app.py

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()