Create app.py

app.py

@@ -0,0 +1,44 @@
+import gradio as gr
+import torch
+from torchvision import models, transforms
+from PIL import Image
+import json
+import os
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model_path = "plant_disease_model/model.pth"
+class_names_path = "plant_disease_model/class_names.json"
+
+model_disease = models.mobilenet_v3_small(pretrained=False)
+model_disease.classifier[3] = torch.nn.Linear(model_disease.classifier[3].in_features, 38)
+model_disease.load_state_dict(torch.load(model_path, map_location=device))
+model_disease.to(device)
+model_disease.eval()
+
+with open(class_names_path, 'r') as f:
+    class_names = json.load(f)
+
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor()
+])
+
+def predict_disease(image_path):
+    image = Image.open(image_path).convert("RGB")
+    img_tensor = transform(image).unsqueeze(0).to(device)
+    with torch.no_grad():
+        outputs = model_disease(img_tensor)
+        _, predicted = torch.max(outputs, 1)
+    return f"🌿 Predicted Disease: *{class_names[str(predicted.item())]}*"
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🌱 AgroVision: Smart Assistant for Farmers")
+    with gr.TabItem("🦠 Plant Disease Detection"):
+            gr.Markdown("### Upload a crop leaf image to detect disease")
+            image_input = gr.Image(type="filepath")
+            disease_btn = gr.Button("Detect Disease")
+            disease_output = gr.Markdown()
+            disease_btn.click(fn=predict_disease, inputs=image_input, outputs=disease_output)
+
+demo.launch()