Update app.py

app.py

@@ -1,147 +1,4 @@
 # #!/usr/bin/env python3
-# import cv2
-# import base64
-# import time
-# import os
-# import json
-# import argparse
-# import sys
-# from openai import OpenAI
-# from dotenv import load_dotenv
-# from gtts import gTTS
-
-# def main():
-#     # Set up argument parsing
-#     parser = argparse.ArgumentParser(description='Video Explanation Agent')
-#     parser.add_argument('--video-file', required=True, help='Path to the video file to process')
-#     parser.add_argument('--prompt-file', required=True, help='Path to the file containing the explanation prompt')
-#     args = parser.parse_args()
-
-#     # Check if files exist
-#     if not os.path.exists(args.video_file):
-#         print(json.dumps({
-#             "error": f"Video file not found: {args.video_file}"
-#         }))
-#         sys.exit(1)
-
-#     if not os.path.exists(args.prompt_file):
-#         print(json.dumps({
-#             "error": f"Prompt file not found: {args.prompt_file}"
-#         }))
-#         sys.exit(1)
-
-#     # Load environment variables from .env file
-#     load_dotenv()
-
-#     # Get the OpenAI API key from the environment
-#     api_key = os.getenv("OPENAI_API_KEY", "<your OpenAI API key if not set as env var>")
-#     client = OpenAI(api_key=api_key)
-
-#     # Read the custom prompt from the file
-#     with open(args.prompt_file, 'r') as f:
-#         custom_prompt = f.read().strip()
-
-#     # Open the video file
-#     video = cv2.VideoCapture(args.video_file)
-#     if not video.isOpened():
-#         print(json.dumps({
-#             "error": f"Failed to open video file: {args.video_file}"
-#         }))
-#         sys.exit(1)
-
-#     # Extract frames from video
-#     base64Frames = []
-#     frame_count = 0
-#     total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
-    
-#     # Calculate sampling rate to get around 20-30 frames total
-#     if total_frames > 0:
-#         sampling_rate = max(1, total_frames // 25)
-#     else:
-#         sampling_rate = 50  # Default fallback
-    
-#     while video.isOpened():
-#         success, frame = video.read()
-#         if not success:
-#             break
-        
-#         # Only take every Nth frame to reduce processing
-#         if frame_count % sampling_rate == 0:
-#             _, buffer = cv2.imencode(".jpg", frame)
-#             base64Frames.append(base64.b64encode(buffer).decode("utf-8"))
-        
-#         frame_count += 1
-
-#     video.release()
-#     print(f"Processed {len(base64Frames)} frames from {total_frames} total frames.", file=sys.stderr)
-
-#     # Create the timestamp for unique filenames
-#     timestamp = int(time.time())
-#     # Create data directory inside the vidExp-agent directory
-#     output_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data")
-#     os.makedirs(output_dir, exist_ok=True)
-
-#     # Generate explanation based on the custom prompt
-#     PROMPT_MESSAGES = [
-#         {
-#             "role": "user",
-#             "content": [
-#                 f"{custom_prompt}",
-#                 *map(lambda x: {"image": x, "resize": 768}, base64Frames),
-#             ],
-#         },
-#     ]
-
-#     params = {
-#         "model": "gpt-4o-mini",
-#         "messages": PROMPT_MESSAGES,
-#         "max_tokens": 500,
-#     }
-
-#     try:
-#         result = client.chat.completions.create(**params)
-#         explanation = result.choices[0].message.content
-#         print(f"Generated explanation based on provided prompt.", file=sys.stderr)
-#     except Exception as e:
-#         print(json.dumps({
-#             "error": f"Error generating explanation: {str(e)}"
-#         }))
-#         sys.exit(1)
-
-#     # Save the explanation as a text file
-#     explanation_file = os.path.join(output_dir, f"explanation_{timestamp}.txt")
-#     with open(explanation_file, "w") as f:
-#         f.write(explanation)
-
-#     # Generate audio from the explanation
-#     audio_filename = f"explanation_{timestamp}.mp3"
-#     audio_path = os.path.join(output_dir, audio_filename)
-    
-#     try:
-#         # Default to English for TTS
-#         tts = gTTS(text=explanation, lang='en')
-#         tts.save(audio_path)
-#         print(f"Generated audio file.", file=sys.stderr)
-#     except Exception as e:
-#         print(json.dumps({
-#             "error": f"Error generating audio: {str(e)}"
-#         }))
-#         sys.exit(1)
-
-#     # Return the results as JSON
-#     result = {
-#         "success": True,
-#         "explanation": explanation,
-#         "explanationFilePath": explanation_file,
-#         "audioFilename": audio_filename,
-#         "audioFilePath": audio_path
-#     }
-    
-#     print(json.dumps(result))
-
-# if __name__ == "__main__":
-#     main()
-
 
 
 import gradio as gr