Update app.py

app.py

@@ -1,48 +1,27 @@
 import streamlit as st
 import yfinance as yf
 import pandas as pd
-from Pandas_Market_Predictor import Pandas_Market_Predictor
 
 def fetch_yfinance_data(symbol):
     data = yf.download(symbol, start="2022-01-01", end="2022-12-31", interval="5m")
     return data
 
-def calculate_indicators(data):
-    # Convert all columns to numeric
-    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['Inside'] = (data['High'] < data['High'].shift(1)) & (data['Low'] > data['Low'].shift(1))
-    return data
-
 def main():
-    st.title("AI Stock Trend Predictor")
+    st.title("Stock Trend Predictor")
+
+    # User input for stock symbol
+    symbol = st.text_input("Enter Stock Symbol (e.g., IBM):")
 
-    # Input for stock symbol
-    symbol = st.text_input("Enter stock symbol (e.g., AAPL):", "AAPL")
+    if not symbol:
+        st.warning("Please enter a valid stock symbol.")
+        st.stop()
 
     # Fetch yfinance data
     stock_data = fetch_yfinance_data(symbol)
 
-    # Calculate indicators
-    stock_data = calculate_indicators(stock_data)
-
-    # Create predictor
-    my_market_predictor = Pandas_Market_Predictor(stock_data)
-
-    # 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("Hold Trend :", trend['HOLD'])
-
-    # Delete the DataFrame to release memory
-    del stock_data
+    # Display the raw data
+    st.subheader("Raw Data:")
+    st.write(stock_data)
 
 if __name__ == "__main__":
     main()