Create python

python

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+
+def fetch_data(ticker, start_date, end_date):
+    # Fetch data using yfinance
+    data = yf.download(ticker, start=start_date, end=end_date)
+    return data
+    def preprocess_data(data):
+    # Select 'Close' prices for prediction
+    prices = data['Close'].values.reshape(-1, 1)
+    # Normalize the data
+    from sklearn.preprocessing import MinMaxScaler
+    scaler = MinMaxScaler(feature_range=(0, 1))
+    scaled_prices = scaler.fit_transform(prices)
+
+    # Create dataset with features and labels
+    x_data, y_data = [], []
+    for i in range(60, len(scaled_prices)):
+        x_data.append(scaled_prices[i-60:i, 0])
+        y_data.append(scaled_prices[i, 0])
+    return np.array(x_data), np.array(y_data), scaler
+def build_model():
+    model = Sequential()
+    model.add(LSTM(units=50, return_sequences=True, input_shape=(60, 1)))
+    model.add(Dropout(0.2))
+    model.add(LSTM(units=50, return_sequences=False))
+    model.add(Dropout(0.2))
+    model.add(Dense(units=1))  # Prediction for the next price
+    model.compile(optimizer='adam', loss='mean_squared_error')
+    return model
+def train_model(x_data, y_data):
+    # Reshape data for LSTM
+    x_data = np.reshape(x_data, (x_data.shape[0], x_data.shape[1], 1))
+    model = build_model()
+    model.fit(x_data, y_data, epochs=50, batch_size=32)
+    return model
+def make_prediction(model, data, scaler):
+    # Prepare the last 60 days of data
+    last_data = data['Close'].values[-60:].reshape(-1, 1)
+    last_data_scaled = scaler.transform(last_data)
+
+    x_test = []
+    x_test.append(last_data_scaled)
+    x_test = np.array(x_test)
+    x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
+
+    predicted_price = model.predict(x_test)
+    predicted_price = scaler.inverse_transform(predicted_price)  # Convert back to original scale
+    return predicted_price[0][0]
+def stock_prediction(ticker, start_date, end_date):
+    data = fetch_data(ticker, start_date, end_date)
+    x_data, y_data, scaler = preprocess_data(data)
+    model = train_model(x_data, y_data)
+    predicted_price = make_prediction(model, data, scaler)
+    
+    # Calculate percentage change, highest and lowest values
+    percentage_change = ((predicted_price - data['Close'].iloc[-1]) / data['Close'].iloc[-1]) * 100
+    highest_price = data['Close'].max()
+    lowest_price = data['Close'].min()
+    
+    # Visualization
+    plt.figure(figsize=(14, 5))
+    plt.plot(data['Close'], label='Historical Prices')
+    plt.axhline(y=predicted_price, color='r', linestyle='--', label='Predicted Price')
+    plt.title(f'{ticker} Price Prediction')
+    plt.xlabel('Date')
+    plt.ylabel('Price')
+    plt.legend()
+    plt.show()
+
+    return {
+        'Predicted Price': predicted_price,
+        'Percentage Change': percentage_change,
+        'Highest Price': highest_price,
+        'Lowest Price': lowest_price
+    }
+
+# Gradio Interface
+ticker_options = ['AAPL', 'GOOGL', 'AMZN', 'MSFT', 'TSLA', 'FB', 'NFLX', 'NVDA', 'BRK.B', 'DIS']
+iface = gr.Interface(
+    fn=stock_prediction,
+    inputs=[
+        gr.inputs.Dropdown(choices=ticker_options, label='Stock Ticker'),
+        gr.inputs.Date(label='Start Date'),
+        gr.inputs.Date(label='End Date')
+    ],
+    outputs='json'
+)
+
+iface.launch()