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| # import os | |
| # import whisper | |
| # import requests | |
| # import asyncio | |
| # import aiohttp # For making async HTTP requests | |
| # from quart import Quart, request, jsonify, render_template | |
| # from dotenv import load_dotenv | |
| # import warnings | |
| # warnings.filterwarnings("ignore", message="FP16 is not supported on CPU; using FP32 instead") | |
| # app = Quart(__name__) | |
| # print("APP IS RUNNING, ANIKET") | |
| # # 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") | |
| # @app.route("/", methods=["GET"]) | |
| # async def health_check(): | |
| # return jsonify({"status": "success", "message": "API is running successfully!"}), 200 | |
| # @app.route("/mbsa") | |
| # async def mbsa(): | |
| # return await render_template("mbsa.html") | |
| # @app.route('/process-audio', methods=['POST']) | |
| # async def process_audio(): | |
| # print("GOT THE PROCESS AUDIO REQUEST, ANIKET") | |
| # 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 asynchronously | |
| # transcription = await 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 asynchronously | |
| # structured_data = await 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 | |
| # async def transcribe_audio(audio_file): | |
| # """ | |
| # Transcribe audio using Whisper AI (async function). | |
| # """ | |
| # print("CAME IN THE transcribe audio function") | |
| # try: | |
| # with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio_file: | |
| # audio_file.save(temp_audio_file.name) | |
| # print(f"Temporary audio file saved: {temp_audio_file.name}") | |
| # # Run Whisper transcription asynchronously | |
| # loop = asyncio.get_event_loop() | |
| # result = await loop.run_in_executor(None, whisper_model.transcribe, temp_audio_file.name) | |
| # print("THE RESULTS ARE", result) | |
| # return result.get("text", "").strip() | |
| # except Exception as e: | |
| # print(f"Error in transcription: {e}") | |
| # return None | |
| # async def query_gemini_api(transcription): | |
| # """ | |
| # Send transcription text to Gemini API and fetch structured recipe information (async function). | |
| # """ | |
| # 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 asynchronously | |
| # async with aiohttp.ClientSession() as session: | |
| # async with session.post( | |
| # f"{GEMINI_API_ENDPOINT}?key={GEMINI_API_KEY}", | |
| # json=payload, | |
| # headers=headers, | |
| # timeout=60 # 60 seconds timeout for the request | |
| # ) as response: | |
| # response.raise_for_status() # Raise error if response code is not 200 | |
| # data = await response.json() | |
| # return data.get("candidates", [{}])[0].get("content", {}).get("parts", [{}])[0].get("text", "No result found") | |
| # except aiohttp.ClientError as e: | |
| # print(f"Error querying Gemini API: {e}") | |
| # return {"error": str(e)} | |
| # if __name__ == '__main__': | |
| # app.run(debug=True) | |
| # 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 | |
| def health_check(): | |
| return jsonify({"status": "success", "message": "API is running successfully!"}), 200 | |
| def mbsa(): | |
| return render_template("mbsa.html") | |
| 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) | |