app.py

import torch
from transformers import pipeline
from PIL import Image
from scipy.io import wavfile
import gradio as gr
import numpy as np
import os
import requests
# Specify the device (CPU or GPU)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load the image-to-text pipeline
caption_image = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large", device=device)
# Load the image-to-text pipeline with the vit-gpt2 model
#caption_pipeline = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning", device=device)

# Load the text-to-speech pipeline
narrator = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs", device=device)

# List of local image paths
example_images = ["image1.jpeg", "image2.jpeg", "image3.jpeg"]

def process_image(image):
    # Generate the caption
    caption = caption_image(image)[0]['generated_text']

    # Generate speech from the caption
    speech = narrator(caption)

    # Convert the audio to PCM format
    audio_data = np.array(speech["audio"][0] * 32767, dtype=np.int16)

    # Save the audio to a WAV file
    audio_path = "caption.wav"
    wavfile.write(audio_path, rate=speech["sampling_rate"], data=audio_data)

    return caption, audio_path

# Create Gradio interface
iface = gr.Interface(
    fn=process_image,
    inputs=gr.Image(type="pil"),
    outputs=[gr.Textbox(label="Generated Caption"), gr.Audio(label="Generated Audio", type="filepath")],
    examples=example_images
)

# Launch the interface
iface.launch()