Updated app 4

app.py

@@ -1,12 +1,12 @@
 import gradio as gr
 from groq import Groq
 from dotenv import load_dotenv
-from duckduckgo_search import DDGS
 import os
 import traceback
 import json
 import time
 from collections import defaultdict
+from duckduckgo_search import DDGS
 import requests
 
 # Load .env environment variables
@@ -78,8 +78,22 @@ def web_search_duckduckgo(query: str, max_results: int = 5, max_retries: int = 2
             time.sleep(1)
 
 # ReAct agent response with thought process
-def respond(message, history, system_message, max_tokens, temperature, top_p, vehicle_profile):
+def respond(message, history, vehicle_profile):
     try:
+        # Default values (hidden from UI)
+        system_message = (
+            "You are CarMaa, a highly intelligent and trusted AI Car Doctor trained on comprehensive automobile data, diagnostics, "
+            "and service records with specialized knowledge of Indian vehicles, road conditions, and market pricing. Your role is to "
+            "guide car owners with accurate insights, including service intervals, symptoms, estimated repair costs, garage locations, "
+            "climate effects, and fuel-efficiency tips. Personalize answers by vehicle details and city. Engage users as a community by "
+            "allowing Q&A posts and sharing maintenance tips. ALWAYS respond with a valid JSON object containing 'thought', 'observation', 'action', "
+            "and optionally 'search_query', 'response', or 'qa_content'. Do NOT include any text outside the JSON object. Example: "
+            "{\"thought\": \"User asks for garages, need to search.\", \"observation\": \"Location: Delhi\", \"action\": \"search\", \"search_query\": \"car garages Delhi\"}"
+        )
+        max_tokens = 1024
+        temperature = 0.7
+        top_p = 0.95
+
         # Initialize messages with ReAct system prompt
         react_prompt = (
             f"{system_message}\n\n"
@@ -87,24 +101,18 @@ def respond(message, history, system_message, max_tokens, temperature, top_p, ve
             "1. **Thought**: Reason about the query and decide the next step. Check the diagnostics database first for known issues. For location-specific queries (e.g., garages, repair shops) or real-time data (e.g., pricing, availability), prioritize web search. For community questions, check the Q&A store.\n"
             "2. **Observation**: Note relevant information (e.g., user input, vehicle profile, tool results, or context).\n"
             "3. **Action**: Choose an action: 'search' (web search), 'respond' (final answer), 'clarify' (ask for details), 'add_qa' (add to Q&A store), or 'get_qa' (retrieve Q&A).\n"
-            "Format your response as a valid JSON object with 'thought', 'observation', 'action', and optionally 'search_query', 'response', or 'qa_content'. Example:\n"
-            "{\n"
-            "  \"thought\": \"User asks for garages in Dehradun, need to search.\",\n"
-            "  \"observation\": \"Location: Dehradun\",\n"
-            "  \"action\": \"search\",\n"
-            "  \"search_query\": \"car repair shops Dehradun\"\n"
-            "}\n"
+            "Format your response as a valid JSON object with 'thought', 'observation', 'action', and optionally 'search_query', 'response', or 'qa_content'.\n"
             f"User vehicle profile: {json.dumps(vehicle_profile)}\n"
             "Use the search tool for locations, prices, or real-time data. Ensure valid JSON."
         )
         messages = [{"role": "system", "content": react_prompt}]
         
-        # Add history
+        # Convert Gradio chat history to OpenAI-style format
         for msg in history:
-            role = msg.get("role")
-            content = msg.get("content")
-            if role in ["user", "assistant"] and content:
-                messages.append({"role": role, "content": content})
+            if msg["role"] == "user":
+                messages.append({"role": "user", "content": msg["content"]})
+            elif msg["role"] == "assistant":
+                messages.append({"role": "assistant", "content": msg["content"]})
         messages.append({"role": "user", "content": message})
 
         # Trigger keywords for garage search
@@ -121,7 +129,7 @@ def respond(message, history, system_message, max_tokens, temperature, top_p, ve
             "car noise issue", "check engine light", "dashboard warning light", "local garage",
             "trusted mechanic", "authorized service center", "car towing service near me",
             "car not starting", "flat battery", "jump start service", "roadside assistance",
-            "ac not cooling", "car breakdown", "pickup and drop car service"
+            "ac not cooling", "car breakdown", "pickup and drop car service", "service centers for car repair"
         ]
 
         # Check diagnostics database
@@ -135,14 +143,17 @@ def respond(message, history, system_message, max_tokens, temperature, top_p, ve
                         f"- **Severity**: {details['severity']}\n"
                         f"Would you like to search for garages to address this issue or learn more?"
                     )
-                    yield response
+                    for i in range(0, len(response), 10):
+                        yield response[:i + 10]
                     return
 
         # Check for community Q&A keywords
         if any(kw in message.lower() for kw in ["community", "forum", "discussion", "share advice", "ask community"]):
             if "post" in message.lower() or "share" in message.lower():
                 community_qa.append({"question": message, "answers": []})
-                yield "Your question has been posted to the community! Check back for answers."
+                response = "Your question has been posted to the community! Check back for answers."
+                for i in range(0, len(response), 10):
+                    yield response[:i + 10]
                 return
             elif "view" in message.lower() or "see" in message.lower():
                 if community_qa:
@@ -152,15 +163,23 @@ def respond(message, history, system_message, max_tokens, temperature, top_p, ve
                     )
                 else:
                     response = "No community questions yet. Post one with 'share' or 'post'!"
-                yield response
+                for i in range(0, len(response), 10):
+                    yield response[:i + 10]
                 return
 
         # Check for trigger keywords to directly perform search
         if any(keyword in message.lower() for keyword in trigger_keywords):
             print(f"Trigger keyword detected in query: {message}")
-            search_results = web_search_duckduckgo(message)
+            # Enhance search query with "car" context and city from vehicle profile if available
+            search_query = message.replace("reapir", "repair")  # Correct typo
+            if "car" not in search_query.lower():
+                search_query = f"car {search_query}"
+            if vehicle_profile.get("city"):
+                search_query = f"{search_query} {vehicle_profile['city']}"
+            search_results = web_search_duckduckgo(search_query)
             print(f"Search Results:\n{search_results}")
-            final_response = f"🔍 Here are some results I found:\n\n{search_results}\n\n**Tip**: {maintenance_tips[hash(message) % len(maintenance_tips)]}"
+            final_response = f"🔍 Here are some car repair service centers I found:\n\n{search_results}\n\n**Tip**: {maintenance_tips[hash(message) % len(maintenance_tips)]}"
+            # Ensure the response is yielded to the UI
             for i in range(0, len(final_response), 10):
                 yield final_response[:i + 10]
             return
@@ -169,12 +188,18 @@ def respond(message, history, system_message, max_tokens, temperature, top_p, ve
         max_iterations = 3
         max_json_retries = 2
         current_response = ""
+        previous_raw_response = None
+        clarification_count = 0  # Track number of clarification requests
+
         for iteration in range(max_iterations):
             print(f"\n--- ReAct Iteration {iteration + 1} ---")
             
             # Call LLM with current messages
             for retry in range(max_json_retries):
                 try:
+                    # Add a slight delay to avoid rate limits
+                    time.sleep(0.5)
+                    
                     completion = client.chat.completions.create(
                         model=MODEL_NAME,
                         messages=messages,
@@ -184,63 +209,117 @@ def respond(message, history, system_message, max_tokens, temperature, top_p, ve
                         stream=False,
                     )
                     raw_response = completion.choices[0].message.content
+                    print(f"Raw LLM Response: {raw_response}")  # Log raw response for debugging
                     
+                    # Check if the response is empty or unchanged
+                    if not raw_response or raw_response == previous_raw_response:
+                        print(f"LLM returned empty or unchanged response on attempt {retry + 1}/{max_json_retries}")
+                        if retry + 1 == max_json_retries:
+                            react_step = {
+                                "thought": "LLM failed to provide a new response",
+                                "observation": "No new response received",
+                                "action": "respond",
+                                "response": "Sorry, I couldn't process your request properly. Please try again or provide more details."
+                            }
+                            thought = react_step["thought"]
+                            observation = react_step["observation"]
+                            action = react_step["action"]
+                            response = react_step["response"]
+                            break
+                        else:
+                            messages.append({
+                                "role": "system",
+                                "content": "Previous response was empty or unchanged. You MUST provide a new, valid JSON object containing 'thought', 'observation', 'action', and optionally 'search_query', 'response', or 'qa_content'. No text outside JSON is allowed."
+                            })
+                            continue
+
                     # Parse LLM response
                     try:
                         react_step = json.loads(raw_response)
                         thought = react_step.get("thought", "")
                         observation = react_step.get("observation", "")
                         action = react_step.get("action", "")
+                        response = react_step.get("response", "")
                         
-                        # Log to console
+                        # Log to terminal
                         print("Thought:", thought)
                         print("Observation:", observation)
                         print("Action:", action)
+                        previous_raw_response = raw_response
                         break
                     except json.JSONDecodeError:
-                        print(f"Error: LLM response is not valid JSON (attempt {retry + 1}/{max_json_retries}).")
+                        print(f"Error: LLM response is not valid JSON (attempt {retry + 1}/{max_json_retries}). Raw response: {raw_response}")
                         if retry + 1 == max_json_retries:
                             print("Max retries reached. Treating as direct response.")
-                            react_step = {"response": raw_response, "action": "respond"}
-                            thought = "N/A (Invalid JSON)"
-                            observation = "N/A (Invalid JSON)"
-                            action = "respond"
+                            react_step = {
+                                "thought": "Unable to parse JSON",
+                                "observation": "Invalid LLM output",
+                                "action": "respond",
+                                "response": "Sorry, I couldn't process your request properly. Please try again or provide more details."
+                            }
+                            thought = react_step["thought"]
+                            observation = react_step["observation"]
+                            action = react_step["action"]
+                            response = react_step["response"]
+                            previous_raw_response = raw_response
                         else:
                             messages.append({
                                 "role": "system",
-                                "content": "Previous response was not valid JSON. Please provide a valid JSON object with 'thought', 'observation', 'action', and optionally 'search_query', 'response', or 'qa_content'."
+                                "content": "Previous response was not valid JSON. You MUST respond with a valid JSON object containing 'thought', 'observation', 'action', and optionally 'search_query', 'response', or 'qa_content'. No text outside JSON is allowed."
                             })
                 except Exception as e:
                     print(f"LLM call failed (attempt {retry + 1}/{max_json_retries}): {str(e)}")
                     if retry + 1 == max_json_retries:
-                        react_step = {"response": f"⚠️ Failed to process query: {str(e)}", "action": "respond"}
-                        thought = "N/A (LLM error)"
-                        observation = "N/A (LLM error)"
-                        action = "respond"
+                        react_step = {
+                            "thought": "LLM call failed",
+                            "observation": f"Error: {str(e)}",
+                            "action": "respond",
+                            "response": f"⚠️ Failed to process query: {str(e)}"
+                        }
+                        thought = react_step["thought"]
+                        observation = react_step["observation"]
+                        action = react_step["action"]
+                        response = react_step["response"]
                     else:
                         time.sleep(1)
 
             # Handle action
             if action == "search":
                 search_query = react_step.get("search_query", message)
+                # Enhance search query with "car" context and city from vehicle profile if available
+                search_query = search_query.replace("reapir", "repair")  # Correct typo
+                if "car" not in search_query.lower():
+                    search_query = f"car {search_query}"
+                if vehicle_profile.get("city"):
+                    search_query = f"{search_query} {vehicle_profile['city']}"
                 print(f"Performing web search for: {search_query}")
                 search_results = web_search_duckduckgo(search_query)
-                messages.append({"role": "assistant", "content": raw_response})
-                messages.append({
-                    "role": "system",
-                    "content": f"Search results for '{search_query}':\n{search_results}"
-                })
+                messages.append({"role": "system", "content": f"Search results for '{search_query}':\n{search_results}"})
                 print(f"Search Results:\n{search_results}")
+                current_response = f"🔍 Here are some car repair service centers I found:\n\n{search_results}\n\n**Tip**: {maintenance_tips[hash(message) % len(maintenance_tips)]}"
+                break  # Exit loop to display results immediately
                 
             elif action == "respond":
-                final_response = react_step.get("response", raw_response)
-                current_response = f"{final_response}\n\n**Tip**: {maintenance_tips[hash(message) % len(maintenance_tips)]}"
+                current_response = f"{response}\n\n**Tip**: {maintenance_tips[hash(message) % len(maintenance_tips)]}"
                 print(f"Final Response:\n{current_response}")
                 break
             elif action == "clarify":
-                clarification = react_step.get("response", "Please provide more details.")
-                messages.append({"role": "assistant", "content": raw_response})
-                current_response = clarification
+                clarification_count += 1
+                current_response = response or "Please provide more details."
+                # Avoid repetitive clarification by modifying the context
+                if clarification_count >= max_iterations:
+                    # Fallback to search if clarification isn't helping
+                    search_query = message.replace("reapir", "repair")  # Correct typo
+                    if "car" not in search_query.lower():
+                        search_query = f"car {search_query}"
+                    if vehicle_profile.get("city"):
+                        search_query = f"{search_query} {vehicle_profile['city']}"
+                    print(f"Performing web search after max clarifications for: {search_query}")
+                    search_results = web_search_duckduckgo(search_query)
+                    current_response = f"🔍 I couldn't get enough details, but here are some car repair service centers I found:\n\n{search_results}\n\n**Tip**: {maintenance_tips[hash(message) % len(maintenance_tips)]}"
+                    print(f"Search Results:\n{search_results}")
+                    break
+                messages.append({"role": "assistant", "content": json.dumps(react_step)})
                 print(f"Clarification Request:\n{current_response}")
             elif action == "add_qa":
                 qa_content = react_step.get("qa_content", message)
@@ -260,9 +339,9 @@ def respond(message, history, system_message, max_tokens, temperature, top_p, ve
                 break
             else:
                 print("Unknown action, continuing to next iteration.")
-                messages.append({"role": "assistant", "content": raw_response})
+                messages.append({"role": "assistant", "content": json.dumps(react_step)})
 
-        # Stream final response to Gradio
+        # Stream the final response to the UI
         for i in range(0, len(current_response), 10):
             yield current_response[:i + 10]
 
@@ -271,45 +350,245 @@ def respond(message, history, system_message, max_tokens, temperature, top_p, ve
         print(error_msg)
         yield error_msg
 
-# Gradio interface with vehicle profile
-with gr.Blocks(title="CarMaa - India's AI Car Doctor") as demo:
-    gr.Markdown("# CarMaa - India's AI Car Doctor")
-    gr.Markdown("Your trusted AI for car diagnostics, garage searches, and community advice.")
-    
-    # Vehicle profile inputs
+# Gradio interface with enhanced, customer-centric UI
+with gr.Blocks(
+    title="CarMaa - India's AI Car Doctor",
+    css="""
+        /* Overall layout and background */
+        .gradio-container {
+            background: linear-gradient(135deg, #1a1a1a, #2c2c2c);
+            font-family: 'Arial', sans-serif;
+            color: #ffffff;
+        }
+        /* Header styling with car-themed elements */
+        .header {
+            background: url('https://www.transparenttextures.com/patterns/asfalt-dark.png');
+            padding: 20px;
+            border-radius: 10px 10px 0 0;
+            border-bottom: 3px solid #ff4d4d;
+            text-align: center;
+            box-shadow: 0 4px 10px rgba(0, 0, 0, 0.5);
+        }
+        .header h1 {
+            font-size: 2.5em;
+            font-weight: bold;
+            color: #ff4d4d;
+            text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.7);
+            margin: 0;
+        }
+        .header p {
+            color: #d1d1d1;
+            font-size: 1.1em;
+            margin-top: 5px;
+        }
+        /* Vehicle profile inputs with car-themed styling */
+        .vehicle-profile {
+            background: #2c2c2c;
+            padding: 20px;
+            border-radius: 8px;
+            margin: 20px 0;
+            border: 1px solid #444;
+            box-shadow: 0 2px 8px rgba(0, 0, 0, 0.3);
+        }
+        .vehicle-profile label {
+            color: #ff4d4d;
+            font-weight: bold;
+            margin-bottom: 5px;
+            display: block;
+        }
+        .vehicle-profile input {
+            background: #3a3a3a;
+            color: #ffffff;
+            border: 1px solid #555;
+            border-radius: 5px;
+            padding: 10px;
+            width: 100%;
+            transition: border-color 0.3s;
+        }
+        .vehicle-profile input:focus {
+            border-color: #ff4d4d;
+            outline: none;
+            box-shadow: 0 0 5px rgba(255, 77, 77, 0.5);
+        }
+        /* Save button with hover animation */
+        .save-btn {
+            background: #ff4d4d !important;
+            color: #ffffff !important;
+            border: none !important;
+            padding: 12px !important;
+            border-radius: 5px !important;
+            font-weight: bold !important;
+            transition: transform 0.2s, background 0.3s !important;
+            width: 100% !important;
+            margin-top: 10px !important;
+        }
+        .save-btn:hover {
+            background: #e63939 !important;
+            transform: scale(1.05);
+        }
+        /* Chat container with car dashboard feel */
+        .chatbot-container {
+            background: #1f1f1f;
+            border: 2px solid #ff4d4d;
+            border-radius: 8px;
+            padding: 15px;
+            max-height: 400px;
+            overflow-y: auto;
+            margin-bottom: 20px;
+            box-shadow: inset 0 0 10px rgba(0, 0, 0, 0.5);
+        }
+        /* Chat messages with car-themed styling */
+        .chatbot-container .message-user div, .chatbot-container .message-assistant div {
+            padding: 10px !important;
+            border-radius: 8px !important;
+            margin-bottom: 10px !important;
+            max-width: 80% !important;
+            word-wrap: break-word !important;
+        }
+        .chatbot-container .message-user div {
+            background: #ff4d4d !important;
+            color: #ffffff !important;
+            margin-left: auto !important;
+            border: 1px solid #e63939 !important;
+        }
+        .chatbot-container .message-assistant div {
+            background: #ffffff !important;
+            color: #1a1a1a !important;
+            margin-right: auto !important;
+            border: 1px solid #d1d1d1 !important;
+        }
+        /* Chat input area */
+        .chatbot-container textarea {
+            background: #3a3a3a !important;
+            color: #ffffff !important;
+            border: 1px solid #555 !important;
+            border-radius: 5px !important;
+            padding: 10px !important;
+            transition: border-color 0.3s !important;
+        }
+        .chatbot-container textarea:focus {
+            border-color: #ff4d4d !important;
+            box-shadow: 0 0 5px rgba(255, 77, 77, 0.5) !important;
+        }
+        /* Send button with car icon */
+        .chatbot-container button {
+            background: #ff4d4d !important;
+            color: #ffffff !important;
+            border: none !important;
+            border-radius: 5px !important;
+            padding: 10px 20px !important;
+            font-weight: bold !important;
+            transition: transform 0.2s, background 0.3s !important;
+        }
+        .chatbot-container button:hover {
+            background: #e63939 !important;
+            transform: scale(1.05);
+        }
+        /* Navigation tabs for quick access */
+        .nav-tabs {
+            display: flex;
+            justify-content: space-around;
+            background: #2c2c2c;
+            padding: 10px 0;
+            border-radius: 8px;
+            margin-bottom: 20px;
+            border: 1px solid #444;
+        }
+        .nav-tabs button {
+            background: none;
+            border: none;
+            color: #d1d1d1;
+            font-weight: bold;
+            padding: 10px;
+            transition: color 0.3s;
+            display: flex;
+            align-items: center;
+            gap: 5px;
+        }
+        .nav-tabs button:hover {
+            color: #ff4d4d;
+        }
+        .nav-tabs button.active {
+            color: #ff4d4d;
+            border-bottom: 2px solid #ff4d4d;
+        }
+        /* Footer with subtle branding */
+        .footer {
+            text-align: center;
+            color: #d1d1d1;
+            font-size: 0.9em;
+            margin-top: 20px;
+        }
+    """
+) as demo:
+    # Header with car-themed branding
+    with gr.Row():
+        gr.Markdown(
+            """
+            <div class='header'>
+                <h1>🚗 CarMaa - India's AI Car Doctor</h1>
+                <p>Diagnose issues, find garages, and connect with the car community!</p>
+            </div>
+            """
+        )
+
+    # Navigation tabs for quick access
     with gr.Row():
-        make_model = gr.Textbox(label="Vehicle Make and Model (e.g., Maruti Alto)", placeholder="Enter your car's make and model")
-        year = gr.Textbox(label="Year", placeholder="Enter the year of manufacture")
-        city = gr.Textbox(label="City", placeholder="Enter your city")
+        gr.Markdown(
+            """
+            <div class='nav-tabs'>
+                <button class='active'>🚘 Profile</button>
+                <button>🔧 Diagnostics</button>
+                <button>🗣️ Community</button>
+            </div>
+            """
+        )
+
+    # Vehicle profile inputs
+    with gr.Row(variant="panel", elem_classes="vehicle-profile"):
+        make_model = gr.Textbox(
+            label="Vehicle Make and Model",
+            placeholder="e.g., Maruti Alto"
+        )
+        year = gr.Textbox(
+            label="Year",
+            placeholder="e.g., 2020"
+        )
+        city = gr.Textbox(
+            label="City",
+            placeholder="e.g., Delhi"
+        )
     vehicle_profile = gr.State(value={"make_model": "", "year": "", "city": ""})
     
     # Update vehicle profile
     def update_vehicle_profile(make_model, year, city):
         return {"make_model": make_model, "year": year, "city": city}
     
-    gr.Button("Save Vehicle Profile").click(
+    gr.Button("Save Vehicle Profile", elem_classes="save-btn").click(
         fn=update_vehicle_profile,
         inputs=[make_model, year, city],
         outputs=vehicle_profile
     )
     
-    # Chat interface
+    # Chat interface with enhanced styling
     chatbot = gr.ChatInterface(
         fn=respond,
-        additional_inputs=[
-            gr.Textbox(value=(
-                "You are CarMaa, a highly intelligent and trusted AI Car Doctor trained on comprehensive automobile data, diagnostics, "
-                "and service records with specialized knowledge of Indian vehicles, road conditions, and market pricing. Your role is to "
-                "guide car owners with accurate insights, including service intervals, symptoms, estimated repair costs, garage locations, "
-                "climate effects, and fuel-efficiency tips. Personalize answers by vehicle details and city. Engage users as a community by "
-                "allowing Q&A posts and sharing maintenance tips."
-            ), label="System message"),
-            gr.Slider(minimum=1, maximum=4096, value=1024, step=1, label="Max new tokens"),
-            gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
-            gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
-            vehicle_profile
-        ],
-        type="messages"
+        additional_inputs=[vehicle_profile],
+        title="",
+        description="Ask about car diagnostics, garage locations, or community advice.",
+        theme="soft",
+        textbox=gr.Textbox(placeholder="Ask about your car... 🚘"),
+        submit_btn="Send 🚀",
+        type="messages"  # Updated to use the modern 'messages' format
+    )
+
+    # Footer
+    gr.Markdown(
+        """
+        <div class='footer'>
+            Powered by CarMaa © 2025 | Your Trusted Car Care Companion
+        </div>
+        """
     )
 
 if __name__ == "__main__":