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import gradio as gr
from transformers import BlipProcessor, BlipForConditionalGeneration
from PIL import Image
import torch
# Load model and processor
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
# Function to generate captions
def generate_caption(image):
image = Image.open(image).convert("RGB")
inputs = processor(image, return_tensors="pt")
outputs = model.generate(**inputs)
caption = processor.decode(outputs[0], skip_special_tokens=True)
return caption
# Create Gradio Interface
iface = gr.Interface(
fn=generate_caption,
inputs=gr.Image(type="pil"),
outputs="text",
title="AI Image Caption Generator",
description="Upload an image and get an AI-generated caption.",
)
# Run app
iface.launch()
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