Abhisesh7 commited on
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f0f6430
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Create app.py

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  1. app.py +130 -0
app.py ADDED
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+ import yfinance as yf
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+ import pandas as pd
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+ import numpy as np
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+ import tensorflow as tf
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+ import gradio as gr
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+ import matplotlib.pyplot as plt
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+ from sklearn.preprocessing import MinMaxScaler
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+
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+ # Function to get stock data
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+ def fetch_stock_data(ticker, start_date, end_date):
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+ stock_data = yf.download(ticker, start=start_date, end=end_date)
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+ return stock_data
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+
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+ # Prepare the dataset for LSTM
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+ def prepare_data(data):
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+ # Scaling data
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+ scaler = MinMaxScaler(feature_range=(0, 1))
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+ scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1,1))
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+
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+ # Create training and testing sets (80% train, 20% test)
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+ train_size = int(len(scaled_data) * 0.8)
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+ train_data, test_data = scaled_data[:train_size], scaled_data[train_size:]
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+
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+ # Create sequences for LSTM (last 60 days used to predict the next one)
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+ def create_sequences(data, time_step=60):
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+ X, y = [], []
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+ for i in range(len(data) - time_step - 1):
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+ X.append(data[i:i + time_step])
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+ y.append(data[i + time_step])
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+ return np.array(X), np.array(y)
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+
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+ X_train, y_train = create_sequences(train_data)
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+ X_test, y_test = create_sequences(test_data)
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+
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+ return X_train, y_train, X_test, y_test, scaler
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+
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+ # Building the LSTM model
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+ def build_lstm_model(input_shape):
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+ model = tf.keras.Sequential([
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+ tf.keras.layers.LSTM(50, return_sequences=True, input_shape=(input_shape[1], 1)),
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+ tf.keras.layers.LSTM(50, return_sequences=False),
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+ tf.keras.layers.Dense(25),
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+ tf.keras.layers.Dense(1)
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+ ])
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+
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+ model.compile(optimizer='adam', loss='mean_squared_error')
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+ return model
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+
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+ # Training the model
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+ def train_model(X_train, y_train):
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+ model = build_lstm_model(X_train.shape)
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+ model.fit(X_train, y_train, batch_size=1, epochs=1)
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+ return model
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+
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+ # Make predictions
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+ def make_predictions(model, X_test, scaler):
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+ predictions = model.predict(X_test)
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+ return scaler.inverse_transform(predictions)
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+
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+ # Calculate metrics for buy/sell decisions
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+ def calculate_metrics(data, predictions):
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+ start_value = data['Close'][0]
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+ end_value = predictions[-1][0]
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+ percentage_change = ((end_value - start_value) / start_value) * 100
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+
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+ highest_value = data['Close'].max()
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+ lowest_value = data['Close'].min()
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+
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+ decision = "Buy" if end_value > start_value else "Sell"
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+
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+ return percentage_change, highest_value, lowest_value, decision
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+
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+ # Plot historical vs predicted
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+ def plot_graph(data, predictions, ticker):
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+ plt.figure(figsize=(12,6))
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+ plt.plot(data.index, data['Close'], label="Historical")
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+ plt.plot(data.index[len(data) - len(predictions):], predictions, label="Predicted")
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+ plt.title(f'{ticker} Stock Price Prediction')
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+ plt.xlabel('Date')
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+ plt.ylabel('Price')
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+ plt.legend()
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+ plt.grid(True)
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+ plt.show()
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+
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+ # Gradio app function
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+ def stock_predictor(ticker, start_date, end_date):
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+ stock_data = fetch_stock_data(ticker, start_date, end_date)
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+
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+ if len(stock_data) == 0:
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+ return "Error: No data found. Please select a valid date range."
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+
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+ X_train, y_train, X_test, y_test, scaler = prepare_data(stock_data)
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+
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+ model = train_model(X_train, y_train)
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+
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+ predictions = make_predictions(model, X_test, scaler)
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+
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+ percentage_change, highest_value, lowest_value, decision = calculate_metrics(stock_data, predictions)
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+
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+ plot_graph(stock_data, predictions, ticker)
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+
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+ return {
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+ "Percentage Change": percentage_change,
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+ "Highest Value": highest_value,
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+ "Lowest Value": lowest_value,
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+ "Prediction (Buy/Sell)": decision
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+ }
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+
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+ # Define stock tickers for dropdown
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+ tickers = ['AAPL', 'GOOGL', 'AMZN', 'MSFT', 'TSLA', 'NFLX', 'NVDA', 'META', 'BABA', 'INTC']
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+
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+ # Gradio interface
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+ gr_interface = gr.Interface(
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+ fn=stock_predictor,
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+ inputs=[
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+ gr.Dropdown(choices=tickers, label="Select Stock Ticker"),
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+ gr.Date(label="Start Date"),
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+ gr.Date(label="End Date")
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+ ],
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+ outputs=[
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+ gr.Label(label="Percentage Change"),
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+ gr.Label(label="Highest Value"),
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+ gr.Label(label="Lowest Value"),
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+ gr.Label(label="Prediction (Buy/Sell)"),
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+ gr.Plot(label="Stock Performance")
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+ ],
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+ title="Stock Prediction App"
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+ )
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+
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+ gr_interface.launch()