app.py

import torch
import gradio as gr
from PIL import Image
import scipy.io.wavfile as wavfile

# Use a pipeline as a high-level helper
from transformers import pipeline

caption_image = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")

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




def generate_audio(text):
    # Generate the narrated text
    narrated_text = narrator(text)

    # Save the audio to a WAV file
    wavfile.write("output.wav", rate=narrated_text["sampling_rate"],
                  data=narrated_text["audio"][0])
    # Return the path to the saved audio file
    return "output.wav"


def caption_my_image(pil_image):
    semantics = caption_image(images=pil_image)[0]['generated_text']
    return generate_audio(semantics)


demo = gr.Interface(
    fn=caption_my_image,
    inputs=[gr.Image(label="Select Image", type="pil")],
    outputs=[gr.Audio(label="Image Caption")],
    title="Project 07: Image Captioning",
    description="As understood from the title, if not already, this application will caption your image"
)

demo.launch()