Updated app V1

.gitignore

@@ -0,0 +1,3 @@
+# Ignore environment files
+.env
+

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
+TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh
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+rU7m2Ys6xt0nUW7/vGT1M0NPAgMBAAGjQjBAMA4GA1UdDwEB/wQEAwIBBjAPBgNV
+HRMBAf8EBTADAQH/MB0GA1UdDgQWBBR5tFnme7bl5AFzgAiIyBpY9umbbjANBgkq
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+emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc=
+-----END CERTIFICATE-----

LICENSE.txt

@@ -0,0 +1,11 @@
+BigScience OpenRAIL-M License
+
+This model is licensed under the BigScience OpenRAIL-M License.
+
+You are free to use, share, and adapt this model for research and non-commercial purposes, provided you give proper attribution to the author (Ritvik Gaur).
+
+🛑 Commercial use is not permitted without explicit written permission.
+
+To request a commercial license, please contact: [[email protected]]
+
+For the full license text, see: https://huggingface.co/spaces/BigScience/OpenRAIL-M/blob/main/LICENSE.md

README.md

@@ -11,4 +11,10 @@ license: openrail
 short_description: Car AI Doctor
 ---
 
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+
+## License
+This project is distributed under the [BigScience OpenRAIL-M License](https://huggingface.co/spaces/BigScience/OpenRAIL-M).  
+Commercial use is prohibited unless explicit permission is granted by the author.
+
+For commercial licensing inquiries, contact: [[email protected]]

app.py

@@ -1,64 +1,316 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, 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 (nucleus sampling)",
-        ),
-    ],
-)
+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
+import requests
 
+# Load .env environment variables
+load_dotenv()
+api_key = os.getenv("GROQ_API_KEY")
+client = Groq(api_key=api_key)
+MODEL_NAME = "llama-3.3-70b-versatile"
+
+# In-memory cache for search results
+search_cache = defaultdict(str)
+cache_timeout = 3600  # 1 hour
+
+# In-memory Q&A store for community simulation
+community_qa = []
+
+# Diagnostics knowledge base (simplified)
+diagnostics_db = {
+    "Maruti Alto": {
+        "check engine light": {
+            "causes": ["Faulty oxygen sensor", "Loose fuel cap", "Spark plug issues"],
+            "solutions": ["Run OBD-II scan (₹500-₹1500)", "Tighten/replace fuel cap (₹100-₹500)", "Replace spark plugs (₹1000-₹2000)"],
+            "severity": "Moderate"
+        },
+        "poor fuel efficiency": {
+            "causes": ["Clogged air filter", "Tire underinflation", "Fuel injector issues"],
+            "solutions": ["Replace air filter (₹300-₹800)", "Check tire pressure (free)", "Clean/replace injectors (₹2000-₹5000)"],
+            "severity": "Low"
+        }
+    },
+    "Hyundai i20": {
+        "ac not cooling": {
+            "causes": ["Low refrigerant", "Faulty compressor", "Clogged condenser"],
+            "solutions": ["Refill refrigerant (₹1500-₹3000)", "Repair/replace compressor (₹5000-₹15000)", "Clean condenser (₹1000-₹2000)"],
+            "severity": "High"
+        }
+    }
+}
+
+# Maintenance tips
+maintenance_tips = [
+    "Check tire pressure monthly to improve fuel efficiency.",
+    "Change engine oil every 10,000 km or 6 months for Indian road conditions.",
+    "Inspect brakes regularly, especially during monsoon seasons.",
+    "Keep your car clean to prevent rust in humid climates."
+]
+
+# Tool: DuckDuckGo web search with retry and structured output
+def web_search_duckduckgo(query: str, max_results: int = 5, max_retries: int = 2):
+    cache_key = query.lower()
+    if cache_key in search_cache:
+        cached_time, cached_results = search_cache[cache_key]
+        if time.time() - cached_time < cache_timeout:
+            print(f"Using cached results for: {query}")
+            return cached_results
+
+    results = []
+    for attempt in range(max_retries):
+        try:
+            with DDGS() as ddgs:
+                for r in ddgs.text(query, region="in-en", safesearch="Moderate", max_results=max_results):
+                    results.append({"title": r['title'], "url": r['href']})
+            formatted_results = "\n\n".join(f"- {r['title']}\n  {r['url']}" for r in results)
+            search_cache[cache_key] = (time.time(), formatted_results)
+            return formatted_results
+        except Exception as e:
+            print(f"Search attempt {attempt + 1} failed: {str(e)}")
+            if attempt + 1 == max_retries:
+                return f"⚠️ Web search failed after {max_retries} attempts: {str(e)}"
+            time.sleep(1)
+
+# ReAct agent response with thought process
+def respond(message, history, system_message, max_tokens, temperature, top_p, vehicle_profile):
+    try:
+        # Initialize messages with ReAct system prompt
+        react_prompt = (
+            f"{system_message}\n\n"
+            "You are using the ReAct framework. For each user query, follow these steps:\n"
+            "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"
+            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
+        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})
+        messages.append({"role": "user", "content": message})
+
+        # Trigger keywords for garage search
+        trigger_keywords = [
+            "garage near", "car service near", "repair shop in", "mechanic in", "car workshop near",
+            "tyre change near", "puncture repair near", "engine repair near", "car wash near",
+            "car ac repair", "suspension work", "car battery replacement", "headlight change",
+            "oil change near", "nearby service center", "wheel alignment near", "wheel balancing",
+            "car painting service", "denting and painting", "car insurance repair", "maruti workshop",
+            "hyundai service", "honda repair center", "toyota garage", "tata motors service",
+            "mahindra car repair", "nexa service center", "kia workshop", "ev charging near",
+            "ev repair", "gearbox repair", "clutch repair", "brake pad replacement", 
+            "windshield repair", "car glass replacement", "coolant top up", "engine tuning",
+            "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"
+        ]
+
+        # Check diagnostics database
+        if vehicle_profile.get("make_model") and any(kw in message.lower() for kw in diagnostics_db.get(vehicle_profile["make_model"], {})):
+            for issue, details in diagnostics_db[vehicle_profile["make_model"]].items():
+                if issue in message.lower():
+                    response = (
+                        f"**Diagnosed Issue**: {issue}\n"
+                        f"- **Possible Causes**: {', '.join(details['causes'])}\n"
+                        f"- **Solutions**: {', '.join(details['solutions'])}\n"
+                        f"- **Severity**: {details['severity']}\n"
+                        f"Would you like to search for garages to address this issue or learn more?"
+                    )
+                    yield response
+                    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."
+                return
+            elif "view" in message.lower() or "see" in message.lower():
+                if community_qa:
+                    response = "Community Q&A:\n" + "\n".join(
+                        f"Q: {qa['question']}\nA: {', '.join(qa['answers']) or 'No answers yet'}"
+                        for qa in community_qa
+                    )
+                else:
+                    response = "No community questions yet. Post one with 'share' or 'post'!"
+                yield response
+                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)
+            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)]}"
+            for i in range(0, len(final_response), 10):
+                yield final_response[:i + 10]
+            return
+
+        # ReAct loop (up to 3 iterations)
+        max_iterations = 3
+        max_json_retries = 2
+        current_response = ""
+        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:
+                    completion = client.chat.completions.create(
+                        model=MODEL_NAME,
+                        messages=messages,
+                        temperature=temperature,
+                        max_completion_tokens=max_tokens,
+                        top_p=top_p,
+                        stream=False,
+                    )
+                    raw_response = completion.choices[0].message.content
+                    
+                    # 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", "")
+                        
+                        # Log to console
+                        print("Thought:", thought)
+                        print("Observation:", observation)
+                        print("Action:", action)
+                        break
+                    except json.JSONDecodeError:
+                        print(f"Error: LLM response is not valid JSON (attempt {retry + 1}/{max_json_retries}).")
+                        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"
+                        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'."
+                            })
+                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"
+                    else:
+                        time.sleep(1)
+
+            # Handle action
+            if action == "search":
+                search_query = react_step.get("search_query", message)
+                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}"
+                })
+                print(f"Search Results:\n{search_results}")
+                
+            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)]}"
+                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
+                print(f"Clarification Request:\n{current_response}")
+            elif action == "add_qa":
+                qa_content = react_step.get("qa_content", message)
+                community_qa.append({"question": qa_content, "answers": []})
+                current_response = "Your question has been posted to the community! Check back for answers."
+                print(f"Community Q&A Added:\n{qa_content}")
+                break
+            elif action == "get_qa":
+                if community_qa:
+                    current_response = "Community Q&A:\n" + "\n".join(
+                        f"Q: {qa['question']}\nA: {', '.join(qa['answers']) or 'No answers yet'}"
+                        for qa in community_qa
+                    )
+                else:
+                    current_response = "No community questions yet. Post one with 'share' or 'post'!"
+                print(f"Community Q&A Retrieved:\n{current_response}")
+                break
+            else:
+                print("Unknown action, continuing to next iteration.")
+                messages.append({"role": "assistant", "content": raw_response})
+
+        # Stream final response to Gradio
+        for i in range(0, len(current_response), 10):
+            yield current_response[:i + 10]
+
+    except Exception as e:
+        error_msg = f"❌ Error: {str(e)}\n{traceback.format_exc()}"
+        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
+    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")
+    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(
+        fn=update_vehicle_profile,
+        inputs=[make_model, year, city],
+        outputs=vehicle_profile
+    )
+    
+    # Chat interface
+    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"
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(share=True)

requirements.txt

@@ -1 +1,5 @@
-huggingface_hub==0.25.2
+huggingface_hub>=0.28.1
+gradio==5.29.0
+groq
+python-dotenv
+duckduckgo_search==6.2.12