shdbfsjdbf

app.py

@@ -107,18 +107,34 @@ elif page == "Model Metrics":
     except:
         st.error("🚨 Model metrics files (`y_true.pth` and `y_pred.pth`) not found!")
 
-# ✅ Disease Predictor Page
+## ✅ Disease Predictor Page
 elif page == "Disease Predictor":
     st.title("🌿 Plant Disease Classifier")
-
-    # File Upload
+    
+    # ✅ App Overview
+    st.write("""
+        This app uses a deep learning model to detect plant diseases from leaf images. 
+        Upload a clear image of a plant leaf, and the model will predict the disease it might have.
+        
+        ### 🏷️ Supported Plant Diseases:
+        - Early Blight
+        - Late Blight
+        - Leaf Mold
+        - Powdery Mildew
+        - Rust
+        - Target Spot
+        - Yellow Leaf Curl Virus
+        - Healthy (No Disease)
+    """)
+
+    # ✅ File Upload
     uploaded_file = st.file_uploader("Upload a plant leaf image", type=["jpg", "png", "jpeg"])
 
     if uploaded_file is not None:
         image = Image.open(uploaded_file)
         st.image(image, caption="Uploaded Image", use_column_width=True)
 
-        # Transform Image
+        # ✅ Transform Image
         transform = transforms.Compose([
             transforms.Resize((128, 128)),
             transforms.ToTensor(),
@@ -127,9 +143,10 @@ elif page == "Disease Predictor":
         
         image_tensor = transform(image).unsqueeze(0)
 
-        # Predict Disease
+        # ✅ Predict Disease
         with torch.no_grad():
             output = model(image_tensor)
             predicted_class = torch.argmax(output, dim=1).item()
 
-        st.write(f"### ✅ Prediction: {CLASS_NAMES[predicted_class]}")
+        st.write(f"### ✅ Prediction: **{CLASS_NAMES[predicted_class]}**")
+        st.success("✔ The prediction is based on a trained deep learning model.")