Update app.py

app.py

@@ -9,7 +9,7 @@ from tensorflow.keras.models import load_model
 # Print versions for debugging
 st.write("TensorFlow version:", tf.__version__)
 
-# model 
+# model
 out_len = 10  # Replace this with the actual number of output classes
 
 # Ensure no conflicts with 'model' or 'load_model'
@@ -24,12 +24,57 @@ except Exception as e:
 
 # Define the class names
 class_names = [
-    'Tomato_Bacterial_spot', 'Tomato_Early_blight', 'Tomato_Late_blight', 
-    'Tomato_Leaf_Mold', 'Tomato_Septoria_leaf_spot', 'Tomato_Spider_mites_Two_spotted_spider_mite', 
-    'Tomato_Target_Spot', 'Tomato_Tomato_Yellow_Leaf_Curl_Virus', 
-    'Tomato_Tomato_mosaic_virus', 'Tomato_healthy'
+    'Tomato_Bacterial_spot', 'Tomato_Early_blight', 'Tomato_Late_blight',
+    'Tomato_Leaf_Mold', 'Tomato_Septoria_leaf_spot',
+    'Tomato_Spider_mites_Two_spotted_spider_mite', 'Tomato_Target_Spot',
+    'Tomato_Tomato_Yellow_Leaf_Curl_Virus', 'Tomato_Tomato_mosaic_virus',
+    'Tomato_healthy'
 ]
 
+# Define cure and prevention suggestions for each disease
+disease_info = {
+    'Tomato_Bacterial_spot': {
+        'cure': "Remove infected plants, use copper-based fungicides.",
+        'prevention': "Use disease-free seeds, practice crop rotation, avoid overhead irrigation."
+    },
+    'Tomato_Early_blight': {
+        'cure': "Remove infected leaves, apply fungicides.",
+        'prevention': "Mulch around plants, ensure good air circulation, water at the base of plants."
+    },
+    'Tomato_Late_blight': {
+        'cure': "Remove and destroy infected plants, apply fungicides.",
+        'prevention': "Plant resistant varieties, avoid overhead watering, space plants properly."
+    },
+    'Tomato_Leaf_Mold': {
+        'cure': "Improve air circulation, apply fungicides.",
+        'prevention': "Reduce humidity, avoid leaf wetness, use resistant varieties."
+    },
+    'Tomato_Septoria_leaf_spot': {
+        'cure': "Remove infected leaves, apply fungicides.",
+        'prevention': "Mulch around plants, practice crop rotation, avoid overhead watering."
+    },
+    'Tomato_Spider_mites_Two_spotted_spider_mite': {
+        'cure': "Use insecticidal soaps or neem oil, introduce predatory mites.",
+        'prevention': "Keep plants well-watered, increase humidity, use reflective mulches."
+    },
+    'Tomato_Target_Spot': {
+        'cure': "Remove infected leaves, apply fungicides.",
+        'prevention': "Improve air circulation, avoid overhead watering, practice crop rotation."
+    },
+    'Tomato_Tomato_Yellow_Leaf_Curl_Virus': {
+        'cure': "No cure available, remove and destroy infected plants.",
+        'prevention': "Use resistant varieties, control whiteflies, use reflective mulches."
+    },
+    'Tomato_Tomato_mosaic_virus': {
+        'cure': "No cure available, remove and destroy infected plants.",
+        'prevention': "Use disease-free seeds, disinfect tools, control aphids."
+    },
+    'Tomato_healthy': {
+        'cure': "No treatment needed.",
+        'prevention': "Maintain good gardening practices for overall plant health."
+    }
+}
+
 # Function to load and preprocess the image
 def load_and_prep_image(image):
     try:
@@ -53,17 +98,27 @@ if uploaded_file is not None:
         # Display the uploaded image
         image = Image.open(uploaded_file)
         st.image(image, caption='Uploaded Image', use_column_width=True)
-        
+
         # Preprocess the image
         prepped_image = load_and_prep_image(image)
-        
+
         # Ensure the model is loaded and image is preprocessed before making a prediction
         if VIT is not None and prepped_image is not None:
             # Make prediction
             prediction = VIT.predict(prepped_image)
             predicted_class = class_names[np.argmax(prediction)]
-            
+
             # Display the prediction
             st.write(f"Prediction: {predicted_class}")
+
+            # Display cure and prevention suggestions
+            if predicted_class in disease_info:
+                st.subheader("Cure:")
+                st.write(disease_info[predicted_class]['cure'])
+                st.subheader("Prevention:")
+                st.write(disease_info[predicted_class]['prevention'])
+            else:
+                st.write("No specific cure or prevention information available for this condition.")
+
     except Exception as e:
-        st.error(f"Error during prediction: {e}")
+        st.error(f"Error during prediction: {e}")