Update app.py

app.py

@@ -227,319 +227,3 @@ if __name__ == '__main__':
 
 
 
-
-
-# # Above code is without polling and sleep
-# import os
-# import whisper
-# import requests
-# from flask import Flask, request, jsonify, render_template
-# import tempfile
-# import warnings
-# warnings.filterwarnings("ignore", message="FP16 is not supported on CPU; using FP32 instead")
-
-# app = Flask(__name__)
-# print("APP IS RUNNING, ANIKET")
-
-# # Gemini API settings
-# from dotenv import load_dotenv
-# # Load the .env file
-# load_dotenv()
-
-# print("ENV LOADED, ANIKET")
-
-# # Fetch the API key from the .env file
-# API_KEY = os.getenv("FIRST_API_KEY")
-
-# # Ensure the API key is loaded correctly
-# if not API_KEY:
-#     raise ValueError("API Key not found. Make sure it is set in the .env file.")
-
-# GEMINI_API_ENDPOINT = "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash-latest:generateContent"
-# GEMINI_API_KEY = API_KEY
-
-
-# # Load Whisper AI model at startup
-# print("Loading Whisper AI model..., ANIKET")
-# whisper_model = whisper.load_model("base")  # Choose model size: tiny, base, small, medium, large
-# print("Whisper AI model loaded successfully, ANIKET")
-
-
-# # Define the "/" endpoint for health check
-# @app.route("/", methods=["GET"])
-# def health_check():
-#     return jsonify({"status": "success", "message": "API is running successfully!"}), 200
-
-# @app.route("/mbsa")
-# def mbsa():
-#     return render_template("mbsa.html")
-
-# @app.route('/process-audio', methods=['POST'])
-# def process_audio():
-#     print("GOT THE PROCESS AUDIO REQUEST, ANIKET")
-#     """
-#     Flask endpoint to process audio:
-#     1. Transcribe provided audio file using Whisper AI.
-#     2. Send transcription to Gemini API for recipe information extraction.
-#     3. Return structured data in the response.
-#     """
-    
-#     if 'audio' not in request.files:
-#         return jsonify({"error": "No audio file provided"}), 400
-
-#     audio_file = request.files['audio']
-#     print("AUDIO FILE NAME: ", audio_file)
-    
-#     try:
-#         print("STARTING TRANSCRIPTION, ANIKET")
-#         # Step 1: Transcribe the uploaded audio file directly
-#         audio_file = request.files['audio']
-#         transcription = transcribe_audio(audio_file)
-    
-#         print("BEFORE THE transcription FAILED ERROR, CHECKING IF I GOT THE TRANSCRIPTION", transcription)
-    
-#         if not transcription:
-#             return jsonify({"error": "Audio transcription failed"}), 500
-        
-#         print("GOT THE transcription")
-    
-#         print("Starting the GEMINI REQUEST TO STRUCTURE IT")
-#         # Step 2: Generate structured recipe information using Gemini API
-#         structured_data = query_gemini_api(transcription)
-        
-#         print("GOT THE STRUCTURED DATA", structured_data)
-#         # Step 3: Return the structured data
-#         return jsonify(structured_data)
-    
-#     except Exception as e:
-#         return jsonify({"error": str(e)}), 500
-
-# def transcribe_audio(audio_path):
-#     """
-#     Transcribe audio using Whisper AI.
-#     """
-#     print("CAME IN THE transcribe audio function")
-#     try:
-#         # Transcribe audio using Whisper AI
-#         print("Transcribing audio...")
-#         result = whisper_model.transcribe(audio_path)
-#         print("THE RESULTS ARE", result)
-        
-#         return result.get("text", "").strip()
-
-#     except Exception as e:
-#         print(f"Error in transcription: {e}")
-#         return None
-
-
-# def query_gemini_api(transcription):
-#     """
-#     Send transcription text to Gemini API and fetch structured recipe information.
-#     """
-#     try:
-#         # Define the structured prompt
-#         prompt = (
-#             "Analyze the provided cooking video transcription and extract the following structured information:\n"
-#             "1. Recipe Name: Identify the name of the dish being prepared.\n"
-#             "2. Ingredients List: Extract a detailed list of ingredients with their respective quantities (if mentioned).\n"
-#             "3. Steps for Preparation: Provide a step-by-step breakdown of the recipe's preparation process, organized and numbered sequentially.\n"
-#             "4. Cooking Techniques Used: Highlight the cooking techniques demonstrated in the video, such as searing, blitzing, wrapping, etc.\n"
-#             "5. Equipment Needed: List all tools, appliances, or utensils mentioned, e.g., blender, hot pan, cling film, etc.\n"
-#             "6. Nutritional Information (if inferred): Provide an approximate calorie count or nutritional breakdown based on the ingredients used.\n"
-#             "7. Serving size: In count of people or portion size.\n"
-#             "8. Special Notes or Variations: Include any specific tips, variations, or alternatives mentioned.\n"
-#             "9. Festive or Thematic Relevance: Note if the recipe has any special relevance to holidays, events, or seasons.\n"
-#             f"Text: {transcription}\n"
-#         )
-
-#         # Prepare the payload and headers
-#         payload = {
-#             "contents": [
-#                 {
-#                     "parts": [
-#                         {"text": prompt}
-#                     ]
-#                 }
-#             ]
-#         }
-#         headers = {"Content-Type": "application/json"}
-
-#         # Send request to Gemini API and wait for the response
-#         print("Querying Gemini API...")
-#         response = requests.post(
-#             f"{GEMINI_API_ENDPOINT}?key={GEMINI_API_KEY}",
-#             json=payload,
-#             headers=headers,
-#             timeout=60  # 60 seconds timeout for the request
-#         )
-#         response.raise_for_status()
-
-#         # Extract and return the structured data
-#         data = response.json()
-#         return data.get("candidates", [{}])[0].get("content", {}).get("parts", [{}])[0].get("text", "No result found")
-
-#     except requests.exceptions.RequestException as e:
-#         print(f"Error querying Gemini API: {e}")
-#         return {"error": str(e)}
-
-
-# if __name__ == '__main__':
-#     app.run(debug=True)
-
-
-
-
-
-
-
-# import os
-# import subprocess
-# import whisper
-# import requests
-# import tempfile
-# import warnings
-# import threading
-# from flask import Flask, request, jsonify, send_file, render_template
-
-# from dotenv import load_dotenv
-# import requests
-
-
-
-
-# warnings.filterwarnings("ignore", category=UserWarning, module="whisper")
-
-
-# app = Flask(__name__)
-
-
-# # Gemini API settings
-# load_dotenv()
-# API_KEY = os.getenv("FIRST_API_KEY")
-
-# # Ensure the API key is loaded correctly
-# if not API_KEY:
-#     raise ValueError("API Key not found. Make sure it is set in the .env file.")
-
-# GEMINI_API_ENDPOINT = "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash-latest:generateContent"
-# GEMINI_API_KEY = API_KEY
-
-# # Load Whisper AI model at startup
-# print("Loading Whisper AI model...")
-# whisper_model = whisper.load_model("base")
-# print("Whisper AI model loaded successfully.")
-
-# # Define the "/" endpoint for health check
-# @app.route("/", methods=["GET"])
-# def health_check():
-#     return jsonify({"status": "success", "message": "API is running successfully!"}), 200
-
-
-# def process_video_in_background(video_file, temp_video_file_name):
-#     """
-#     This function is executed in a separate thread to handle the long-running
-#     video processing tasks such as transcription and querying the Gemini API.
-#     """
-#     try:
-#         transcription = transcribe_audio(temp_video_file_name)
-
-#         if not transcription:
-#             print("Audio transcription failed")
-#             return
-
-#         structured_data = query_gemini_api(transcription)
-
-#         # Send structured data back or store it in a database, depending on your use case
-#         print("Processing complete. Structured data:", structured_data)
-
-#     except Exception as e:
-#         print(f"Error processing video: {e}")
-
-#     finally:
-#         # Clean up temporary files
-#         if os.path.exists(temp_video_file_name):
-#             os.remove(temp_video_file_name)
-
-
-# @app.route('/process-video', methods=['POST'])
-# def process_video():
-#     if 'video' not in request.files:
-#         return jsonify({"error": "No video file provided"}), 400
-
-#     video_file = request.files['video']
-
-#     try:
-#         # Save video to a temporary file
-#         with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_video_file:
-#             video_file.save(temp_video_file.name)
-#             print(f"Video file saved: {temp_video_file.name}")
-
-#             # Start the video processing in a background thread
-#             threading.Thread(target=process_video_in_background, args=(video_file, temp_video_file.name)).start()
-
-#             return jsonify({"message": "Video is being processed in the background."}), 202
-
-#     except Exception as e:
-#         return jsonify({"error": str(e)}), 500
-
-
-# def transcribe_audio(video_path):
-#     """
-#     Transcribe audio directly from a video file using Whisper AI.
-#     """
-#     try:
-#         print(f"Transcribing video: {video_path}")
-#         result = whisper_model.transcribe(video_path)
-#         return result['text']
-#     except Exception as e:
-#         print(f"Error in transcription: {e}")
-#         return None
-
-
-# def query_gemini_api(transcription):
-#     """
-#     Send transcription text to Gemini API and fetch structured recipe information.
-#     """
-#     try:
-#         # Define the structured prompt
-#         prompt = (
-#             "Analyze the provided cooking video transcription and extract the following structured information:\n"
-#             "1. Recipe Name: Identify the name of the dish being prepared.\n"
-#             "2. Ingredients List: Extract a detailed list of ingredients with their respective quantities (if mentioned).\n"
-#             "3. Steps for Preparation: Provide a step-by-step breakdown of the recipe's preparation process, organized and numbered sequentially.\n"
-#             "4. Cooking Techniques Used: Highlight the cooking techniques demonstrated in the video, such as searing, blitzing, wrapping, etc.\n"
-#             "5. Equipment Needed: List all tools, appliances, or utensils mentioned, e.g., blender, hot pan, cling film, etc.\n"
-#             "6. Nutritional Information (if inferred): Provide an approximate calorie count or nutritional breakdown based on the ingredients used.\n"
-#             "7. Serving size: In count of people or portion size.\n"
-#             "8. Special Notes or Variations: Include any specific tips, variations, or alternatives mentioned.\n"
-#             "9. Festive or Thematic Relevance: Note if the recipe has any special relevance to holidays, events, or seasons.\n"
-#             f"Text: {transcription}\n"
-#         )
-
-#         payload = {
-#             "contents": [
-#                 {"parts": [{"text": prompt}]}
-#             ]
-#         }
-#         headers = {"Content-Type": "application/json"}
-
-#         # Send request to Gemini API
-#         response = requests.post(
-#             f"{GEMINI_API_ENDPOINT}?key={GEMINI_API_KEY}",
-#             json=payload,
-#             headers=headers
-#         )
-#         response.raise_for_status()
-
-#         # Extract and return the structured data
-#         data = response.json()
-#         return data.get("candidates", [{}])[0].get("content", {}).get("parts", [{}])[0].get("text", "No result found")
-
-#     except requests.exceptions.RequestException as e:
-#         print(f"Error querying Gemini API: {e}")
-#         return {"error": str(e)}
-
-
-# if __name__ == '__main__':
-#     app.run(debug=True)
-