Update app.py

app.py

@@ -1,99 +1,27 @@
-# Import necessary libraries
-import pandas as pd
 import yfinance as yf
-from sklearn.model_selection import train_test_split
-from sklearn.ensemble import RandomForestRegressor
-from sklearn.metrics import mean_squared_error
 import streamlit as st
-from datetime import datetime, timedelta, timezone
+import pandas as pd
 
 # Function to fetch historical stock data
 def fetch_stock_data(symbol, start_date, end_date):
     data = yf.download(symbol, start=start_date, end=end_date)
     return data
 
-# Function to create features for the model
-def create_features(data):
-    data['Datetime'] = pd.to_datetime(data['Datetime'])  # Replace 'Date' with the actual column name
-
-    data['Year'] = data['Datetime'].dt.year
-    data['Month'] = data['Datetime'].dt.month
-    data['Day'] = data['Datetime'].dt.day
-    data['Hour'] = data['Datetime'].dt.hour
-    data['Minute'] = data['Datetime'].dt.minute
-
-    return data
-
-# Function to train a machine learning model
-def train_model(data):
-    features = ['Year', 'Month', 'Day', 'Hour', 'Minute']
-    target = 'Close'
-
-    if len(data) == 0:
-        st.write("Not enough data for training.")
-        return None
-
-    X = data[features]
-    y = data[target]
-
-    if len(data) <= 1:
-        st.write("Not enough data for splitting.")
-        return None
-
-    try:
-        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-        if len(X_train) == 0 or len(X_test) == 0:
-            st.write("Not enough data after splitting.")
-            return None
-
-        model = RandomForestRegressor()
-        model.fit(X_train, y_train)
-
-        # Evaluate the model
-        predictions = model.predict(X_test)
-        mse = mean_squared_error(y_test, predictions)
-        st.write(f"Mean Squared Error: {mse}")
-
-        return model
-
-    except ValueError as e:
-        st.write(f"Error during train-test split: {e}")
-        return None
-
 # Streamlit UI
 def main():
-    st.title("Stock Price Prediction Tool")
+    st.title("Stock Data Viewer")
 
     symbol = st.text_input("Enter Stock Symbol (e.g., AAPL):")
     start_date = st.date_input("Select Start Date:")
     end_date = st.date_input("Select End Date:")
 
-    if st.button("Predict Stock Price"):
+    if st.button("Fetch Stock Data"):
         # Fetch stock data
         stock_data = fetch_stock_data(symbol, start_date, end_date)
 
-        # Create features
-        stock_data = create_features(stock_data)
-
-        # Train the model
-        model = train_model(stock_data)
-
-        if model:
-            # Predict the stock price for a specific date (e.g., the last date in the dataset)
-            prediction_date = stock_data['Date'].iloc[-1]
-            prediction_features = [[
-                prediction_date.year,
-                prediction_date.month,
-                prediction_date.day,
-                prediction_date.hour,
-                prediction_date.minute
-            ]]
-            predicted_price = model.predict(prediction_features)[0]
-
-            st.subheader(f"Predicted Stock Price on {prediction_date} (UTC):")
-            st.write(f"${predicted_price:.2f}")
+        # Display the raw stock data
+        st.subheader("Raw Stock Data:")
+        st.write(stock_data)
 
-# Run the Streamlit app
 if __name__ == '__main__':
     main()