Update app.py

app.py

@@ -1,91 +1,91 @@
-# import streamlit as st
-# import yfinance as yf
-# import pandas as pd
-# import matplotlib.pyplot as plt
-# import numpy as np 
-# from sklearn.preprocessing import MinMaxScaler
-# from tensorflow.keras.models import Sequential
-# from tensorflow.keras.layers import LSTM, Dense
+import streamlit as st
+import yfinance as yf
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np 
+from sklearn.preprocessing import MinMaxScaler
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense
 
-# def fetch_stock_data(symbol): 
-#   stock = yf.Ticker(symbol) 
-#   hist = stock.history(period="6mo")
-#   return hist
+def fetch_stock_data(symbol): 
+  stock = yf.Ticker(symbol) 
+  hist = stock.history(period="6mo")
+  return hist
 
-# def prepare_data(data, time_steps=10): 
-#   scaler = MinMaxScaler(feature_range=(0,1))
-#   scaled_data = scaler.fit_transform(data[['Close']].values)
+def prepare_data(data, time_steps=10): 
+  scaler = MinMaxScaler(feature_range=(0,1))
+  scaled_data = scaler.fit_transform(data[['Close']].values)
 
-#   X, y = [], []
-#   for i in range(time_steps, len(scaled_data)):
-#     X.append(scaled_data[i-time_steps:i, 0])
-#     y.append(scaled_data[i, 0])
+  X, y = [], []
+  for i in range(time_steps, len(scaled_data)):
+    X.append(scaled_data[i-time_steps:i, 0])
+    y.append(scaled_data[i, 0])
 
-#   X, y = np.array(X), np.array(y)
-#   X = np.reshape(X, (X.shape[0], X.shape[1], 1))
-#   return X, y, scaler
+  X, y = np.array(X), np.array(y)
+  X = np.reshape(X, (X.shape[0], X.shape[1], 1))
+  return X, y, scaler
 
-# def build_lstm_model():
-#   model = Sequential([
-#        LSTM(50, return_sequences=True, input_shape=(10,1)),
-#        LSTM(50, return_sequences=False), 
-#        Dense(25), 
-#        Dense(1) ])
-#   model.compile(optimizer='adam', loss='mean_squared_error') 
-#   return model
+def build_lstm_model():
+  model = Sequential([
+       LSTM(50, return_sequences=True, input_shape=(10,1)),
+       LSTM(50, return_sequences=False), 
+       Dense(25), 
+       Dense(1) ])
+  model.compile(optimizer='adam', loss='mean_squared_error') 
+  return model
 
-# def predict_prices(data, days=7):
-#   X, y, scaler = prepare_data(data)
-#   model = build_lstm_model()
-#   model.fit(X, y, epochs=10, batch_size=16, verbose=0)
+def predict_prices(data, days=7):
+  X, y, scaler = prepare_data(data)
+  model = build_lstm_model()
+  model.fit(X, y, epochs=10, batch_size=16, verbose=0)
 
-#   future_input = X[-1].reshape(1, 10, 1)
-#   predictions = []
-#   for _ in range(days):
-#     pred = model.predict(future_input)[0, 0]
-#     predictions.append(pred)
-#     future_input = np.append(future_input[:, 1:, :], [[[pred]]], axis=1)
-#     return np.array(predictions).reshape(-1, 1), scaler
+  future_input = X[-1].reshape(1, 10, 1)
+  predictions = []
+  for _ in range(days):
+    pred = model.predict(future_input)[0, 0]
+    predictions.append(pred)
+    future_input = np.append(future_input[:, 1:, :], [[[pred]]], axis=1)
+    return np.array(predictions).reshape(-1, 1), scaler
 
-# def main():
-#   st.title("Stock Data Viewer with LSTM Prediction")
+def main():
+  st.title("Stock Data Viewer with LSTM Prediction")
 
-#   symbol = st.text_input("Enter Stock Symbol (e.g., AAPL, TSLA):")
+  symbol = st.text_input("Enter Stock Symbol (e.g., AAPL, TSLA):")
 
-#   if symbol:
-#     try:
-#         data = fetch_stock_data(symbol)
-#         if data.empty:
-#             st.error("Invalid stock symbol or no data available.")
-#         else:
-#             st.subheader("Stock Price History")
-#             st.write(data)
+  if symbol:
+    try:
+        data = fetch_stock_data(symbol)
+        if data.empty:
+            st.error("Invalid stock symbol or no data available.")
+        else:
+            st.subheader("Stock Price History")
+            st.write(data)
             
-#             st.subheader("Stock Price Chart")
-#             fig, ax = plt.subplots()
-#             ax.plot(data.index, data['Close'], label='Close Price')
-#             ax.set_xlabel("Date")
-#             ax.set_ylabel("Price (USD)")
-#             ax.legend()
-#             st.pyplot(fig)
+            st.subheader("Stock Price Chart")
+            fig, ax = plt.subplots()
+            ax.plot(data.index, data['Close'], label='Close Price')
+            ax.set_xlabel("Date")
+            ax.set_ylabel("Price (USD)")
+            ax.legend()
+            st.pyplot(fig)
             
-#             days_to_predict = st.slider("Days to Predict", 1, 30, 7)
-#             predictions, scaler = predict_prices(data, days_to_predict)
-#             predicted_prices = scaler.inverse_transform(predictions)
+            days_to_predict = st.slider("Days to Predict", 1, 30, 7)
+            predictions, scaler = predict_prices(data, days_to_predict)
+            predicted_prices = scaler.inverse_transform(predictions)
             
-#             st.subheader("Predicted Stock Prices")
-#             fig, ax = plt.subplots()
-#             ax.plot(data.index, data['Close'], label='Historical Close Price')
-#             ax.plot(pd.date_range(data.index[-1], periods=days_to_predict+1, freq='D')[1:], predicted_prices, linestyle='dashed', label='Predicted Price')
-#             ax.set_xlabel("Date")
-#             ax.set_ylabel("Price (USD)")
-#             ax.legend()
-#             st.pyplot(fig)
+            st.subheader("Predicted Stock Prices")
+            fig, ax = plt.subplots()
+            ax.plot(data.index, data['Close'], label='Historical Close Price')
+            ax.plot(pd.date_range(data.index[-1], periods=days_to_predict+1, freq='D')[1:], predicted_prices, linestyle='dashed', label='Predicted Price')
+            ax.set_xlabel("Date")
+            ax.set_ylabel("Price (USD)")
+            ax.legend()
+            st.pyplot(fig)
             
-#             csv = data.to_csv().encode('utf-8')
-#             st.download_button("Download CSV", csv, f"{symbol}_data.csv", "text/csv")
-#     except Exception as e:
-#         st.error(f"An error occurred: {e}")
+            csv = data.to_csv().encode('utf-8')
+            st.download_button("Download CSV", csv, f"{symbol}_data.csv", "text/csv")
+    except Exception as e:
+        st.error(f"An error occurred: {e}")
 
-# if  __name__ == "main":
-#   main()
+if  __name__ == "main":
+  main()