| """
|
| EDEN Plant Disease Detection - Gradio Interface
|
| """
|
| import gradio as gr
|
| import torch
|
| from PIL import Image
|
| import json
|
| from torchvision import transforms
|
| from modeling import load_model
|
|
|
|
|
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
| model, labels_data = load_model(".", device)
|
| idx_to_disease = labels_data["idx_to_disease"]
|
|
|
|
|
| transform = transforms.Compose([
|
| transforms.Resize((256, 256)),
|
| transforms.CenterCrop(224),
|
| transforms.ToTensor(),
|
| transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
|
| ])
|
|
|
| def predict_disease(image):
|
| """Predict plant disease from image"""
|
| if image is None:
|
| return {}
|
|
|
|
|
| img_tensor = transform(image).unsqueeze(0).to(device)
|
|
|
|
|
| with torch.no_grad():
|
| outputs = model(img_tensor)
|
| probabilities = torch.softmax(outputs, dim=1)[0]
|
|
|
|
|
| top5_prob, top5_idx = torch.topk(probabilities, 5)
|
|
|
| results = {}
|
| for prob, idx in zip(top5_prob, top5_idx):
|
| disease = idx_to_disease[str(idx.item())]
|
| results[disease] = prob.item()
|
|
|
| return results
|
|
|
|
|
| title = " EDEN Plant Disease Detection"
|
| description = """
|
| Detect plant diseases using AI-powered image analysis.
|
| Upload an image of a plant leaf to identify potential diseases.
|
|
|
| **Model**: Mob-Res (MobileNetV2 + ResNet hybrid)
|
| **Accuracy**: 99.4%
|
| **Diseases**: Multiple common plant diseases
|
| """
|
|
|
| demo = gr.Interface(
|
| fn=predict_disease,
|
| inputs=gr.Image(type="pil", label="Upload Plant Image"),
|
| outputs=gr.Label(num_top_classes=5, label="Disease Predictions"),
|
| title=title,
|
| description=description,
|
| examples=None
|
| )
|
|
|
| if __name__ == "__main__":
|
| demo.launch()
|
|
|
