Update app.py

app.py

@@ -1,6 +1,5 @@
 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
@@ -12,16 +11,6 @@ def fetch_alpha_vantage_data(api_key):
     alpha_vantage_data = response.json()
     return alpha_vantage_data
 
-def calculate_indicators(data):
-    # Convert relevant columns to numeric
-    numeric_columns = ['4. close', '1. open', '2. high', '3. low']
-    data[numeric_columns] = data[numeric_columns].apply(pd.to_numeric, errors='coerce')
-
-    # 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")
 
@@ -37,24 +26,12 @@ def main():
     df.index = pd.to_datetime(df.index)
     df = df.dropna(axis=0)
 
-    # 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}%")
+    # Print DataFrame for observation
+    st.subheader("Raw Data:")
+    st.write(df)
 
-    # Delete the DataFrame to release memory
-    del df
+    # Uncomment the next line if you want to stop the execution here to observe the data
+    # st.stop()
 
 if __name__ == "__main__":
     main()