Create app.py

app.py

@@ -0,0 +1,115 @@
+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 38 different classes of plant diseases")
+
+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'final1_model.h5', compile = False)
+
+    shape = ((255,255,3))
+    model = keras.Sequential([hub.KerasLayer(classifier_model, input_shape = shape)])     # ye bhi kaam kar raha he
+    test_image = image.resize((255, 255))
+    test_image = keras.preprocessing.image.img_to_array(test_image)
+    test_image /= 255.0
+    test_image = np.expand_dims(test_image, axis = 0)
+    class_name = ["Apple___Apple_scab","Apple___Black_rot",
+              "Apple___Cedar_apple_rust","Apple___healthy",
+              "Blueberry___healthy",
+              "Cherry_(including_sour)___Powdery_mildew",
+              "Cherry___healthy",
+              "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot", 
+              "Corn_(maize)___Common_rust_",
+              "Corn_(maize)___Northern_Leaf_Blight",
+              "Corn_(maize)___healthy","Grape___Black_rot",
+              "Grape___Esca_(Black_Measles)",
+              "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)",
+              "Grape___healthy",
+              "Orange___Haunglongbing_(Citrus_greening)",
+              "Peach___Bacterial_spot",
+              "Peach___healthy",
+              "Pepper__bell___Bacterial_spot",
+              "Pepper,_bell___healthy",
+              "Potato___Early_blight",
+              "Potato___Late_blight",
+              "Potato___healthy",
+              "Raspberry___healthy",
+              "Soybean___healthy",
+              "Squash___Powdery_mildew",
+              "Strawberry___Leaf_scorch",
+              "Strawberry___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_Yellow_Leaf_Curl_Virus",
+              "Tomato___Tomato_mosaic_virus",
+              "Tomato___healthy"]
+    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;
+}
+
+<div class="footer">
+<p align="center"> Developed with ❤ by Mato</p>
+</div>
+</style>
+"""
+st.markdown(footer, unsafe_allow_html = True) 
+if __name__ == "__main__": 
+    main()