 7000,
	"tpm": 7000,
	"tpd": 500000
	},
	"llama-3.2-90b-vision-preview": {
	"rpm": 15,
	"rpd": 3500,
	"tpm": 7000,
	"tpd": 250000
	},
	"llama-3.3-70b-specdec": {
	"rpm": 30,
	"rpd": 1000,
	"tpm": 6000,
	"tpd": 100000
	},
	"llama-3.3-70b-versatile": {
	"rpm": 30,
	"rpd": 1000,
	"tpm": 6000,
	"tpd": 100000
	},
	"llama-guard-3-8b": {
	"rpm": 30,
	"rpd": 14400,
	"tpm": 15000,
	"tpd": 500000
	},
	"llama3-70b-8192": {
	"rpm": 30,
	"rpd": 14400,
	"tpm": 6000,
	"tpd": 500000
	},
	"llama3-8b-8192": {
	"rpm": 30,
	"rpd": 14400,
	"tpm": 6000,
	"tpd": 500000
	},
	"mistral-saba-24b": {
	"rpm": 30,
	"rpd": 1000,
	"tpm": 6000,
	"tpd": 500000
	},
	"qwen-2.5-32b": {
	"rpm": 30,
	"rpd": 1000,
	"tpm": 6000,
	"tpd": "No limit"
	},
	"qwen-2.5-coder-32b": {
	"rpm": 30,
	"rpd": 1000,
	"tpm": 6000,
	"tpd": "No limit"
	},
	"qwen-qwq-32b": {
	"rpm": 30,
	"rpd": 1000,
	"tpm": 6000,
	"tpd": "No limit"
	},
	"claude-3-5-sonnet-20240620": {
	"rpm": 30,
	"rpd": 14400,
	"tpm": 15000,
	"tpd": 500000
	},
	"mixtral-8x7b-32768": {
	"rpm": 30,
	"rpd": 14400,
	"tpm": 15000,
	"tpd": 500000
	}
	}

	# 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;
	}
	.model-limits-container {
	background-color: #E8F5E9;
	padding: 1rem;
	border-radius: 5px;
	margin-top: 0.5rem;
	margin-bottom: 1rem;
	}
	.limit-pill {
	display: inline-block;
	margin: 0.2rem;
	padding: 0.3rem 0.6rem;
	background-color: #C8E6C9;
	color: #2E7D32;
	border-radius: 20px;
	font-size: 0.8rem;
	}
	.limit-table {
	width: 100%;
	border-collapse: collapse;
	margin-top: 0.5rem;
	font-size: 0.9rem;
	}
	.limit-table th, .limit-table td {
	padding: 0.4rem;
	text-align: left;
	border-bottom: 1px solid #E0E0E0;
	}
	.limit-table th {
	background-color: #E8F5E9;
	color: #2E7D32;
	font-weight: bold;
	}
	</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)

	# Available models
	available_models = [
	'gemma2-9b-it',
	'llama3-8b-8192',
	'mixtral-8x7b-32768',
	'llama3-70b-8192',
	'claude-3-5-sonnet-20240620',
	'llama-3.1-8b-instant',
	'llama-3.2-3b-preview',
	'llama-3.3-70b-versatile',
	'qwen-2.5-32b',
	'mistral-saba-24b'
	]

	model_option = st.selectbox(
	'LLM Model',
	available_models,
	index=0,
	help="Select from available Groq models"
	)

	# Display the rate limits for the selected model
	if model_option in MODEL_LIMITS:
	limits = MODEL_LIMITS[model_option]
	st.markdown('<div class="model-limits-container">', unsafe_allow_html=True)
	st.markdown(f"#### Rate Limits for {model_option}")

	# Create a table for the limits
	st.markdown("""
	<table class="limit-table">
	<tr>
	<th>Limit Type</th>
	<th>Value</th>
	</tr>
	<tr>
	<td>Requests per Minute</td>
	<td>{rpm}</td>
	</tr>
	<tr>
	<td>Requests per Day</td>
	<td>{rpd}</td>
	</tr>
	<tr>
	<td>Tokens per Minute</td>
	<td>{tpm}</td>
	</tr>
	<tr>
	<td>Tokens per Day</td>
	<td>{tpd}</td>
	</tr>
	</table>
	""".format(
	rpm=limits['rpm'],
	rpd=limits['rpd'],
	tpm=limits['tpm'],
	tpd=limits['tpd']
	), unsafe_allow_html=True)

	st.markdown('</div>', unsafe_allow_html=True)

	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)