| import gradio as gr |
| from PIL import Image |
| from model import predict |
|
|
| def predict_crop(image, crop_name): |
| if image is None or not crop_name: |
| return {"error": "Image and crop_name are required"} |
|
|
| prediction, confidence = predict(image, crop_name) |
|
|
| return { |
| "prediction": prediction, |
| "confidence": confidence |
| } |
|
|
| gr.Interface( |
| fn=predict_crop, |
| inputs=[ |
| gr.Image(type="pil"), |
| gr.Textbox(label="Crop Name (banana, tomato, rice)") |
| ], |
| outputs="json", |
| api_name="/predict_crop", |
| title="LeafBuddy Crop Disease Detection" |
| ).launch() |
|
|
