app.py

import json
import os
from pathlib import Path
import numpy as np
import requests
from typing import Optional, List, Tuple, Dict

import gradio as gr
from dotenv import load_dotenv
from gradio_client import Client, handle_file
from sentence_transformers import SentenceTransformer

# --- 1. SETUP AND MODEL LOADING ---
load_dotenv()
GROQ_API_KEY = os.getenv("GROQ_API_KEY") 

if not GROQ_API_KEY:
    raise ValueError("GROQ_API_KEY not found. It's needed for summarizing analysis results.")

print("Loading models and connecting to clients...")
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
try:
    chatbot_client = Client("Anvit25/LLM_chatbot2")
    audio_client = Client("Anvit25/new_audio")
    vision_client = Client("Anvit25/vision-classifier")
    print("All models and clients loaded successfully.")
except Exception as e:
    print(f"FATAL: Failed to connect to a Gradio client: {e}")
    exit()

# --- 2. LOAD & PRECOMPUTE DATA FOR LOCAL SEARCH & INTENT ---
# Load local image data
image_list = []
try:
    with open("image.json", "r") as f:
        data = json.load(f)
    image_dir = Path("images")
    if image_dir.exists():
        for page in data.get("pages", []):
            for img in page.get("images", []):
                description = img.get("description", "")
                if not description: continue
                for img_file in image_dir.iterdir():
                    if img_file.is_file() and description.lower() in img_file.name.lower():
                        image_list.append({"file": str(img_file), "description": description})
                        break
        print(f"Found {len(image_list)} local images for semantic search.")
        print("Precomputing embeddings for local images...")
        for img in image_list:
            img["embedding"] = embedding_model.encode(img["description"])
        print("Embeddings precomputed.")
    else:
        print("Warning: 'images' directory not found.")
except FileNotFoundError:
    print("Warning: image.json not found.")

# Load intents from JSON for the new rule-based classifier
try:
    with open("intents.json", "r") as f:
        intents_data = json.load(f)
    print("Local intent classifier phrases loaded successfully.")
except FileNotFoundError:
    print("FATAL: intents.json not found. This file is required.")
    exit()

# --- 3. HELPER FUNCTIONS ---

def get_user_intent_local(user_query: str) -> dict:
    """
    CORRECTED: Uses a robust rule-based check to classify user intent.
    This is much more reliable than the previous semantic search approach for intents.
    """
    lower_query = user_query.lower()
    
    # Iterate through intents and their trigger phrases
    for intent, phrases in intents_data.items():
        for phrase in phrases:
            if phrase.lower() in lower_query:
                # If a trigger phrase is found, identify the intent
                subject = lower_query.replace(phrase.lower(), "").strip()
                result = {
                    "intent": intent,
                    "query": subject if subject else user_query
                }
                print(f"Local Intent Classifier Result: {result}")
                return result

    # If no specific trigger phrase is found, default to a general chat
    result = {"intent": "chat", "query": user_query}
    print(f"Local Intent Classifier Result: {result}")
    return result

def summarize_analysis_with_groq(json_result: dict, context: str) -> str:
    """Takes a JSON/dict result and uses Groq to create a human-readable summary."""
    prompt = f"""
    You are a helpful assistant. Based on the following technical analysis from a specialized AI model, provide a friendly and concise summary for the user.
    Context: The user asked to '{context}'.
    AI Model's Raw JSON Output:
    ```json
    {json.dumps(json_result, indent=2)}
    ```
    Your friendly, easy-to-understand summary:
    """
    try:
        response = requests.post(
            "https://api.groq.com/openai/v1/chat/completions",
            headers={"Authorization": f"Bearer {GROQ_API_KEY}"},
            json={"messages": [{"role": "user", "content": prompt}], "model": "llama-3.3-70b-versatile"},
        )
        response.raise_for_status()
        return response.json()["choices"][0]["message"]["content"]
    except Exception as e:
        print(f"Groq summary error: {e}")
        return f"I finished the analysis, but had trouble summarizing it. Here is the raw data:\n`{json.dumps(json_result)}`"

def cosine_similarity(vec1, vec2):
    norm1 = np.linalg.norm(vec1)
    norm2 = np.linalg.norm(vec2)
    if norm1 == 0 or norm2 == 0: return 0.0
    return np.dot(vec1, vec2) / (norm1 * norm2)

def find_best_matching_image(query: str) -> Optional[dict]:
    if not image_list: return None
    query_emb = embedding_model.encode(query)
    best_match = max(image_list, key=lambda img: cosine_similarity(query_emb, img.get("embedding", [])))
    highest_score = cosine_similarity(query_emb, best_match.get("embedding", []))
    if best_match and highest_score > 0.4:
        return best_match
    return None

def generate_groq_narrative(user_query: str, search_result: Optional[dict]) -> str:
    if search_result:
        prompt = f"A user asked to find: '{user_query}'. You found an image described as: '{search_result['description']}'. Craft a short, friendly response."
    else:
        prompt = f"A user asked to find: '{user_query}'. You searched but couldn't find a match. Craft a short, polite response."
    try:
        response = requests.post(
            "https://api.groq.com/openai/v1/chat/completions",
            headers={"Authorization": f"Bearer {GROQ_API_KEY}"},
            json={"messages": [{"role": "user", "content": prompt}], "model": "llama3-8b-8192"},
        )
        response.raise_for_status()
        return response.json()["choices"][0]["message"]["content"]
    except Exception as e:
        print(f"Groq narrative error: {e}")
        return "I had an issue describing the search result."

# --- 4. CORE GRADIO LOGIC ---

def handle_image_analysis(file_path: str) -> str:
    try:
        vision_result = vision_client.predict(image=handle_file(file_path), api_name="/predict")
        summary = summarize_analysis_with_groq(vision_result, "Analyze this image")
        return summary
    except Exception as e:
        return f"Sorry, I couldn't analyze the image. Error: {e}"

def handle_audio_analysis(file_path: str) -> str:
    try:
        prediction_text, _ = audio_client.predict(audio_filepath=handle_file(file_path), api_name="/predict")
        return f"The audio analysis result is: **{prediction_text}**"
    except Exception as e:
        return f"Sorry, I couldn't analyze the audio. Error: {e}"

def chat_interface(user_input: dict, history: List[Tuple[str, str]]):
    user_text = user_input["text"].strip()
    user_files = user_input["files"]
    new_history = history or []
    
    # Priority 1: Handle file uploads
    if user_files:
        file_path = user_files[0]
        new_history.append(((file_path,), None))
        
        if file_path.lower().endswith(('.png', '.jpg', '.jpeg', '.webp')):
            bot_message = handle_image_analysis(file_path)
        elif file_path.lower().endswith(('.wav', '.mp3', '.flac')):
            bot_message = handle_audio_analysis(file_path)
        else:
            bot_message = "I'm not sure how to handle that file type."
        
        new_history[-1] = (new_history[-1][0], bot_message)
        return new_history, None

    # Priority 2: Handle text-only queries
    if not user_text:
        return new_history, None

    new_history.append((user_text, None))
    intent_data = get_user_intent_local(user_text) # Use the NEW, robust classifier
    intent = intent_data.get("intent")
    query_subject = intent_data.get("query")

    if intent == "chat":
        try:
            prediction = chatbot_client.predict(user_input=query_subject, api_name="/chatbot_response")
            bot_message = prediction[-1]['content']
        except Exception as e:
            print(f"Error calling chatbot client: {e}")
            bot_message = "I'm sorry, I'm having trouble connecting to my chat brain right now. Please try again."

    elif intent == "search_local_image":
        found_image = find_best_matching_image(query_subject)
        bot_message = generate_groq_narrative(query_subject, found_image)
        new_history[-1] = (user_text, bot_message)
        if found_image:
             new_history.append((None, (found_image['file'],)))
        return new_history, None

    elif intent == "request_image_analysis":
        bot_message = "Of course. Please upload the image you want me to analyze."
    elif intent == "request_audio_analysis":
        bot_message = "I'm ready. Please upload the audio file for analysis."
    else:
        bot_message = "I'm not sure how to handle that. Can you rephrase?"

    new_history[-1] = (user_text, bot_message)
    return new_history, None


# --- 5. GRADIO UI DEFINITION ---
with gr.Blocks(theme=gr.themes.Soft(), title="Multi-Modal AI Chatbot") as demo:
    gr.Markdown("# Multi-Modal AI Chatbot")
    gr.Markdown("I can chat, search for local images, or analyze images and audio you upload.")

    chatbot_history = gr.Chatbot(height=600, show_copy_button=True, layout="bubble", render=False)
    
    with gr.Row():
        multimodal_textbox = gr.MultimodalTextbox(
            file_types=["image", "audio"],
            placeholder="Type your message or upload a file...",
            submit_btn="Send",
            render=False,
            autofocus=True
        )

    chatbot_history.render()
    multimodal_textbox.render()

    multimodal_textbox.submit(
        fn=chat_interface,
        inputs=[multimodal_textbox, chatbot_history],
        outputs=[chatbot_history, multimodal_textbox]
    )

if __name__ == "__main__":
    demo.launch(debug=True)