Create app.py

app.py

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+import streamlit as st
+import requests
+from Pandas_Market_Predictor import Pandas_Market_Predictor
+import pandas as pd
+
+def fetch_alpha_vantage_data(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
+
+def main():
+    st.title("Stock Trend Predictor")
+
+    # Get Alpha Vantage API key from user input
+    api_key = st.text_input("Enter your Alpha Vantage API key:")
+
+    # Fetch Alpha Vantage data
+    if api_key:
+        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 (5min)', {})
+        df = pd.DataFrame(alpha_vantage_time_series).T
+        df.index = pd.to_datetime(df.index)
+        df = df.dropna(axis=0)
+
+        # Create predictor
+        my_market_predictor = Pandas_Market_Predictor(df)
+
+        # Predict Trend
+        indicators = ["Indicator1", "Indicator2"]
+        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}%")
+
+        # Delete the DataFrame to release memory
+        del df
+
+if __name__ == "__main__":
+    main()