Create app.py

app.py

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+import os
+import streamlit as st
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+import numpy as np
+import pickle
+
+# Disable oneDNN custom operations
+os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
+
+# Ensure TensorFlow uses CPU
+import tensorflow as tf
+tf.config.set_visible_devices([], 'GPU')
+
+# Load the saved model
+@st.cache_resource
+def load_keras_model():
+    return load_model("best_model.h5")
+
+model = load_keras_model()
+
+# Load the class labels from a pickle file
+with open("mod_class_labels.pkl", "rb") as f:
+    class_indices = pickle.load(f)
+
+# Ensure class_indices is a dictionary
+if isinstance(class_indices, list):
+    class_indices = {i: label for i, label in enumerate(class_indices)}
+
+# Function to preprocess the image
+def preprocess_image(image):
+    image = load_img(image, target_size=(256, 256))  # Load the image with target size
+    image = img_to_array(image)  # Convert the image to array
+    image = np.expand_dims(image, axis=0)  # Expand dimensions to match the input shape
+    image = image / 255.0  # Rescale the image
+    return image
+
+# Function to make a prediction and get the label
+def predict_image(image):
+    image = preprocess_image(image)
+    prediction = model.predict(image)
+    predicted_class = np.argmax(prediction, axis=1)[0]
+    predicted_label = class_indices[predicted_class]
+    return predicted_label
+
+# Streamlit App
+st.title("Rice Leaf Disease Classification")
+
+st.write("Upload an image of a rice leaf and the model will predict its disease category.")
+
+# File uploader
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    image = load_img(uploaded_file, target_size=(256, 256))
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+    st.write("")
+    st.write("Classifying...")
+
+    # Make a prediction
+    predicted_label = predict_image(uploaded_file)
+    st.write(f"Predicted label: {predicted_label}")