Spaces:
Running
Running
| import os | |
| import whisper | |
| import requests | |
| from flask import Flask, request, jsonify, render_template | |
| from dotenv import load_dotenv | |
| from deepgram import DeepgramClient, PrerecordedOptions | |
| import tempfile | |
| import json | |
| import warnings | |
| warnings.filterwarnings("ignore", message="FP16 is not supported on CPU; using FP32 instead") | |
| app = Flask(__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") | |
| DEEPGRAM_API_KEY = os.getenv("SECOND_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.") | |
| if not DEEPGRAM_API_KEY: | |
| raise ValueError("DEEPGRAM_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") | |
| 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") | |
| 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) | |
| temp_audio_path = None | |
| try: | |
| print("STARTING TRANSCRIPTION, ANIKET") | |
| # Step 1: Save the audio file temporarily to a specific location | |
| with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as temp_audio_file: | |
| temp_audio_path = temp_audio_file.name # Get the file path | |
| temp_audio_file.write(audio_file.read()) # Write the uploaded audio to the temp file | |
| print(f"Temporary audio file saved at: {temp_audio_path}") | |
| # Step 2: Transcribe the uploaded audio file synchronously | |
| transcription = transcribe_audio(temp_audio_path) | |
| 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") | |
| # Step 3: Generate structured recipe information using Gemini API synchronously | |
| print("Starting the GEMINI REQUEST TO STRUCTURE IT") | |
| structured_data = query_gemini_api(transcription) | |
| print("GOT THE STRUCTURED DATA", structured_data) | |
| # Step 4: Return the structured data | |
| return jsonify(structured_data) | |
| except Exception as e: | |
| return jsonify({"error": str(e)}), 500 | |
| finally: | |
| # Clean up the temporary WAV file | |
| if temp_audio_path and os.path.exists(temp_audio_path): | |
| os.remove(temp_audio_path) | |
| print(f"Temporary WAV file deleted: {temp_audio_path}") | |
| def transcribe_audio(wav_file_path): | |
| """ | |
| Transcribe audio from a video file using Deepgram API synchronously. | |
| Args: | |
| wav_file_path (str): Path to save the converted WAV file. | |
| Returns: | |
| dict: A dictionary containing status, transcript, or error message. | |
| """ | |
| print("Entered the transcribe_audio function") | |
| try: | |
| # Initialize Deepgram client | |
| deepgram = DeepgramClient(DEEPGRAM_API_KEY) | |
| # Open the converted WAV file | |
| with open(wav_file_path, 'rb') as buffer_data: | |
| payload = {'buffer': buffer_data} | |
| # Configure transcription options | |
| options = PrerecordedOptions( | |
| smart_format=True, model="nova-2", language="en-US" | |
| ) | |
| # Transcribe the audio | |
| response = deepgram.listen.prerecorded.v('1').transcribe_file(payload, options) | |
| # Check if the response is valid | |
| if response: | |
| print("Request successful! Processing response.") | |
| # Convert response to JSON string | |
| try: | |
| data_str = response.to_json(indent=4) | |
| except AttributeError as e: | |
| return {"status": "error", "message": f"Error converting response to JSON: {e}"} | |
| # Parse the JSON string to a Python dictionary | |
| try: | |
| data = json.loads(data_str) | |
| except json.JSONDecodeError as e: | |
| return {"status": "error", "message": f"Error parsing JSON string: {e}"} | |
| # Extract the transcript | |
| try: | |
| transcript = data["results"]["channels"][0]["alternatives"][0]["transcript"] | |
| except KeyError as e: | |
| return {"status": "error", "message": f"Error extracting transcript: {e}"} | |
| print(f"Transcript obtained: {transcript}") | |
| # Step: Save the transcript to a text file | |
| transcript_file_path = "transcript_from_transcribe_audio.txt" | |
| with open(transcript_file_path, "w", encoding="utf-8") as transcript_file: | |
| transcript_file.write(transcript) | |
| print(f"Transcript saved to file: {transcript_file_path}") | |
| return transcript | |
| else: | |
| return {"status": "error", "message": "Invalid response from Deepgram."} | |
| except FileNotFoundError: | |
| return {"status": "error", "message": f"Video file not found: {wav_file_path}"} | |
| except Exception as e: | |
| return {"status": "error", "message": f"Unexpected error: {e}"} | |
| finally: | |
| # Clean up the temporary WAV file | |
| if os.path.exists(wav_file_path): | |
| os.remove(wav_file_path) | |
| print(f"Temporary WAV file deleted: {wav_file_path}") | |
| def query_gemini_api(transcription): | |
| """ | |
| Send transcription text to Gemini API and fetch structured recipe information synchronously. | |
| """ | |
| try: | |
| # Define the structured prompt | |
| prompt = ( | |
| "Print the transcription in the response as well" | |
| "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 synchronously | |
| response = requests.post( | |
| f"{GEMINI_API_ENDPOINT}?key={GEMINI_API_KEY}", | |
| json=payload, | |
| headers=headers, | |
| ) | |
| # Raise error if response code is not 200 | |
| response.raise_for_status() | |
| 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) | |
| # # 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) | |