M-A-Z/Image-Generator

1import gradio as gr2from transformers import BlipProcessor, BlipForConditionalGeneration3from PIL import Image4import torch5 6# Load BLIP model & processor7processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")8model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")9 10def generate_caption(image):11    inputs = processor(image, return_tensors="pt")12    out = model.generate(**inputs)13    caption = processor.decode(out[0], skip_special_tokens=True)14    return caption15 16# Gradio UI17demo = gr.Interface(18    fn=generate_caption,19    inputs=gr.Image(type="pil"),20    outputs=gr.Textbox(),21    title="🖼️ Image to Text Generator",22    description="Upload an image and get a caption generated using BLIP model.",23)24 25if __name__ == "__main__":26    demo.launch()27