from transformers import AutoProcessor, AutoModelForCausalLM
import gradio as gr
import torch

processor = AutoProcessor.from_pretrained('microsoft/git-base')
model = AutoModelForCausalLM.from_pretrained('./')

def predict(image):
    try:
        inputs = processor(images=image, return_tensors="pt")

        device = "cuda" if torch.cuda.is_available() else "cpu"
        inputs = {key: value.to(device) for key, value in inputs.items()}
        model.to(device)

        outputs = model.generate(**inputs)

        caption = processor.batch_decode(outputs, skip_special_tokens=True)[0]
        
        return caption

    except Exception as e:
        print("Error during prediction:", str(e))
        return "Error: " + str(e)

with gr.Blocks() as demo:
    image = gr.Image(type="pil")
    predict_btn = gr.Button("Predict", variant="primary")
    output = gr.Textbox(label="Generated Caption")

    inputs = [image]
    outputs = [output]
    
    predict_btn.click(predict, inputs=inputs, outputs=outputs)

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