app.py

import gradio as gr
import torch
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_the_image(image):
    semantics = caption_image(images=image)
    return generate_audio(semantics[0]['generated_text'])


gr.close_all()

demo = gr.Interface(fn=caption_the_image,
                    inputs=[gr.Image(label="Select Image",type="pil")],
                    outputs=[gr.Audio(label="Generated Audio")],
                    title="@GenAILearniverse Project 8: Image Captioning with Audio",
                    description="This application uses 'Salesforce/blip-image-captioning-large' and 'kakao-enterprise/vits-ljs' to create text from image and narrate the captioning")
demo.launch()