Update app.py

app.py

@@ -1,15 +1,59 @@
 
 import streamlit as st
-from tensorflow.keras.models import load_model
+from tensorflow.keras.models import load_model, model_from_json
 from tensorflow.keras.preprocessing import image
 import numpy as np
 from PIL import Image
+import requests
+import zipfile
+import os
+
+# Download and extract the filter model files
+@st.cache_resource
+def load_filter_model():
+    # URL of the zipped filter model folder
+    filter_model_url = "https://huggingface.co/username/path-to-folder/resolve/main/model_checkpoint.zip"
+
+    # Download the zipped folder
+    zip_path = "model_checkpoint.zip"
+    with open(zip_path, "wb") as f:
+        f.write(requests.get(filter_model_url).content)
+
+    # Extract the zipped folder
+    extract_dir = "filter_model"
+    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+        zip_ref.extractall(extract_dir)
+
+    # Load the filter model
+    config_path = os.path.join(extract_dir, "config.json")
+    weights_path = os.path.join(extract_dir, "model.weights.h5")
+
+    with open(config_path, "r") as f:
+        config_json = f.read()
+    filter_model = model_from_json(config_json)
+    filter_model.load_weights(weights_path)
+    return filter_model
+
+filter_model = load_filter_model()
+
+# Load the disease models
+@st.cache_resource
+def load_disease_models():
+    corn_model = load_model('corn_model_mobilenetv2.h5')
+    cassava_model = load_model('cassava_disease_model.h5')
+    return corn_model, cassava_model
+
+corn_model, cassava_model = load_disease_models()
+
+# Function to predict using the filter model
+def filter_prediction(img, model):
+    img = image.img_to_array(img)
+    img = np.expand_dims(img, axis=0)
+    img = img / 255.0  # Normalize image
+    prediction = model.predict(img)
+    return np.argmax(prediction)
 
-# Load the models
-corn_model = load_model('corn_model_mobilenetv2.h5')
-cassava_model = load_model('cassava_disease_model.h5')
-
-# Function to predict an uploaded image
+# Function to predict crop disease
 def predict_image(img, model, class_names):
     img = image.img_to_array(img)
     img = np.expand_dims(img, axis=0)
@@ -21,93 +65,76 @@ 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.
-        
+        Welcome to the Crop Disease Identification App. This tool helps 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 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.
-        
-        
+        This application assists farmers in diagnosing common diseases in maize and cassava crops.
+        It uses deep learning models trained on leaf images to identify diseases like blight, common rust, gray leaf spot (maize),
+        bacterial blight, green mottle, brown streak, and mosaic (cassava).
+        If you have questions or feedback, reach out to the developer at:
         E-mail: olorunnisholato7@gmail.com
-        
-        Thank you for using the Crop Disease Identification App!
     """)
-    
 
 def prediction_page():
     st.title("Crop Disease Prediction")
 
-    # Model selection
-    crop_type = st.selectbox("Select the crop:", ["Maize", "Cassava"])
-
-    # File uploader for image input
     uploaded_file = st.file_uploader("Upload an image of the leaf...", type=["jpg", "jpeg", "png"])
 
     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
+        img = Image.open(uploaded_file).resize((128, 128))
         st.image(img, caption='Uploaded Image', use_column_width=True)
-        st.write("")
 
-        # Predict button
         if st.button('Predict'):
-            st.write("Classifying...")
-
-            # Predict based on the selected crop type
-            if crop_type == "Maize":
-                class_names = ['Blight', 'Common_Rust', 'Gray_Leaf_Spot', 'Healthy']
-                predicted_class, class_name = predict_image(img, corn_model, class_names)
-            else:
-                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}")
+            st.write("Classifying with the filter model...")
 
-            # Recommendation based on the predicted class
-            if class_name == 'Healthy' or class_name == 'Cassava_healthy':
-                st.success("The leaf is healthy. No action needed.")
+            # Use the filter model to determine if the image is a leaf
+            filter_result = filter_prediction(img, filter_model)
+            if filter_result == 0:
+                st.error("The uploaded image is not a leaf. Please upload a valid leaf image.")
             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])
+                st.success("The uploaded image is a leaf. Proceeding with disease detection.")
+
+                # Select the crop type
+                crop_type = st.selectbox("Select the crop:", ["Maize", "Cassava"])
+
+                # Predict disease
+                if crop_type == "Maize":
+                    img = img.resize((128, 128))
+                    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))
+                    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}")
+
+                # Recommendation
+                if class_name == 'Healthy' or class_name == 'Cassava_healthy':
+                    st.success("The leaf is healthy. No action needed.")
+                else:
+                    recommendations = {
+                        'Blight': "Remove infected plants and apply fungicide.",
+                        'Common_Rust': "Use resistant varieties and fungicides.",
+                        'Gray_Leaf_Spot': "Ensure crop rotation and apply fungicides.",
+                        'Cassava_bacterial_blight': "Use disease-free planting material.",
+                        'Cassava_brown_streak_disease': "Plant resistant cassava varieties.",
+                        'Cassava_green_mottle': "Ensure good field sanitation.",
+                        'Cassava_mosaic_disease': "Use resistant varieties and good field hygiene."
+                    }
+                    st.warning(recommendations[class_name])
 
 # Main app
 def main():
@@ -123,4 +150,3 @@ def main():
 
 if __name__ == "__main__":
     main()
-