# app.py
from flask import Flask, render_template, request, jsonify
from transformers import pipeline, BlipForConditionalGeneration, BlipProcessor
import torchaudio
from torchaudio.transforms import Resample
import torch
import gradio as gr

app = Flask(__name__)

# Initialize TTS model from Hugging Face
tts_model_name = "Kamonwan/blip-image-captioning-new"
tts = pipeline(task="text-to-speech", model=tts_model_name)

# Initialize Blip model for image captioning
model_id = "Kamonwan/blip-image-captioning-new"
blip_model = BlipForConditionalGeneration.from_pretrained(model_id)
blip_processor = BlipProcessor.from_pretrained(model_id)

def generate_caption(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 generated_caption, audio_path

@app.route('/upload', methods=['POST'])
def generate_caption_api():
    image = request.files['image'].read()
    generated_caption, audio_path = generate_caption(image)
    return jsonify({'generated_caption': generated_caption, 'audio_path': audio_path})

@app.route('/')
def index():
    return render_template("index.html")

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)