Create app.py

app.py

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+import yfinance as yf
+import gradio as gr
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from datetime import datetime, timedelta
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, LSTM
+
+# Load stock data using Yahoo Finance
+def load_stock_data(ticker, start, end):
+    stock = yf.download(ticker, start=start, end=end)
+    return stock
+
+# Data Preprocessing
+def preprocess_data(stock):
+    data = stock[['Close']]
+    scaler = MinMaxScaler(feature_range=(0, 1))
+    scaled_data = scaler.fit_transform(data)
+
+    x_train, y_train = [], []
+    for i in range(60, len(scaled_data)):
+        x_train.append(scaled_data[i-60:i, 0])
+        y_train.append(scaled_data[i, 0])
+
+    x_train, y_train = np.array(x_train), np.array(y_train)
+    x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
+
+    return x_train, y_train, scaler
+
+# Build the LSTM model
+def build_model():
+    model = Sequential()
+    model.add(LSTM(units=50, return_sequences=True, input_shape=(60, 1)))
+    model.add(LSTM(units=50, return_sequences=False))
+    model.add(Dense(units=25))
+    model.add(Dense(units=1))
+
+    model.compile(optimizer='adam', loss='mean_squared_error')
+    return model
+
+# Training and prediction
+def train_model(ticker, start, end):
+    # Load stock data
+    stock_data = load_stock_data(ticker, start, end)
+
+    # Preprocess the data
+    x_train, y_train, scaler = preprocess_data(stock_data)
+
+    # Build and train the model
+    model = build_model()
+    model.fit(x_train, y_train, batch_size=1, epochs=1)
+
+    return model, scaler, stock_data
+
+# Predict stock prices
+def predict_stock(model, scaler, stock_data, ticker, start, end):
+    # Load real-time stock data for future predictions
+    test_data = stock_data[['Close']][-60:].values
+    test_data_scaled = scaler.transform(test_data)
+
+    x_test = []
+    x_test.append(test_data_scaled)
+
+    x_test = np.array(x_test)
+    x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
+
+    predictions = model.predict(x_test)
+    predictions = scaler.inverse_transform(predictions)
+
+    # Plotting the results
+    plt.figure(figsize=(10, 6))
+    plt.plot(stock_data.index, stock_data['Close'], label="Historical Prices")
+    future_dates = [stock_data.index[-1] + timedelta(days=i) for i in range(1, 91)]
+    plt.plot(future_dates, predictions.flatten(), label="Predicted Prices")
+    plt.title(f'{ticker} Stock Price Prediction')
+    plt.xlabel('Date')
+    plt.ylabel('Price')
+    plt.legend()
+    plt.show()
+
+    return predictions[-1][0]
+
+# Gradio Interface
+def stock_prediction(ticker, start, end):
+    # Train the model
+    model, scaler, stock_data = train_model(ticker, start, end)
+
+    # Make prediction for the next 3 months
+    prediction = predict_stock(model, scaler, stock_data, ticker, start, end)
+
+    # Stock performance data
+    start_price = stock_data['Close'].iloc[0]
+    end_price = stock_data['Close'].iloc[-1]
+    percent_change = ((end_price - start_price) / start_price) * 100
+    highest_price = stock_data['Close'].max()
+    lowest_price = stock_data['Close'].min()
+
+    return {
+        "Predicted Next Price": prediction,
+        "Percentage Change": percent_change,
+        "Highest Price": highest_price,
+        "Lowest Price": lowest_price
+    }
+
+# Gradio UI
+tickers = ['AAPL', 'GOOG', 'MSFT', 'TSLA', 'AMZN', 'NFLX', 'META', 'NVDA', 'BABA', 'INTC']
+start_default = (datetime.now() - timedelta(days=365)).strftime("%Y-%m-%d")
+end_default = datetime.now().strftime("%Y-%m-%d")
+
+iface = gr.Interface(
+    fn=stock_prediction,
+    inputs=[
+        gr.inputs.Dropdown(choices=tickers, label="Select Stock Ticker"),
+        gr.inputs.Date(label="Start Date", default=start_default),
+        gr.inputs.Date(label="End Date", default=end_default),
+    ],
+    outputs=[
+        gr.outputs.JSON(label="Prediction and Analysis")
+    ],
+    live=True
+)
+
+if __name__ == "__main__":
+    iface.launch()