Update prediction.py

prediction.py

@@ -5,6 +5,12 @@ from tensorflow.keras.models import load_model
 from tensorflow.keras.preprocessing.image import img_to_array
 
 def run():
+    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"])
+
     # Function to preprocess the uploaded image
     def preprocess_image(image):
         img = image.resize((img_height, img_width))
@@ -15,30 +21,24 @@ def run():
     
     # 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 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]}')
+    else:
+        st.text("Please upload an image file")
+        
 if __name__ == "__main__":
     main()