app.py

import gradio as gr
from PIL import Image
from transformers import pipeline
import scipy.io.wavfile as wavfile
import numpy as np
# import torch

# device = "cuda" if torch.cuda.is_available else "cpu"

# model_path = "C:/Users/ankitdwivedi/OneDrive - Adobe/Desktop/NLP Projects/Video to Text Summarization/Model/models--Salesforce--blip-image-captioning-large/snapshots/2227ac38c9f16105cb0412e7cab4759978a8fd90"
# caption_image = pipeline("image-to-text", model=model_path)
caption_image = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
# tts_model_path = "C:/Users/ankitdwivedi/OneDrive - Adobe/Desktop/NLP Projects/Video to Text Summarization/Model/models--kakao-enterprise--vits-ljs/snapshots/3bcb8321394f671bd948ebf0d086d694dda95464"
# Narrator = pipeline("text-to-speech", model=tts_model_path)
Narrator = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs")

def generate_audio(text):
    Narrated_Text = Narrator(text)
    audio_data = np.array(Narrated_Text["audio"][0])
    sampling_rate = Narrated_Text["sampling_rate"]
    wavfile.write("generated_audio.wav", rate=sampling_rate, data=audio_data)
    return "generated_audio.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="Generated_Audio")],
                    title="Project 8: Audio Caption Image ",
                    description="THIS APPLICATION WILL BE USED TO provide Audio caption for the Image")
demo.launch()