Update app.py

app.py

@@ -1,286 +1,3 @@
-# import os
-# import requests
-# import cv2
-# import re
-# from flask import Flask, request, jsonify, render_template
-# from deepgram import DeepgramClient, PrerecordedOptions
-# from dotenv import load_dotenv
-# import tempfile
-# import json
-# import subprocess
-
-
-# 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
-
-# @app.route("/", methods=["GET"])
-# def health_check():
-#     return jsonify({"status": "success", "message": "API is running successfully!"}), 200
-
-
-# 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 download_video(url, temp_video_path):
-#     """Download video (MP4 format) from the given URL and save it to temp_video_path."""
-#     response = requests.get(url, stream=True)
-#     if response.status_code == 200:
-#         with open(temp_video_path, 'wb') as f:
-#             for chunk in response.iter_content(chunk_size=1024):
-#                 f.write(chunk)
-#         print(f"Audio downloaded successfully to {temp_video_path}")
-#     else:
-#         raise Exception(f"Failed to download audio, status code: {response.status_code}")
-
-
-# def preprocess_frame(frame):
-#     """Preprocess the frame for better OCR accuracy."""
-#     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-#     denoised = cv2.medianBlur(gray, 3)
-#     _, thresh = cv2.threshold(denoised, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
-#     return thresh
-
-# def clean_ocr_text(text):
-#     """Clean the OCR output by removing noise and unwanted characters."""
-#     cleaned_text = re.sub(r'[^A-Za-z0-9\s,.!?-]', '', text)
-#     cleaned_text = '\n'.join([line.strip() for line in cleaned_text.splitlines() if len(line.strip()) > 2])
-#     return cleaned_text
-
-# def get_information_from_video_using_OCR(video_path, interval=1):
-#     """Extract text from video frames using OCR and return the combined text content."""
-#     cap = cv2.VideoCapture(video_path)
-#     fps = int(cap.get(cv2.CAP_PROP_FPS))
-#     frame_interval = interval * fps
-#     frame_count = 0
-#     extracted_text = ""
-
-#     print("Starting text extraction from video...")
-
-#     while cap.isOpened():
-#         ret, frame = cap.read()
-#         if not ret:
-#             break
-
-#         if frame_count % frame_interval == 0:
-#             preprocessed_frame = preprocess_frame(frame)
-#             text = pytesseract.image_to_string(preprocessed_frame, lang='eng', config='--psm 6 --oem 3')
-#             cleaned_text = clean_ocr_text(text)
-#             if cleaned_text:
-#                 extracted_text += cleaned_text + "\n\n"
-#                 print(f"Text found at frame {frame_count}: {cleaned_text[:50]}...")
-
-#         frame_count += 1
-
-#     cap.release()
-#     print("Text extraction completed.")
-#     return extracted_text
-
-
-
-
-# @app.route('/process-video', methods=['POST'])
-# def process_video():
-#     if 'videoUrl' not in request.json:
-#         return jsonify({"error": "No video URL provided"}), 400
-
-#     video_url = request.json['videoUrl']
-#     temp_video_path = None
-
-#     try:
-#         # Step 1: Download the WAV file from the provided URL
-#         with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_video_file:
-#             temp_video_path = temp_video_file.name
-#             download_video(video_url, temp_video_path)
-#         interval = 1
-#         # Step 2: get the information from the downloaded MP4 file synchronously
-#         video_info = get_information_from_video_using_OCR(temp_video_path, interval)
-
-#         if not video_info:
-#             video_info = ""
-
-        
-
-#         # Step 2: Convert the MP4 to WAV
-#         with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_wav_file:
-#             temp_wav_path = temp_wav_file.name
-#             convert_mp4_to_wav(temp_video_path, temp_wav_path)
-
-#         audio_info = transcribe_audio(temp_wav_path)
-        
-#         # If no transcription present, use an empty string
-#         if not audio_info:
-#             audio_info = ""
-
-        
-            
-#         # Step 3: Generate structured recipe information using Gemini API synchronously
-#         structured_data = query_gemini_api(video_info, audio_info)
-
-#         return jsonify(structured_data)
-
-#     except Exception as e:
-#         return jsonify({"error": str(e)}), 500
-
-#     finally:
-#         # Clean up temporary audio file
-#         if temp_video_path and os.path.exists(temp_video_path):
-#             os.remove(temp_video_path)
-#             print(f"Temporary audio file deleted: {temp_video_path}")
-
-
-
-
-
-
-# def query_gemini_api(video_transcription, audio_transcription):
-#     """
-#     Send transcription text to Gemini API and fetch structured recipe information synchronously.
-#     """
-#     try:
-#         # Define the structured prompt
-#         prompt = (
-#             "Analyze the provided cooking video and audio transcription combined and based on the combined information 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"
-#             "Also, make sure not to provide anything else or any other information or warning or text apart from the above things mentioned."
-#             f"Text: {audio_transcription}\n"
-#             f"Text: {video_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)
-
-
-
 import os
 import requests
 import cv2
@@ -292,9 +9,8 @@ from dotenv import load_dotenv
 import tempfile
 import json
 import subprocess
-
-
 import warnings
+
 warnings.filterwarnings("ignore", message="FP16 is not supported on CPU; using FP32 instead")
 
 app = Flask(__name__)
@@ -388,7 +104,6 @@ def transcribe_audio(wav_file_path):
         if os.path.exists(wav_file_path):
             os.remove(wav_file_path)
             print(f"Temporary WAV file deleted: {wav_file_path}")
-            
 
 
 def download_video(url, temp_video_path):
@@ -411,7 +126,7 @@ def preprocess_frame(frame):
     return thresh
 
 def clean_ocr_text(text):
-    """Clean the OCR output by removing noise and unwanted characters."""
+    """Clean the OCR output by removing noise and unwanted characters.""" 
     cleaned_text = re.sub(r'[^A-Za-z0-9\s,.!?-]', '', text)
     cleaned_text = '\n'.join([line.strip() for line in cleaned_text.splitlines() if len(line.strip()) > 2])
     return cleaned_text
@@ -494,11 +209,15 @@ def process_video():
         return jsonify({"error": str(e)}), 500
 
     finally:
-        # Clean up temporary video file
+        # Clean up temporary video file and WAV file
         if temp_video_path and os.path.exists(temp_video_path):
             os.remove(temp_video_path)
             print(f"Temporary video file deleted: {temp_video_path}")
 
+        if temp_wav_path and os.path.exists(temp_wav_path):
+            os.remove(temp_wav_path)
+            print(f"Temporary WAV file deleted: {temp_wav_path}")
+
 
 def query_gemini_api(video_transcription, audio_transcription):
     """