Update app.py

app.py

@@ -5,27 +5,9 @@ import numpy as np
 from PIL import Image
 
 # Load the models
-filter_model = load_model("model_checkpoint")
 corn_model = load_model('corn_model_mobilenetv2.h5')
 cassava_model = load_model('cassava_disease_model.h5')
 
-# Function to preprocess and predict with the filter model
-def is_leaf_image(img, model, threshold=0.5):
-    img_resized = img.resize((128, 128))  # Ensure correct size for the filter model
-    img_array = np.expand_dims(image.img_to_array(img_resized) / 255.0, axis=0)  # Normalize
-    prediction = model.predict(img_array)
-    
-    # Debugging output
-    st.write(f"Filter Model Prediction Output: {prediction}")
-    
-    # Handle single-value or two-class outputs
-    if len(prediction[0]) == 1:  # Single-class output
-        leaf_probability = prediction[0][0]  # Output is the probability of "Leaf"
-    else:  # Two-class output
-        leaf_probability = prediction[0][1]  # "Leaf" class probability
-
-    return leaf_probability >= threshold, leaf_probability
-
 # Function to predict an uploaded image
 def predict_image(img, model, class_names):
     img = image.img_to_array(img)
@@ -38,33 +20,38 @@ def predict_image(img, model, class_names):
 # Define pages
 def home_page():
     st.title("Maize and Cassava Crop Disease Identification")
+    # Add an image to the home page
     st.image("1093908406.jpg", use_column_width=True)
     st.write("""
         Welcome to the Crop Disease Identification App. This tool aims at helping local farmers
         identify diseases in maize and cassava crops by analyzing images of leaves.
-
+        
         Use the navigation menu to explore the app:
         - **Home:** Overview of the app
         - **About:** Information about the app and its purpose
         - **Prediction:** Upload an image and get a disease diagnosis along with recommendations
     """)
+    
 
 def about_page():
     st.title("About")
+     # Add an image to the about page
     st.image("IMG_20240727_143208_444.jpg", use_column_width=True)
     st.write("""
         This application is designed to assist farmers in diagnosing common diseases in maize and cassava crops.
         By utilizing deep learning models trained on thousands of leaf images, the app can accurately identify
         diseases such as "blight, common rust and gray leaf spot" in maize and "Bacterial blight, green mottle, brown streak and mosaic" in cassava.
-        The models were built using Convolutional Neural Networks (CNNs), trained on publicly available datasets and tested on locally obtained images.
+        The models were built using Convolutional Neural Networks (CNNs), trained on publicly available datasets and tested on locally obtained images..
         The goal is to provide quick and accurate disease identification to help mitigate crop losses and improve yields.
-
+        
         If you have any questions or feedback, please reach out to the developer.
-
+        
+        
         E-mail: olorunnisholato7@gmail.com
-
+        
         Thank you for using the Crop Disease Identification App!
     """)
+    
 
 def prediction_page():
     st.title("Crop Disease Prediction")
@@ -78,43 +65,46 @@ def prediction_page():
     if uploaded_file is not None:
         # Load the uploaded image
         img = Image.open(uploaded_file)
+        # Set image size based on crop type
+        if crop_type == "Maize":
+            img = img.resize((128, 128))  # Resize for Maize
+            target_size = (128, 128)
+        else:
+            img = img.resize((224, 224))  # Resize for Cassava
+            target_size = (224, 224)
+
+        # Display the uploaded image
         st.image(img, caption='Uploaded Image', use_column_width=True)
         st.write("")
 
-        # Check if the image contains a leaf
-        is_leaf, leaf_probability = is_leaf_image(img, filter_model)
+        # Predict button
+        if st.button('Predict'):
+            st.write("Classifying...")
 
-        if not is_leaf:
-            st.error(f"This image does not appear to contain a leaf (Probability: {leaf_probability:.2f}).")
-        else:
-            st.success(f"The image contains a leaf (Probability: {leaf_probability:.2f}).")
-            
-            # Set image size based on crop type
+            # Predict based on the selected crop type
             if crop_type == "Maize":
-                img = img.resize((128, 128))  # Resize for Maize
                 class_names = ['Blight', 'Common_Rust', 'Gray_Leaf_Spot', 'Healthy']
                 predicted_class, class_name = predict_image(img, corn_model, class_names)
             else:
-                img = img.resize((224, 224))  # Resize for Cassava
                 class_names = ['Cassava_bacterial_blight','Cassava_brown_streak_disease','Cassava_green_mottle','Cassava_healthy','Cassava_mosaic_disease']
                 predicted_class, class_name = predict_image(img, cassava_model, class_names)
 
             # Display prediction
             st.write(f"Predicted Class: {class_name}")
 
-            # Recommendations based on prediction
-            recommendations = {
-                'Blight': "Remove infected plants, rotate crops, apply fungicides.",
-                'Common_Rust': "Use resistant varieties, apply fungicides early.",
-                'Gray_Leaf_Spot': "Monitor fields, apply fungicides, manage nitrogen.",
-                'Cassava_bacterial_blight': "Remove infected plants, sterilize tools.",
-                'Cassava_brown_streak_disease': "Plant resistant varieties, manage whiteflies.",
-                'Cassava_green_mottle': "Use resistant varieties, control weeds.",
-                'Cassava_mosaic_disease': "Plant resistant varieties, ensure hygiene.",
-                'Healthy': "The leaf is healthy. No action needed.",
-                'Cassava_healthy': "The leaf is healthy. No action needed."
-            }
-            if class_name in recommendations:
+            # Recommendation based on the predicted class
+            if class_name == 'Healthy' or class_name == 'Cassava_healthy':
+                st.success("The leaf is healthy. No action needed.")
+            else:
+                recommendations = {
+                    'Blight': "Recommendation: Immediately remove and destroy infected plants to prevent the spread of the disease.  Implement a rotation plan that avoids planting corn for at least two years in affected fields.  Apply a fungicide that is effective against blight, such as those containing chlorothalonil or mancozeb, at the early signs of disease.Consider planting blight-resistant corn varieties in future planting seasons.",
+                    'Common_Rust': "Recommendation: Use resistant varieties and apply fungicides if necessary.  If rust is detected early, apply a fungicide like azoxystrobin or tebuconazole to control the spread.  Clear out any crop debris from previous seasons, as rust spores can survive in old plant material.  Avoid overhead irrigation to reduce leaf wetness, which can promote rust development.",
+                    'Gray_Leaf_Spot': "Recommendation: Ensure proper crop rotation and apply fungicides.   Regularly monitor fields during humid conditions, as Gray Leaf Spot thrives in such environments.   Use fungicides like pyraclostrobin or propiconazole at the onset of symptoms.   Ensure balanced nitrogen levels; excessive nitrogen can exacerbate Gray Leaf Spot.   Consider tillage practices that bury crop residues, reducing the amount of fungal spores in the soil.",
+                    'Cassava_bacterial_blight': "Recommendation: Use disease-free planting material and remove affected plants.   Remove and destroy infected plants to prevent the spread.   Avoid excessive irrigation and ensure proper drainage to reduce waterlogging, which can exacerbate CBB.   Sterilize tools and equipment to avoid spreading bacteria from infected plants.",
+                    'Cassava_brown_streak_disease': "Recommendation: Plant CBSD-resistant cassava varieties, such as those developed through breeding programs.    Consider harvesting earlier than usual to reduce yield loss if CBSD symptoms are present.    Manage whiteflies, the primary vector for CBSD, by using insecticides or planting barrier crops.   Isolate infected fields from healthy ones to prevent the spread of the virus",
+                    'Cassava_green_mottle': "Recommendation: Ensure good field sanitation and use resistant varieties.   Select varieties known for resistance to CGM.   Control weeds that may serve as hosts for the CGM virus.",
+                    'Cassava_mosaic_disease': "Recommendation: Use resistant varieties and practice good field hygiene."
+                }
                 st.warning(recommendations[class_name])
 
 # Main app
@@ -130,4 +120,4 @@ def main():
         prediction_page()
 
 if __name__ == "__main__":
-    main()
+    main()