Update app.py

app.py

@@ -24,9 +24,12 @@ def label_data(ranges):
         start = int(start)
         end = int(end)
         print(f"Processing range {i}: start={start}, end={end}, label={label}")
-        if start < 0 or end >= len(global_data):
+        if start < 0 or start >= len(global_data):
             print(f"Invalid range: start={start}, end={end}, label={label}")
             continue
+        if end >= len(global_data):
+            print(f"End index {end} exceeds data length {len(global_data)}. Adjusting to {len(global_data) - 1}.")
+            end = len(global_data) - 1
         global_data.loc[start:end, 'label'] = label
     print("Data after labeling:\n", global_data.tail())
     return global_data.tail()
@@ -34,8 +37,7 @@ def label_data(ranges):
 def preprocess_data():
     global global_data
     try:
-        if 'Unnamed: 0' in global_data.columns:
-            global_data.drop(columns='Unnamed: 0', axis=1, inplace=True)
+        global_data.drop(columns=global_data.columns[0], axis=1, inplace=True)
         global_data.columns = ['raw_eeg', 'label']
         raw_data = global_data['raw_eeg']
         labels_old = global_data['label']
@@ -120,16 +122,43 @@ def preprocess_data():
     except Exception as e:
         print(f"An error occurred during preprocessing: {e}")
         return f"An error occurred during preprocessing: {e}", None, None
+        
+def train_model():
+    global global_data
+    data = preprocess_data(global_data)
+    scaler = StandardScaler()
+    X = data.drop('label', axis=1)
+    y = data['label']
+    X_scaled = scaler.fit_transform(X)
+    X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
+
+    param_grid = {'C': [0.1, 1, 10, 100], 'gamma': ['scale', 'auto', 0.1, 0.01, 0.001, 0.0001], 'kernel': ['rbf']}
+    svc = SVC(probability=True)
+    grid_search = GridSearchCV(estimator=svc, param_grid=param_grid, cv=5, verbose=2, n_jobs=-1)
+    grid_search.fit(X_train, y_train)
+
+    model = grid_search.best_estimator_
+    model_filename = 'model.pkl'
+    scaler_filename = 'scaler.pkl'
+
+    with open(model_filename, 'wb') as file:
+        pickle.dump(model, file)
+
+    with open(scaler_filename, 'wb') as file:
+        pickle.dump(scaler, file)
+
+    return "Training complete! Model and scaler saved.", model_filename, scaler_filename
 
 with gr.Blocks() as demo:
     file_input = gr.File(label="Upload CSV File")
     data_preview = gr.Dataframe(label="Data Preview", interactive=False)
     ranges_input = gr.Dataframe(headers=["Start Index", "End Index", "Label"], label="Ranges for Labeling")
     labeled_data_preview = gr.Dataframe(label="Labeled Data Preview", interactive=False)
+
     preprocessing_status = gr.Textbox(label="Preprocessing Status")
     processed_data_file = gr.File(label="Download Processed Data")
     scaler_file = gr.File(label="Download Scaler")
-    
+
     file_input.upload(get_data_preview, inputs=file_input, outputs=data_preview)
     label_button = gr.Button("Label Data")
     label_button.click(label_data, inputs=[ranges_input], outputs=labeled_data_preview)