| import gradio as gr | |
| from ultralytics import YOLO | |
| import torch | |
| # model = YOLO('best.pt') | |
| path = 'best.pt' | |
| model = torch.hub.load("WongKinYiu/yolov7","custom",path,trust_repo=True) | |
| def predict(input_image): | |
| """ | |
| Predict model output | |
| """ | |
| price = "0" | |
| return [input_image, price] | |
| with gr.Blocks() as demo: | |
| # Title | |
| gr.Markdown( | |
| """ | |
| <h1 align="center">AI Cafeteria Price Evaluator</h1> | |
| """) | |
| # Model Evaluation | |
| # gr.Interface( | |
| # fn=predict, | |
| # inputs=gr.Image(type="pil"), | |
| # outputs=[gr.Image(type="pil", label="Image Prediction"), | |
| # gr.Textbox(type="text", label="Price Prediction")] | |
| # ) | |
| gr.Interface(inputs=["image"],outputs=["image"],fn=lambda img:model(img).render()[0]).launch() | |
| if __name__ == "__main__": | |
| demo.launch() | |