delete comment

app.py

@@ -1,46 +1,3 @@
-# from transformers import pipeline, BlipForConditionalGeneration, BlipProcessor, AutoTokenizer, AutoModelForSeq2SeqLM
-# import torchaudio
-# from torchaudio.transforms import Resample
-# import torch
-# from flask import Flask, request, jsonify
-# # from PLI import Image
-# # import pytesseract
-# # import gradio as gr
-
-# app = Flask(__name__)
-
-# # Initialize TTS model from Hugging Face
-# tts_model_name = "suno/bark"
-# tts = pipeline(task="text-to-speech", model=tts_model_name)
-
-# # Initialize Blip model for image captioning
-# model_id = "dblasko/blip-dalle3-img2prompt"
-# blip_model = BlipForConditionalGeneration.from_pretrained(model_id)
-# blip_processor = BlipProcessor.from_pretrained(model_id)
-
-# @app.route('/generate_caption_and_audio', methods=['POST'])
-# def generate_caption ():
-#     try:
-#         # Get image file from the request
-#         image = request.files['image']
-
-#          # Generate caption from image using Blip model
-#         inputs = blip_processor(images=image, return_tensors="pt")
-#         pixel_values = inputs.pixel_values
-#         generated_ids = blip_model.generate(pixel_values=pixel_values, max_length=50)
-#         generated_caption = blip_processor.batch_decode(generated_ids, skip_special_tokens=True, temperature=0.8, top_k=40, top_p=0.9)[0]
-
-#         # Use TTS model to convert generated caption to audio
-#         audio_output = tts(generated_caption)
-#         audio_path = "generated_audio_resampled.wav"
-#         torchaudio.save(audio_path, torch.tensor(audio_output[0]), audio_output["sampling_rate"])
-
-#         return jsonify({'generate_caption': generate_caption, 'audio_path': audio_path})
-#     except Exception as e:
-#         return jsonify({'error': str(e)})
-    
-# if __name__ == '__main__':
-#     app.run(debug=True)
 from flask import Flask, request, jsonify
 from transformers import pipeline, BlipForConditionalGeneration, BlipProcessor
 import torchaudio