Upload 3 files

app.py

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+import streamlit as st
+import pandas as pd 
+import numpy as np
+from tensorflow import keras
+import tensorflow as tf
+from keras.models import load_model
+import matplotlib.pyplot as plt
+from PIL import Image
+
+st.title('Plant Disease Recognition')
+
+# import model
+
+model = load_model('plant_model.h5')
+
+# prediction function
+
+def prediction(file):
+    img = tf.keras.utils.load_img(file, target_size=(512, 512))
+    x = tf.keras.utils.img_to_array(img)
+    x = np.expand_dims(x, axis=0)
+
+    # predict the class probabilities
+    
+    classes = model.predict(x)
+
+    # get the predicted class label
+    
+    classes = np.ravel(classes) # convert to 1D array
+    idx = np.argmax(classes)
+    class_name = ['Healthy', 'Powdery', 'Rust'][idx]
+
+    return class_name
+
+# file uploader
+
+uploaded_file = st.file_uploader("Choose a leaf picture:")
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    image = image.resize((240, 240))
+    image = tf.keras.preprocessing.image.img_to_array(image)
+    image = image / 255.0
+    image = tf.expand_dims(image, axis=0)
+  
+# result
+
+if st.button('Predict'): 
+    result = prediction(uploaded_file)
+    st.write('Prediction is {}'.format(result))

plant_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:455355eb14a60440c66483c4f8571daffebca33cda65a562cfe29aac93812a4d
+size 243915080

requirements.txt

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+pandas
+numpy
+scikit-learn
+tensorflow
+keras
+matplotlib