Update src/streamlit_app.py

src/streamlit_app.py

@@ -115,21 +115,6 @@ def build_cnn_model(input_length, num_classes=3, num_words=10000, embedding_dim=
     model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
     return model
 
-def extract_text_from_file(file):
-    
-    '''Extract text from uploaded file'''
-
-    # read text file
-    if file.type == "text/plain":
-        # To convert to a string based IO:
-        stringio = StringIO(file.getvalue().decode("cp1252"))
-
-        # To read file as string:
-        file_text = stringio.read()
-        
-        return file_text, None
-
-
 #----------------------------------------------------------------------------------------------------
 # Sidebar
 #----------------------------------------------------------------------------------------------------
@@ -181,27 +166,18 @@ elif choose == "CNN":
     st.write("Aplikasi ini menggunakan model CNN untuk mengklasifikasikan data bandwidth menjadi Fake, Genuine, atau No Heavy Activity")
 
     # Upload file
-    training_file = st.file_uploader("Upload Data Training (.txt)", accept_multiple_files=True)
-    
-    if training_file is not None:
-        text_training_file, title_traning = extract_text_from_file(training_file)
-
-    # file_1 = st.sidebar.file_uploader('Upload Data Training (.txt)', type=['txt'],accept_multiple_files=False, key='file_1', label_visibility='hidden')
-
-    real_files = st.file_uploader("Upload Data Real (.txt)", accept_multiple_files=True)
-
-    if real_files is not None:
-        text_real_files, title_real = extract_text_from_file(real_files)
+    training_file = st.file_uploader("Upload Data Training (.txt)", accept_multiple_files=True, label_visibility='hidden')
+    real_files = st.file_uploader("Upload Data Real (.txt)", accept_multiple_files=True, label_visibility='hidden')
 
     # Parameter model
     epochs = st.number_input("Jumlah Epoch", min_value=1, value=2000)
     batch_size = st.number_input("Ukuran Batch", min_value=1, value=32)
   
     if st.button("Proses Data"):
-        if text_training_file and text_real_files:
+        if training_file and real_files:
             # Memproses data training
             try:
-                data_train, labels_train, bandwidth_train = load_data(text_training_file)
+                data_train, labels_train, bandwidth_train = load_data(training_file)
                 if len(data_train) == 0:
                     st.error("Data training tidak valid atau kosong!")
                     st.stop()
@@ -286,7 +262,7 @@ elif choose == "CNN":
             
                 # Memproses data real
                 data_real, labels_real, bandwidth_real = [], [], []
-                for file in text_real_files:
+                for file in real_files:
                     d, lbl, bw = load_data(file)
                     data_real.extend(d)
                     labels_real.extend(lbl)