Update app.py

app.py

@@ -2,13 +2,12 @@ import streamlit as st
 import requests
 from Pandas_Market_Predictor import Pandas_Market_Predictor
 import pandas as pd
-from sklearn.decomposition import PCA
 
 # Hard-coded API key for demonstration purposes
 API_KEY = "QR8F9B7T6R2SWTAT"
 
 def fetch_alpha_vantage_data(api_key):
-    url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol=IBM&interval=60min&apikey={api_key}'
+    url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol=IBM&interval=5min&apikey={api_key}'
     response = requests.get(url)
     alpha_vantage_data = response.json()
     return alpha_vantage_data
@@ -20,19 +19,8 @@ def calculate_indicators(data):
     # Example: Simple condition for doji and inside
     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, columns=[f'component_{i+1}' for i in range(n_components)])
-
 def main():
     st.title("Stock Trend Predictor")
 
@@ -43,22 +31,19 @@ def main():
     alpha_vantage_data = fetch_alpha_vantage_data(api_key)
 
     # Extract relevant data from Alpha Vantage response
-    alpha_vantage_time_series = alpha_vantage_data.get('Time Series (60min)', {})
+    alpha_vantage_time_series = alpha_vantage_data.get('Time Series (5min)', {})
     df = pd.DataFrame(alpha_vantage_time_series).T
     df.index = pd.to_datetime(df.index)
     df = df.dropna(axis=0)
 
     # Rename columns
-    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[['open_original', 'high_original', 'low_original', 'Close', 'volume']])
+    df = df.rename(columns={'1. open': 'open', '2. high': 'high', '3. low': 'low', '4. close': 'Close', '5. volume': 'volume'})
 
-    # Calculate indicators on reduced data
-    df_reduced = calculate_indicators(df_reduced)
+    # Calculate indicators
+    df = calculate_indicators(df)
 
     # Create predictor
-    my_market_predictor = Pandas_Market_Predictor(df_reduced)
+    my_market_predictor = Pandas_Market_Predictor(df)
 
     # Predict Trend
     indicators = ["Doji", "Inside"]