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app.py

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+import streamlit as st
+from PIL import Image
+import pandas as pd
+import numpy as np
+from keras.models import load_model
+model = load_model('./model.h5')
+
+# Classification report data
+data = {
+    "classes": ['Pepper__bell___Bacterial_spot',
+                    'Pepper__bell___healthy',
+                    'Potato___Early_blight',
+                    'Potato___Late_blight',
+                    'Potato___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_YellowLeaf__Curl_Virus',
+                    'Tomato__Tomato_mosaic_virus',
+                    'Tomato_healthy'],
+    "precision": [0.99, 0.99, 0.98, 0.83, 0.41, 0.99, 0.74, 1.00, 1.00, 0.91, 0.95, 0.60, 1.00, 0.87, 0.91],
+    "recall": [0.99, 0.86, 1.00, 0.99, 1.00, 0.74, 0.90, 0.83, 0.71, 0.97, 0.64, 0.99, 0.98, 0.98, 1.00],
+    "f1-score": [0.99, 0.92, 0.99, 0.90, 0.58, 0.85, 0.82, 0.90, 0.83, 0.94, 0.77, 0.74, 0.99, 0.92, 0.95],
+    "support": [199, 296, 213, 205, 28, 399, 206, 379, 187, 359, 326, 290, 629, 81, 331]
+}
+
+# Create a DataFrame
+df = pd.DataFrame(data)
+
+def get_predictions(immg):
+    # from skimage import io
+    # from keras.preprocessing import image
+    import keras.utils as image
+    #path='imbalanced/Scratch/Scratch_400.jpg'
+    # "C:\Users\prati\Desktop\AML 3104 final project\PlantVillage\Pepper__bell___Bacterial_spot\00f2e69a-1e56-412d-8a79-fdce794a17e4___JR_B.Spot 3132.JPG"
+    # pth = immg
+    # show_img=image.load_img(pth, grayscale=False, target_size=(200, 200))
+    disease_class = data["classes"]
+
+    x = image.img_to_array(immg)
+    x = np.expand_dims(x, axis = 0)
+    x = np.array(x, 'float32')
+    x /= 255
+    
+    custom = model.predict(x)
+    print(custom[0])
+
+    # x = x.reshape([64, 64])
+
+    #plt.gray()
+    # plt.imshow(show_img)
+    # plt.show()
+
+    a=custom[0]
+    ind=np.argmax(a)
+            
+    print('Prediction:',disease_class[ind])
+    return(disease_class[ind])
+
+
+#User Interface---------------------------------------------------------
+
+
+pred_flag = False
+def main():
+    st.label_visibility='collapse'
+    st.title('Plant desise identification')
+    st.write('# Detecting plant desise using deep learnings dense net model')
+    # Display the DataFrame as a table in Streamlit
+    st.header('Evaluation Report:')
+    
+
+    # Create two columns
+    col1, col2 = st.columns(2)
+
+    # Add content to the first column
+    with col1:
+        st.write('Classification Report:')
+        st.table(df)
+
+    # Add content to the second column
+    with col2:
+        st.write('Confussion matrix')
+        st.image("./confussion_matrix.png",width=650)
+
+    uploaded_file = st.file_uploader("", type=['jpg','png','jpeg'])
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.markdown('<p style="text-align: center;"><label>Image : </label></p>',unsafe_allow_html=True)
+        st.image(image,width=500)
+        # image = Image.open()
+    if st.button("Predict"):
+        resized_image = image.resize((64, 64))
+        pred_cls = get_predictions(resized_image)
+        st.markdown('<p style="text-align: center;"><label>Prediction : </label></p>',unsafe_allow_html=True)
+        # st.image(image,width=900)
+        st.markdown(pred_cls)
+        # result =''
+        # st.success('The output is {}'.format(result))
+if __name__ == '__main__': #
+    main()

final_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7a37375a2e143e19e773290e5541d3c6426726674a937fa76697e35a037914fa
+size 88637056

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1ad27f74d08d9864cd3574826841909ddc8a4dc902b1e72069f27888b75dfa2a
+size 88637056

requirements.txt

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+tensorflow==2.11.0
+keras==2.11.0
+sklearn==0.0
+Pillow==9.2.0
+pandas==1.4.3
+streamlit==1.15.1
+protobuf==3.19.0
+numpy