app.py

import gradio as gr
from transformers import BlipProcessor, BlipForConditionalGeneration
from concurrent.futures import ThreadPoolExecutor
import pyttsx3

model_id = "dblasko/blip-dalle3-img2prompt"
model = BlipForConditionalGeneration.from_pretrained(model_id)
processor = BlipProcessor.from_pretrained(model_id)

# Initialize Text-to-Speech engine
tts_engine = pyttsx3.init()

def generate_caption(image):
    # Generate caption from image
    inputs = processor(images=image, return_tensors="pt")
    pixel_values = inputs.pixel_values
    generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
    generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True, temperature=0.8, top_k=40, top_p=0.9)[0]

    # Convert the generated caption to speech
    tts_engine.save_to_file(generated_caption, "generated_audio.mp3")
    tts_engine.runAndWait()

    return generated_caption, "generated_audio.mp3"

# 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("Convert to Audio", None),
    ],
    live=True  # ทำให้ Gradio ทำงานแบบไม่บล็อก
)
demo.launch(share=True)