Update app.py

app.py

@@ -1,5 +1,6 @@
 import streamlit as st
 import requests
+from Pandas_Market_Predictor import Pandas_Market_Predictor
 import pandas as pd
 
 # Hard-coded API key for demonstration purposes
@@ -11,6 +12,12 @@ def fetch_alpha_vantage_data(api_key):
     alpha_vantage_data = response.json()
     return alpha_vantage_data
 
+def calculate_indicators(data):
+    # Example: Simple condition for doji and inside
+    data['Doji'] = abs(data['4. close'] - data['1. open']) <= 0.01 * (data['2. high'] - data['3. low'])
+    data['Inside'] = (data['2. high'] < data['3. high']) & (data['2. low'] > data['3. low'])
+    return data
+
 def main():
     st.title("Stock Trend Predictor")
 
@@ -26,12 +33,27 @@ def main():
     df.index = pd.to_datetime(df.index)
     df = df.dropna(axis=0)
 
-    # Print DataFrame for observation
-    st.subheader("Raw Data:")
-    st.write(df)
+    # Rename columns
+    df = df.rename(columns={'1. open': 'open', '2. high': 'high', '3. low': 'low', '4. close': 'close', '5. volume': 'volume'})
+
+    # Calculate indicators
+    df = calculate_indicators(df)
+
+    # Create predictor
+    my_market_predictor = Pandas_Market_Predictor(df)
+
+    # Predict Trend
+    indicators = ["Doji", "Inside"]
+    trend = my_market_predictor.Trend_Detection(indicators, 10)
+
+    # Display results
+    st.subheader("Predicted Trend:")
+    st.write("Buy Trend :", trend['BUY'])
+    st.write("Sell Trend :", trend['SELL'])
+    st.write(f"Standard Deviation Percentage: {my_market_predictor.PERCENT_STD}%")
 
-    # Uncomment the next line if you want to stop the execution here to observe the data
-    # st.stop()
+    # Delete the DataFrame to release memory
+    del df
 
 if __name__ == "__main__":
     main()