| from transformers import AutoProcessor, AutoModelForCausalLM | |
| import gradio as gr | |
| import torch | |
| processor = AutoProcessor.from_pretrained('microsoft/git-base') | |
| model = AutoModelForCausalLM.from_pretrained('./instagram_caption_generating_model') | |
| 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() | |