Init app and model

app.py

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+import streamlit as st
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+import matplotlib.pyplot as plt
+import tensorflow_hub as hub
+
+hide_streamlit_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            </style>
+            """
+            
+st.markdown(hide_streamlit_style, unsafe_allow_html = True)
+
+st.title('Plant Disease Prediction')
+st.write("This model is capable of predicting plant disease as a demo")
+
+def main() :
+    file_uploaded = st.file_uploader('Choose an image...', type = 'jpg')
+    if file_uploaded is not None :
+        image = Image.open(file_uploaded)
+        st.write("Uploaded Image.")
+        figure = plt.figure()
+        plt.imshow(image)
+        plt.axis('off')
+        st.pyplot(figure)
+        result, confidence = predict_class(image)
+        st.write('Prediction : {}'.format(result))
+        st.write('Confidence : {}%'.format(confidence))
+
+def predict_class(image) :
+    with st.spinner('Loading Model...'):
+        classifier_model = keras.models.load_model(r'plant_badr_model.h5', compile = False)
+
+    shape = ((200,200,3))
+    model = keras.Sequential([hub.KerasLayer(classifier_model, input_shape = shape)])     # ye bhi kaam kar raha he
+    test_image = image.resize((200, 200))
+    test_image = keras.preprocessing.image.img_to_array(test_image)
+    test_image /= 256.0
+    test_image = np.expand_dims(test_image, axis = 0)
+    class_name = list(range(0, 37))
+    prediction = model.predict_generator(test_image)
+    confidence = round(100 * (np.max(prediction[0])), 2)
+    final_pred = class_name[np.argmax(prediction)]
+    return final_pred, confidence
+
+footer = """
+<style>
+a:link , a:visited{
+    color: white;
+    background-color: transparent;
+    text-decoration: None;
+}
+a:hover,  a:active {
+    color: red;
+    background-color: transparent;
+    text-decoration: None;
+}
+.footer {
+    position: fixed;
+    left: 0;
+    bottom: 0;
+    width: 100%;
+    background-color: transparent;
+    color: black;
+    text-align: center;
+}
+</style>
+"""
+st.markdown(footer, unsafe_allow_html = True) 
+if __name__ == "__main__": 
+    main()

plant_badr_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:884363935c77eb7074fde23ffaa23022d88bb2c2f834cbca3fee1ad27609dfcd
+size 3061008

requirements.txt

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+tensorflow
+numpy
+pillow
+tensorflow_hub