Update app.py

app.py

@@ -1,326 +1,3 @@
-# import json
-# import os
-# from pathlib import Path
-# import numpy as np
-# from fastapi import FastAPI, Query
-# from fastapi.responses import FileResponse
-
-# # Use a dedicated library for creating text embeddings
-# from sentence_transformers import SentenceTransformer
-
-# # --- 1. Load the Local Embedding Model ---
-# # This line downloads (first time only) and loads a powerful, lightweight model 
-# # into memory. This is much more efficient than using an API for this task.
-# print("Loading sentence-transformer model...")
-# embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
-# print("Model loaded successfully.")
-
-# # --- 2. Load Image Metadata ---
-# try:
-#     with open("image.json", "r") as f:
-#         data = json.load(f)
-# except FileNotFoundError:
-#     print("Error: image.json not found. Please make sure the file exists.")
-#     exit()
-
-# image_list = []
-# image_dir = Path("images")
-# if not image_dir.exists():
-#     print(f"Error: The '{image_dir}' directory does not exist.")
-#     exit()
-
-# # Prepare list of images and descriptions
-# for page in data.get("pages", []):
-#     for img in page.get("images", []):
-#         description = img.get("description", "")
-#         if not description:
-#             continue
-#         # Match description to a file in the images folder
-#         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 # Move to the next description once a match is found
-
-# print(f"Found {len(image_list)} images with matching descriptions.")
-
-
-# # --- 3. Function to Get Embeddings Locally ---
-# def get_embedding(text: str) -> np.ndarray:
-#     """
-#     Generates an embedding for the given text using the local SentenceTransformer model.
-#     """
-#     # The model.encode() method directly returns a numpy array. It's fast and local.
-#     return embedding_model.encode(text)
-
-# # --- 4. Precompute Embeddings for All Images ---
-# print("Precomputing embeddings for all image descriptions...")
-# for img in image_list:
-#     # Each description is converted into a numerical vector (embedding)
-#     img["embedding"] = get_embedding(img["description"])
-# print("Embeddings precomputed.")
-
-
-# # --- 5. FastAPI Application ---
-# app = FastAPI(title="Semantic Image Search API")
-
-# def cosine_similarity(vec1: np.ndarray, vec2: np.ndarray) -> float:
-#     """Calculates the cosine similarity between two vectors."""
-#     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)
-
-# @app.get("/search_image/")
-# async def search_image(query: str = Query(..., description="Search text")):
-#     # Convert the user's search query into an embedding
-#     query_emb = get_embedding(query)
-    
-#     best_match = None
-#     highest_score = -1.0 # Cosine similarity ranges from -1 to 1
-    
-#     # Compare the query embedding to all precomputed image description embeddings
-#     for img in image_list:
-#         score = cosine_similarity(query_emb, img["embedding"])
-#         if score > highest_score:
-#             highest_score = score
-#             best_match = img
-
-#     if best_match:
-#         print(f"Query: '{query}' -> Found best match: {best_match['file']} with score: {highest_score:.4f}")
-#         return FileResponse(best_match["file"])
-        
-#     return {"error": "No matching image found"}
-
-
-
-# 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, util
-
-# # --- 1. SETUP AND MODEL LOADING ---
-# load_dotenv()
-# GROQ_API_KEY = os.getenv("GROQ_API_KEY") # Still used for summarizing results
-
-# 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...")
-# # Model for local intent classification and image search
-# 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 (same as before)
-# image_list = []
-# # ... (Your image loading and embedding logic is unchanged) ...
-
-# # NEW: Load intents from JSON and pre-compute their embeddings
-# intent_embeddings = {}
-# try:
-#     with open("intents.json", "r") as f:
-#         intents_data = json.load(f)
-#         for intent, phrases in intents_data.items():
-#             intent_embeddings[intent] = {
-#                 "phrases": phrases,
-#                 "embeddings": embedding_model.encode(phrases)
-#             }
-#     print("Local intent classifier loaded successfully.")
-# except FileNotFoundError:
-#     print("FATAL: intents.json not found. This file is required for the local intent classifier.")
-#     exit()
-
-# # --- 3. HELPER FUNCTIONS ---
-
-# def get_user_intent_local(user_query: str) -> dict:
-#     """
-#     Uses SentenceTransformer to classify user intent locally based on intents.json.
-#     """
-#     query_embedding = embedding_model.encode(user_query)
-#     best_match = {"intent": "chat", "score": 0.7, "query": user_query} # Default to chat
-
-#     for intent, data in intent_embeddings.items():
-#         # Calculate cosine similarity between user query and all trigger phrases for an intent
-#         scores = util.cos_sim(query_embedding, data["embeddings"])[0]
-#         max_score = max(scores)
-        
-#         if max_score > best_match["score"]:
-#             best_match["score"] = max_score.item()
-#             best_match["intent"] = intent
-#             # Extract the subject by removing the trigger phrase
-#             best_phrase_index = np.argmax(scores)
-#             trigger_phrase = data["phrases"][best_phrase_index]
-#             subject = user_query.lower().replace(trigger_phrase.lower(), "").strip()
-#             best_match["query"] = subject if subject else user_query
-
-#     print(f"Local Intent Classifier Result: {best_match}")
-#     return best_match
-
-# def summarize_analysis_with_groq(json_result: dict, context: str) -> str:
-#     """
-#     NEW: 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": "llama3-8b-8192"},
-#         )
-#         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)}`"
-
-
-# # ... (cosine_similarity and find_best_matching_image functions are unchanged) ...
-# def find_best_matching_image(query: str) -> Optional[dict]: # ... (Identical) ...
-#     pass
-# def generate_groq_narrative(user_query: str, search_result: Optional[dict]) -> str: # ... (Identical) ...
-#     pass
-
-# # --- 4. CORE GRADIO LOGIC (UPDATED) ---
-
-# def handle_image_analysis(file_path: str) -> str:
-#     """Analyzes an image and returns a text summary."""
-#     try:
-#         vision_result = vision_client.predict(image=handle_file(file_path), api_name="/predict")
-#         # NEW: Summarize the JSON result
-#         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:
-#     """Analyzes audio and returns a text summary."""
-#     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]]):
-#     """
-#     The main function that powers the Gradio chat interface.
-#     It now prioritizes file uploads over text for intent classification.
-#     """
-#     user_text = user_input["text"].strip()
-#     user_files = user_input["files"]
-#     new_history = history or []
-
-#     bot_message = ""
-    
-#     # === Priority 1: Handle file uploads ===
-#     if user_files:
-#         file_path = user_files[0]
-#         # Display the uploaded file in the chat
-#         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 no files are uploaded ===
-#     if not user_text:
-#         return new_history, None
-
-#     new_history.append((user_text, None))
-#     intent_data = get_user_intent_local(user_text) # Use local classifier
-#     intent = intent_data.get("intent")
-#     query_subject = intent_data.get("query")
-
-#     if intent == "chat":
-#         prediction = chatbot_client.predict(user_input=query_subject, api_name="/chatbot_response")
-#         bot_message = prediction[-1]['content']
-
-#     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'],))) # Display image on new line
-#         return new_history, None
-
-#     # For these intents, we just prompt the user to upload a file
-#     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.")
-
-#     # CORRECTED LINE: The 'bubble_fn' argument is removed.
-#     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
-#         )
-
-#     # Render components after defining the layout
-#     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)
-
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 import json
 import os
 from pathlib import Path