Update prediction.py

prediction.py

@@ -1,43 +1,44 @@
-import streamlit as st
-from PIL import Image
-import numpy as np
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.image import img_to_array
-
-# Function to preprocess the uploaded image
-def preprocess_image(image):
-    img = image.resize((img_height, img_width))
-    img_array = img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0)
-    img_array /= 255.0
-    return img_array
-
-# Mapping numerical predictions to class labels
-class_labels = {0: "Arborio", 1: "Basmati", 2: "Ipsala", 3: "Jasmine", 4: "Karacadag"}
-
-# Main function to run the Streamlit app
-def main():
-    st.title("Rice Classifier")
-    st.write("Upload a picture of rice to predict its type..")
-
-    # File upload
-    uploaded_file = st.file_uploader("Select the rice image...", type=["jpg", "jpeg", "png"])
-
-    if uploaded_file is not None:
-        # Display the uploaded image
-        image = Image.open(uploaded_file)
-        st.image(image, caption="Gambar yang diunggah", use_column_width=True)
-
-        # Load the model (once the image is uploaded)
-        model = load_model("my_model.keras")
-
-        # Preprocess and predict
-        img_array = preprocess_image(image)
-        prediction = model.predict(img_array)
-        predicted_class = np.argmax(prediction, axis=1)[0]
-
-        # Display prediction
-        st.write(f'Prediksi: {class_labels[predicted_class]}')
-
-if __name__ == "__main__":
-    main()
+import streamlit as st
+from PIL import Image
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import img_to_array
+
+def run():
+    # Function to preprocess the uploaded image
+    def preprocess_image(image):
+        img = image.resize((img_height, img_width))
+        img_array = img_to_array(img)
+        img_array = np.expand_dims(img_array, axis=0)
+        img_array /= 255.0
+        return img_array
+    
+    # Mapping numerical predictions to class labels
+    class_labels = {0: "Arborio", 1: "Basmati", 2: "Ipsala", 3: "Jasmine", 4: "Karacadag"}
+    
+    # Main function to run the Streamlit app
+    def main():
+        st.title("Rice Classifier")
+        st.write("Upload a picture of rice to predict its type..")
+    
+        # File upload
+        uploaded_file = st.file_uploader("Select the rice image...", type=["jpg", "jpeg", "png"])
+    
+        if uploaded_file is not None:
+            # Display the uploaded image
+            image = Image.open(uploaded_file)
+            st.image(image, caption="Gambar yang diunggah", use_column_width=True)
+    
+            # Load the model (once the image is uploaded)
+            model = load_model("my_model.keras")
+    
+            # Preprocess and predict
+            img_array = preprocess_image(image)
+            prediction = model.predict(img_array)
+            predicted_class = np.argmax(prediction, axis=1)[0]
+    
+            # Display prediction
+            st.write(f'Prediksi: {class_labels[predicted_class]}')
+
+if __name__ == "__main__":
+    main()