update python

app.py

@@ -1,8 +1,54 @@
-from transformers import pipeline, BlipForConditionalGeneration, BlipProcessor, AutoTokenizer, AutoModelForSeq2SeqLM
+# 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
 from torchaudio.transforms import Resample
 import torch
-import gradio as gr
+from io import BytesIO
+
+app = Flask(__name__)
 
 # Initialize TTS model from Hugging Face
 tts_model_name = "suno/bark"
@@ -27,15 +73,15 @@ def generate_caption(image):
 
     return generated_caption, audio_path
 
-# Create a Gradio interface with an image input, a textbox output, a button, and an audio player
-demo = gr.Interface(
-    fn=generate_caption,
-    inputs=gr.Image(),
-    outputs=[
-        gr.Textbox(label="Generated caption"),
-        gr.Button("Converts to Audio"),
-        gr.Audio(type="filepath", label="Generated Audio")
-    ],
-    live=True
-)
-demo.launch(share=True)
+@app.route('/upload', methods=['POST'])
+def upload_image():
+    if 'image' not in request.files:
+        return jsonify({'error': 'No image provided'}), 400
+
+    image_file = request.files['image'].read()
+    generated_caption, audio_path = generate_caption(image_file)
+
+    return jsonify({'generated_caption': generated_caption, 'audio_url': audio_path}), 200
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000, debug=True)