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README.md

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----
-title: TechMatrix AI Web Search Agent
-emoji: 👀
-colorFrom: blue
-colorTo: green
-sdk: streamlit
-sdk_version: 1.43.2
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: Intelligent web search and response agent
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: TechMatrix AI Web Search Agent
+emoji: 🔍
+colorFrom: indigo
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.43.2
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: Intelligent web search and response agent
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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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
+
+# 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)

notebook.ipynb

@@ -0,0 +1,111 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/soumyadip/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from llama_index.core.agent import ReActAgent\n",
+    "from llama_index.llms.groq import Groq\n",
+    "from llama_index.core.llms import ChatMessage\n",
+    "from llama_index.core.tools import BaseTool, FunctionTool\n",
+    "from llama_index.tools.tavily_research.base import TavilyToolSpec\n",
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "load_dotenv()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = TavilyToolSpec(api_key=os.getenv('TAVILY_API_KEY'))\n",
+    "def search_tool(prompt:str)->list:\n",
+    "    \"return only search result from the web\"\n",
+    "    results = search.search(prompt)\n",
+    "    return [result.text for result in results]\n",
+    "search_toolkit = FunctionTool.from_defaults(fn=search_tool)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = Groq(model = \"gemma2-9b-it\")\n",
+    "agent = ReActAgent.from_tools([search_toolkit],llm=llm,verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> Running step 72522799-577e-4630-afd1-3f7637988a23. Step input: what is the addition of 25 and 26?\n",
+      "\u001b[1;3;38;5;200mThought: I can answer without using any more tools. I'll use the user's language to answer\n",
+      "Answer: 51\n",
+      "\u001b[0m"
+     ]
+    }
+   ],
+   "source": [
+    "response = agent.chat(\"what is the instagram link of Soumyadip Changder?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

@@ -0,0 +1,9 @@
+streamlit
+llama-index
+llama-index-llms-groq
+python-dotenv
+llama_index_tools_tavily_research
+pandas
+pybase64
+requests
+plotly