Update app.py

app.py

@@ -1,52 +1,44 @@
 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 tensorflow.keras.models import load_model
 from PIL import Image
 
 st.title('Plant Disease Recognition')
 
-# import model
+# import the model
 
 model = load_model('plant_model.h5')
 
-# prediction function
+# define the preprocessing function
 
-def prediction(file):
-    img = tf.keras.utils.load_img(file, target_size=(240, 240))
-    x = tf.keras.utils.img_to_array(img)
-    x = np.expand_dims(x, axis=0)
+def preprocess_image(image):
+    image = image.resize((240, 240))  # resize the image to the desired dimensions
+    image = image.convert("RGB")  # convert the image to RGB mode if needed
+    image = np.array(image)  # convert the image to a NumPy array
+    image = image / 255.0  # normalize the pixel values to the range of 0 to 1
+    image = np.expand_dims(image, axis=0)  # add an extra dimension for batch size
+    return image
 
-    # predict the class probabilities
-    
-    classes = model.predict(x)
+# define the prediction function
 
-    # 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
+def prediction(image):
+    preprocessed_image = preprocess_image(image)
+    classes = model.predict(preprocessed_image)
+    predicted_class_index = np.argmax(classes)
+    class_labels = ['Healthy', 'Powdery', 'Rust']
+    predicted_class = class_labels[predicted_class_index]
+    return predicted_class
 
 # file uploader
 
 uploaded_file = st.file_uploader("Upload your 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'):
     if uploaded_file is None:
         st.write('Please upload a leaf picture first.')
     else:
-        result = prediction(uploaded_file)
-        st.write('This leaf belongs to {} class.'.format(result))
+        image = Image.open(uploaded_file)
+        result = prediction(image)
+        st.write('This leaf belongs to the {} class.'.format(result))