Update src/prediction.py

src/prediction.py

@@ -14,60 +14,23 @@ model=tf.keras.models.load_model('./src/best_model_sore.keras')
 klas = ['baseball_cap', 'beanie_hat', 'bucket_hat', 'fedora_hat', 'flat_cap']
 
 st.title('Jenis Topi')
-
-picup = st.file_uploader('Upload a picture', type=['jpg', 'jpeg', 'png'])
-
-if picup is not None:
-    st.image(picup, caption='Uploaded Image', use_column_width=True)
-
-    img = Image.open(picup).convert('RGB')
-    img = img.resize((400,400))
-    img_array = np.array(img)
-    img_array = np.expand_dims(img_array, axis=0)
-
-    prediction = model.predict(img_array)
-    pred_index = np.argmax(prediction)
-    pred_class = klas[pred_index]
-    confidence = prediction[0][pred_index]*100
-    st.write(f'Prediction: **{pred_class}** ({confidence:.2f}% confidence)')
-
-# class picupload(object):
-
-#     def __init__(self):
-#         self.fileTypes=['jpg']
-
-#     def run(self):
-#         topi = st.file_uploader('Upload picture', type=self.fileTypes)
-#         show_file = st.empty()
-#         if not topi:
-#             show_file.info('Please upload a picture')
-#             return
-#         content = topi.getvalue()
-#         if isinstance(topi, BytesIO):
-#             show_file.image(topi)
-#         topi.close()
-
-# if __name__ == '__main__':
-#     helper = picupload()
-#     helper.run()
-
-# def run():
+def run():
+    picup = st.file_uploader('Upload a picture', type=['jpg', 'jpeg', 'png'])
     
-#     with st.form(key='type-of-cap'):
-
-#         st.write('##Topi')
-
-#         topi = st.file_uploader('Upload file', type=['jpg'])
-#         show_topi = st.empty()
-
-#         if not topi:
-#             show_topi.info('Please upload a picture : {}'.format(' '.join(['jpg'])))
-#             return
-#         content = topi.getvalue()
-
-#         if isinstance(topi, BytesIO):
-#             show_topi.image(topi)
-#         topi.close()
-
-# run()
-        
+    if picup is not None:
+        st.image(picup, caption='Uploaded Image', use_column_width=True)
+    
+        img = Image.open(picup).convert('RGB')
+        img = img.resize((400,400))
+        img_array = np.array(img)
+        img_array = np.expand_dims(img_array, axis=0)
+    
+        prediction = model.predict(img_array)
+        pred_index = np.argmax(prediction)
+        pred_class = klas[pred_index]
+        confidence = prediction[0][pred_index]*100
+        st.write(f'Prediction: **{pred_class}** ({confidence:.2f}% confidence)')
+        st.success(f'Prediction: **{pred_class}** ({confidence:.2f}% confidence)')
+
+if __name__ == '__main__':
+    run()