import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoFeatureExtractor
import torch
from PIL import Image
import requests

# Load the tokenizer, model, and feature extractor
model_name = "Salesforce/BLIP-image-captioning-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)

def generate_caption(image):
    inputs = feature_extractor(images=image, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=128, num_beams=4, return_dict_in_generate=True)
    caption = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
    return caption

# Create the Gradio interface
interface = gr.Interface(fn=generate_caption,
                         inputs=gr.inputs.Image(type="pil"),
                         outputs="text",
                         title="Image Captioning with BLIP",
                         description="Upload an image to generate a caption.")

if __name__ == "__main__":
    interface.launch()