Description of changes

Application/app.py

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+import streamlit as st
+from PIL import Image
+import joblib
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from keras.layers import Dense
+from keras.models import Sequential, load_model
+from tensorflow.keras.layers import BatchNormalization
+from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
+from tensorflow.keras.preprocessing import image as keras_image
+from skimage import io, color, transform
+from skimage.feature import hog
+# from keras.layers import Dense,Flatten,BatchNormalization,Dropout
+from scipy.special import softmax
+
+# ... (Previous imports and model loading code remain the same)
+
+
+def preprocess_image(img, model_name):
+    # if model_name == 'Resnet':
+    #     # Resize image to match ResNet input size (256x256)
+    #     img = img.resize((256, 256))
+    #     img_array = keras_image.img_to_array(img)
+    #     img_array = np.expand_dims(img_array, axis=0)
+    #     img_array = preprocess_input(img_array)
+    #     return img_array
+    if model_name == 'SVM':
+        resized_image = transform.resize(np.array(img), (100, 100), mode='reflect')
+        if resized_image.shape[2] == 4:
+            resized_image = color.rgba2rgb(resized_image)
+        grayscale_image = color.rgb2gray(resized_image)
+        hog_features = hog(grayscale_image, orientations=9, pixels_per_cell=(8, 8),
+                           cells_per_block=(2, 2), block_norm='L2-Hys')
+        return hog_features
+    elif model_name == "ANN" or model_name == "CNN":
+        resized_image = img.resize((256, 256))
+        img_array = keras_image.img_to_array(resized_image)
+        img_array = np.expand_dims(img_array, axis=0)
+        img_array = img_array / 255.0  # Normalize pixel values to be between 0 and 1
+        return img_array
+
+
+
+def predict(model, img, model_name):
+    img_array = preprocess_image(img, model_name)
+
+    # if model_name == 'Resnet':
+    #     predictions = model.predict(img_array)
+    #     decoded_predictions = custom_decode_predictions(predictions)
+    #     return decoded_predictions
+
+    if model_name == 'SVM':
+        # Flatten the array before passing it to the SVM model
+        img_array_flattened = img_array.flatten().reshape(1, -1)
+        prediction = model.predict(img_array_flattened)
+        return prediction[0]
+    elif model_name == 'ANN' or model_name == 'CNN':
+        prediction = model.predict(img_array)
+        confidence_scores = softmax(prediction, axis=1)  # Apply softmax to get confidence scores
+        predicted_class = np.argmax(confidence_scores, axis=1)[0]  # Get the class with the highest confidence
+        confidence = confidence_scores[0, predicted_class]
+        return predicted_class, confidence
+
+def custom_decode_predictions(predictions):
+    # Assuming predictions is a 2D array with shape (1, num_classes)
+    num_classes = predictions.shape[1]
+
+    # Assuming predictions contain class probabilities
+    confidence_scores = predictions[0]
+
+    # Get the index of the class with the highest probability
+    predicted_class_index = np.argmax(confidence_scores)
+
+    # Return the class index, confidence score, and probability distribution
+    return [
+        predicted_class_index,
+    confidence_scores[predicted_class_index],
+    ]
+
+# Load the models
+pper_model_CNN = load_model("../pepper/models/pepper_CNN.h5")
+pper_model_SVM = joblib.load('../pepper/models/pepper_hog_svm_model.pkl')
+# pper_model_resnet = load_model('../pepper/models/pepper_resnet50.h5')
+potato_model_CNN = load_model('../potato/models/potato_CNN.h5')
+potato_model_SVM = joblib.load('../potato/models/potato_hog_svm_model.pkl')
+# potato_model_resnet = load_model('../potato/models/potato_resnet50.h5')
+tomato_model_CNN = load_model('../tomato/models/tomato_CNN.h5')
+tomato_model_SVM = joblib.load('../tomato/models/tomato_hog_svm_model.pkl')
+tomato_model_ANN = load_model('../tomato/models/ANN_tomato.h5')
+# tomato_model_resnet = load_model('../tomato/models/tomato_resnet50.h5')
+Potato_class = 3
+Pepper_class = 2
+Tomato_class = 10
+
+# Set the title of the app
+st.title('Plant Disease Detection App')
+
+# Create a dropdown menu for the user to choose a plant
+plant = st.selectbox('Choose a plant', ('Pepper', 'Potato', 'Tomato'))
+
+# Create a radio button for the user to choose a model
+if plant == 'Pepper':
+    no_of_classes=2
+    class_name = ['pepper_bacterial_spot', 'pepper_bell_healthy']
+    model = st.radio('Choose a model', ('CNN', 'SVM', ))
+    if model == 'SVM':
+        model_func = pper_model_SVM
+    elif model == 'CNN':
+        model_func = pper_model_CNN
+    # elif model == 'Resnet':
+    #     model_func = pper_model_resnet
+elif plant == 'Potato':
+    class_name=['EarlyBlight','Healthy', 'LateBlight']
+    no_of_classes=2
+    model = st.radio('Choose a model', ('SVM', 'CNN', ))
+    if model == 'SVM':
+        model_func =potato_model_SVM
+    elif model == 'CNN':
+        model_func =potato_model_CNN
+    # elif model == 'Resnet':
+    #     model_func = potato_model_resnet
+elif plant == 'Tomato':
+    class_name=['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']
+
+    no_of_classes=10
+    model = st.radio('Choose a model', ('SVM','CNN', 'ANN', ))
+    if model == 'SVM':
+        model_func = tomato_model_SVM
+    elif model == 'CNN':
+        model_func = tomato_model_CNN
+    elif model == 'ANN':
+        model_func = tomato_model_ANN
+    # elif model== 'Resnet':
+    #     model_func = tomato_model_resnet
+
+# Create a file uploader for the user to upload an image
+uploaded_file = st.file_uploader('Upload an image', type='jpg')
+
+# Display the selected model and plant
+st.write('Selected plant:', plant)
+st.write('Selected model:', model)
+
+# If an image is uploaded, display it and make a prediction
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    # st.image(image, caption='Uploaded Image', use_column_width=True)
+    
+    # Make a prediction using the selected model
+    prediction_result = predict(model_func, image, model)
+    print(no_of_classes)
+    # st.write(prediction_result)
+    # Display the result based on the model type
+    if model == 'SVM':
+        st.write(f'Prediction: {prediction_result}')
+    else:
+        predicted_class, confidence = prediction_result
+        st.write(f'Prediction: {class_name[predicted_class]}')
+        st.write(f'Confidence: {confidence}')
+

app.py

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+import streamlit as st
+from PIL import Image
+import joblib
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from keras.layers import Dense
+from keras.models import Sequential, load_model
+from tensorflow.keras.layers import BatchNormalization
+from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
+from tensorflow.keras.preprocessing import image as keras_image
+from skimage import io, color, transform
+from skimage.feature import hog
+# from keras.layers import Dense,Flatten,BatchNormalization,Dropout
+from scipy.special import softmax
+
+# ... (Previous imports and model loading code remain the same)
+
+
+def preprocess_image(img, model_name):
+    # if model_name == 'Resnet':
+    #     # Resize image to match ResNet input size (256x256)
+    #     img = img.resize((256, 256))
+    #     img_array = keras_image.img_to_array(img)
+    #     img_array = np.expand_dims(img_array, axis=0)
+    #     img_array = preprocess_input(img_array)
+    #     return img_array
+    if model_name == 'SVM':
+        resized_image = transform.resize(np.array(img), (100, 100), mode='reflect')
+        if resized_image.shape[2] == 4:
+            resized_image = color.rgba2rgb(resized_image)
+        grayscale_image = color.rgb2gray(resized_image)
+        hog_features = hog(grayscale_image, orientations=9, pixels_per_cell=(8, 8),
+                           cells_per_block=(2, 2), block_norm='L2-Hys')
+        return hog_features
+    elif model_name == "ANN" or model_name == "CNN":
+        resized_image = img.resize((256, 256))
+        img_array = keras_image.img_to_array(resized_image)
+        img_array = np.expand_dims(img_array, axis=0)
+        img_array = img_array / 255.0  # Normalize pixel values to be between 0 and 1
+        return img_array
+
+
+
+def predict(model, img, model_name):
+    img_array = preprocess_image(img, model_name)
+
+    # if model_name == 'Resnet':
+    #     predictions = model.predict(img_array)
+    #     decoded_predictions = custom_decode_predictions(predictions)
+    #     return decoded_predictions
+
+    if model_name == 'SVM':
+        # Flatten the array before passing it to the SVM model
+        img_array_flattened = img_array.flatten().reshape(1, -1)
+        prediction = model.predict(img_array_flattened)
+        return prediction[0]
+    elif model_name == 'ANN' or model_name == 'CNN':
+        prediction = model.predict(img_array)
+        confidence_scores = softmax(prediction, axis=1)  # Apply softmax to get confidence scores
+        predicted_class = np.argmax(confidence_scores, axis=1)[0]  # Get the class with the highest confidence
+        confidence = confidence_scores[0, predicted_class]
+        return predicted_class, confidence
+
+def custom_decode_predictions(predictions):
+    # Assuming predictions is a 2D array with shape (1, num_classes)
+    num_classes = predictions.shape[1]
+
+    # Assuming predictions contain class probabilities
+    confidence_scores = predictions[0]
+
+    # Get the index of the class with the highest probability
+    predicted_class_index = np.argmax(confidence_scores)
+
+    # Return the class index, confidence score, and probability distribution
+    return [
+        predicted_class_index,
+    confidence_scores[predicted_class_index],
+    ]
+
+# Load the models
+pper_model_CNN = load_model("../pepper/models/pepper_CNN.h5")
+pper_model_SVM = joblib.load('../pepper/models/pepper_hog_svm_model.pkl')
+# pper_model_resnet = load_model('../pepper/models/pepper_resnet50.h5')
+potato_model_CNN = load_model('../potato/models/potato_CNN.h5')
+potato_model_SVM = joblib.load('../potato/models/potato_hog_svm_model.pkl')
+# potato_model_resnet = load_model('../potato/models/potato_resnet50.h5')
+tomato_model_CNN = load_model('../tomato/models/tomato_CNN.h5')
+tomato_model_SVM = joblib.load('../tomato/models/tomato_hog_svm_model.pkl')
+tomato_model_ANN = load_model('../tomato/models/ANN_tomato.h5')
+# tomato_model_resnet = load_model('../tomato/models/tomato_resnet50.h5')
+Potato_class = 3
+Pepper_class = 2
+Tomato_class = 10
+
+# Set the title of the app
+st.title('Plant Disease Detection App')
+
+# Create a dropdown menu for the user to choose a plant
+plant = st.selectbox('Choose a plant', ('Pepper', 'Potato', 'Tomato'))
+
+# Create a radio button for the user to choose a model
+if plant == 'Pepper':
+    no_of_classes=2
+    class_name = ['pepper_bacterial_spot', 'pepper_bell_healthy']
+    model = st.radio('Choose a model', ('CNN', 'SVM', ))
+    if model == 'SVM':
+        model_func = pper_model_SVM
+    elif model == 'CNN':
+        model_func = pper_model_CNN
+    # elif model == 'Resnet':
+    #     model_func = pper_model_resnet
+elif plant == 'Potato':
+    class_name=['EarlyBlight','Healthy', 'LateBlight']
+    no_of_classes=2
+    model = st.radio('Choose a model', ('SVM', 'CNN', ))
+    if model == 'SVM':
+        model_func =potato_model_SVM
+    elif model == 'CNN':
+        model_func =potato_model_CNN
+    # elif model == 'Resnet':
+    #     model_func = potato_model_resnet
+elif plant == 'Tomato':
+    class_name=['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']
+
+    no_of_classes=10
+    model = st.radio('Choose a model', ('SVM','CNN', 'ANN', ))
+    if model == 'SVM':
+        model_func = tomato_model_SVM
+    elif model == 'CNN':
+        model_func = tomato_model_CNN
+    elif model == 'ANN':
+        model_func = tomato_model_ANN
+    # elif model== 'Resnet':
+    #     model_func = tomato_model_resnet
+
+# Create a file uploader for the user to upload an image
+uploaded_file = st.file_uploader('Upload an image', type='jpg')
+
+# Display the selected model and plant
+st.write('Selected plant:', plant)
+st.write('Selected model:', model)
+
+# If an image is uploaded, display it and make a prediction
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    # st.image(image, caption='Uploaded Image', use_column_width=True)
+    
+    # Make a prediction using the selected model
+    prediction_result = predict(model_func, image, model)
+    print(no_of_classes)
+    # st.write(prediction_result)
+    # Display the result based on the model type
+    if model == 'SVM':
+        st.write(f'Prediction: {prediction_result}')
+    else:
+        predicted_class, confidence = prediction_result
+        st.write(f'Prediction: {class_name[predicted_class]}')
+        st.write(f'Confidence: {confidence}')
+

requirements.txt

@@ -0,0 +1,81 @@
+absl-py==2.1.0
+altair==5.3.0
+astunparse==1.6.3
+attrs==23.2.0
+blinker==1.8.2
+cachetools==5.3.3
+certifi==2024.2.2
+charset-normalizer==3.3.2
+click==8.1.7
+flatbuffers==24.3.25
+gast==0.5.4
+gitdb==4.0.11
+GitPython==3.1.43
+google-auth==2.29.0
+google-auth-oauthlib==1.2.0
+google-pasta==0.2.0
+grpcio==1.64.0
+h5py==3.11.0
+idna==3.7
+imageio==2.34.1
+Jinja2==3.1.4
+joblib==1.4.2
+jsonschema==4.22.0
+jsonschema-specifications==2023.12.1
+keras==3.3.3
+lazy_loader==0.4
+libclang==18.1.1
+Markdown==3.6
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+mdurl==0.1.2
+ml-dtypes==0.3.2
+namex==0.0.8
+networkx==3.3
+numpy==1.26.4
+oauthlib==3.2.2
+opt-einsum==3.3.0
+optree==0.11.0
+packaging==24.0
+pandas==2.2.2
+pillow==10.3.0
+protobuf==4.25.3
+pyarrow==16.1.0
+pyasn1==0.6.0
+pyasn1_modules==0.4.0
+pydeck==0.9.1
+Pygments==2.18.0
+python-dateutil==2.9.0.post0
+pytz==2024.1
+referencing==0.35.1
+requests==2.32.2
+requests-oauthlib==2.0.0
+rich==13.7.1
+rpds-py==0.18.1
+rsa==4.9
+scikeras==0.12.0
+scikit-image==0.23.2
+scikit-learn==1.5.0
+scipy==1.13.0
+six==1.16.0
+smmap==5.0.1
+streamlit==1.34.0
+tenacity==8.3.0
+tensorboard==2.16.2
+tensorboard-data-server==0.7.2
+tensorflow==2.16.1
+tensorflow-estimator==2.15.0
+tensorflow-io-gcs-filesystem==0.37.0
+tensorflow-macos==2.15.0
+tensorflow-metal==1.1.0
+termcolor==2.4.0
+threadpoolctl==3.5.0
+tifffile==2024.5.10
+toml==0.10.2
+toolz==0.12.1
+tornado==6.4
+typing_extensions==4.11.0
+tzdata==2024.1
+urllib3==2.2.1
+Werkzeug==3.0.3
+wrapt==1.14.1