Update app.py

app.py

@@ -18,14 +18,20 @@ def calculate_indicators(data):
     data = data.apply(pd.to_numeric, errors='coerce')
 
     # Example: Simple condition for doji and inside
-    data['Doji'] = abs(data['Close'] - data['open']) <= 0.01 * (data['high'] - data['low'])
+    data['Doji'] = abs(data['close'] - data['open']) <= 0.01 * (data['high'] - data['low'])
     data['Inside'] = (data['high'] < data['high'].shift(1)) & (data['low'] > data['low'].shift(1))
+
+    # Include the necessary columns from the original dataset
+    data['open'] = data['open_original']
+    data['high'] = data['high_original']
+    data['low'] = data['low_original']
+
     return data
 
 def apply_pca(data, n_components=5):
     pca = PCA(n_components=n_components)
     reduced_data = pca.fit_transform(data)
-    return pd.DataFrame(reduced_data, index=data.index)
+    return pd.DataFrame(reduced_data, index=data.index, columns=[f'component_{i+1}' for i in range(n_components)])
 
 def main():
     st.title("Stock Trend Predictor")
@@ -43,10 +49,10 @@ def main():
     df = df.dropna(axis=0)
 
     # Rename columns
-    df = df.rename(columns={'1. open': 'open', '2. high': 'high', '3. low': 'low', '4. close': 'Close', '5. volume': 'volume'})
+    df = df.rename(columns={'1. open': 'open_original', '2. high': 'high_original', '3. low': 'low_original', '4. close': 'close', '5. volume': 'volume'})
 
     # Apply PCA for dimensionality reduction
-    df_reduced = apply_pca(df)
+    df_reduced = apply_pca(df[['open_original', 'high_original', 'low_original', 'close', 'volume']])
 
     # Calculate indicators on reduced data
     df_reduced = calculate_indicators(df_reduced)