Update src/prediction.py

src/prediction.py

@@ -1,76 +1,73 @@
-import streamlit as st
-import pandas as pd
-import os
-import sys
-from io import BytesIO, StringIO
-import tensorflow as tf
-from PIL import Image
-import numpy as np
-
-# load files
-
-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)
-
-    if st.button('Predict'):
-
-        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.success(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():
-    
-#     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()
+import streamlit as st
+import pandas as pd
+import os
+import sys
+from io import BytesIO, StringIO
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+# load files
+
+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():
+    
+#     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()