Update app.py

app.py

@@ -1,6 +1,6 @@
 import yfinance as yf
 import pandas as pd
-import numpy as np  # Make sure to import numpy
+import numpy as np
 from sklearn.preprocessing import MinMaxScaler
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import LSTM, Dense
@@ -35,7 +35,7 @@ def train_model(stock_data, window_size=60):
     for i in range(window_size, len(stock_data)):
         X_train.append(stock_data[i-window_size:i, 0])
         y_train.append(stock_data[i, 0])
-    X_train, y_train = np.array(X_train), np.array(y_train)  # Ensure numpy is used here
+    X_train, y_train = np.array(X_train), np.array(y_train)
     X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
 
     model = create_lstm_model((X_train.shape[1], 1))
@@ -45,13 +45,12 @@ def train_model(stock_data, window_size=60):
 # Predict future stock prices
 def predict_future_prices(model, scaler, recent_data, days_to_predict=90):
     predictions = []
-    input_data = recent_data[-60:].reshape(1, 60, 1)  # Using the last 60 days to predict
+    input_data = recent_data[-60:].reshape(1, 60, 1)
     for _ in range(days_to_predict):
         pred_price = model.predict(input_data)[0, 0]
         predictions.append(pred_price)
         input_data = np.append(input_data[:, 1:, :], [[pred_price]], axis=1)
 
-    # Inverse transform to get the original prices
     predicted_prices = scaler.inverse_transform(np.array(predictions).reshape(-1, 1))
     return predicted_prices
 
@@ -88,8 +87,8 @@ with app:
     gr.Markdown("# Stock Buy/Sell Prediction App")
     
     ticker = gr.Dropdown(tickers, label="Select Stock Ticker")
-    start_date = gr.Date(label="Start Date")
-    end_date = gr.Date(label="End Date")
+    start_date = gr.inputs.DatePicker(label="Start Date")  # Use DatePicker for selecting dates
+    end_date = gr.inputs.DatePicker(label="End Date")  # Use DatePicker for selecting dates
     
     predict_button = gr.Button("Predict")