Update app.py
Browse files
app.py
CHANGED
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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 matplotlib.pyplot as plt
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import gradio as gr
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from datetime import datetime
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# Ensure matplotlib does not require a display environment
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import matplotlib
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matplotlib.use('Agg')
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# Define the stock tickers
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STOCK_TICKERS = [
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'AAPL', 'GOOGL', 'MSFT', 'AMZN', 'TSLA',
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'FB', 'NVDA', 'JPM', 'V', 'DIS'
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]
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def fetch_data(ticker, start_date, end_date):
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"""
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Fetch historical stock data from yfinance.
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start_date (str): Start date in 'YYYY-MM-DD'.
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end_date (str): End date in 'YYYY-MM-DD'.
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"""
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data = yf.download(ticker, start=start_date, end=end_date)
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return data
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return features, labels
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def build_model(input_shape):
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"""
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Build and compile the TensorFlow model.
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Args:
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input_shape (int): Number of features.
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Returns:
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tf.keras.Model: Compiled model.
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"""
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model = models.Sequential([
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layers.Dense(64, activation='relu', input_shape=(input_shape,)),
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layers.Dense(32, activation='relu'),
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layers.Dense(1, activation='sigmoid') # Binary classification
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])
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model
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return model
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# Train the model for each stock ticker and store in a dictionary
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models_dict = {}
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for ticker in STOCK_TICKERS:
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# Fetch data for the past 5 years
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end = datetime.today()
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start = end - timedelta(days=5*365)
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data = fetch_data(ticker, start.strftime('%Y-%m-%d'), end.strftime('%Y-%m-%d'))
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if data.empty:
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print(f"No data found for {ticker}. Skipping...")
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continue
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features, labels = preprocess_data(data)
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model = build_model(features.shape[1])
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model.fit(features, labels, epochs=10, batch_size=32, verbose=0)
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models_dict[ticker] = model
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print(f"Model trained for {ticker}")
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def predict_stock(ticker, start_date, end_date):
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"""
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Predict whether to Buy or Sell the stock based on user input.
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Args:
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ticker (str): Selected stock ticker.
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start_date (str): Training start date.
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end_date (str): Training end date.
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Returns:
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dict: Prediction results and graph.
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"""
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# Fetch data
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data = fetch_data(ticker, start_date, end_date)
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if data.empty:
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return {
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"Percentage Change": "No data available for the selected dates.",
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"Highest Price": "N/A",
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"Lowest Price": "N/A",
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"Prediction": "N/A",
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"Graph": None
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}
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#
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return {
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"Percentage Change": "Insufficient data after preprocessing.",
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"Highest Price": "N/A",
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"Lowest Price": "N/A",
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"Prediction": "N/A",
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"Graph": None
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}
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if not model:
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return {
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"Percentage Change": "Model not found for the selected ticker.",
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"Highest Price": "N/A",
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"Lowest Price": "N/A",
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"Prediction": "N/A",
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"Graph": None
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}
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prediction = model.predict(latest_data)
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prediction_label = "Buy" if prediction[0][0] > 0.5 else "Sell"
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# Calculate percentage change
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start_close = data['Close'].iloc[0]
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latest_close = data['Close'].iloc[-1]
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percent_change = ((latest_close - start_close) / start_close) * 100
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# Highest and Lowest values
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highest = data['Close'].max()
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lowest = data['Close'].min()
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# Plot historical data
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plt.figure(figsize=(10,5))
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plt.plot(data.index, data['Close'], label='Historical Close')
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# Predict future 3 months (approx 63 trading days)
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future_days = 63
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# For simplicity, we'll extend the latest close with random walk
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future_prices = [latest_close]
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for _ in range(future_days):
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change_percent = np.random.uniform(-0.02, 0.02) # Simulate small changes
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new_price = future_prices[-1] * (1 + change_percent)
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future_prices.append(new_price)
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future_dates = pd.date_range(data.index[-1] + timedelta(days=1), periods=future_days+1, freq='B')
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plt.plot(future_dates, future_prices[1:], label='Predicted Close')
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plt.xlabel('Date')
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plt.ylabel('Price')
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plt.title(f'{ticker} Historical and Predicted Performance')
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plt.legend()
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plt.
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plt.
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inputs=[
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gr.Dropdown(choices=STOCK_TICKERS, label="Select Stock Ticker"),
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gr.DatePicker(label="Start Date"),
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gr.DatePicker(label="End Date")
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],
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outputs=[
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gr.Textbox(label="Percentage Change"),
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gr.Textbox(label="Highest Price"),
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gr.Textbox(label="Lowest Price"),
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gr.Textbox(label="Buy/Sell Prediction"),
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gr.Image(label="Performance Graph")
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],
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title="📈 Stock Buy/Sell Prediction App",
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description=(
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"Select a stock ticker and a date range to predict whether to **Buy** or **Sell** the stock. "
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"View the percentage change, highest and lowest prices, and a performance graph."
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)
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)
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# Launch the app
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iface.launch()
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense, Input
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def create_model(input_shape):
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model = Sequential()
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model.add(Input(shape=(input_shape,))) # Explicitly define input shape
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model.add(Dense(64, activation='relu'))
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model.add(Dense(32, activation='relu'))
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model.add(Dense(1, activation='sigmoid'))
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model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
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return model
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data.loc[:, 'Target'] = np.where(data['Close'].shift(-1) > data['Close'], 1, 0)
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import gradio as gr
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def predict_stock(ticker, start_date, end_date):
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# Your prediction logic
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return "Prediction result"
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interface = gr.Interface(
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fn=
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inputs=[
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gr.inputs.Dropdown(
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gr.inputs.Date(label="Start Date"),
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gr.inputs.Date(label="End Date")
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],
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outputs=
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)
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interface.launch()
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import yfinance as yf
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import tensorflow as tf
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import gradio as gr
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from datetime import datetime
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# Stock tickers for dropdown
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stock_tickers = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'TSLA', 'NFLX', 'FB', 'NVDA', 'JPM', 'BAC']
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# Function to download stock data from Yahoo Finance
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def fetch_stock_data(ticker, start_date, end_date):
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data = yf.download(ticker, start=start_date, end=end_date)
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return data
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# Function to train LSTM model and predict stock prices
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def train_lstm(data):
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# Preprocessing and creating training dataset
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data['Close'] = data['Close'].fillna(method='ffill') # Forward-fill to handle NaNs
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scaled_data = data['Close'].values.reshape(-1, 1)
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training_data = scaled_data[:int(len(scaled_data) * 0.8)] # 80% data for training
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test_data = scaled_data[int(len(scaled_data) * 0.8):]
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# Creating sequences for LSTM input
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def create_sequences(data, seq_length):
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X, y = [], []
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for i in range(len(data) - seq_length):
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X.append(data[i:i + seq_length])
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y.append(data[i + seq_length])
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return np.array(X), np.array(y)
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seq_length = 60 # Using 60 days for prediction
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X_train, y_train = create_sequences(training_data, seq_length)
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# Define the LSTM model
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model = tf.keras.Sequential([
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tf.keras.layers.LSTM(50, return_sequences=True, input_shape=(X_train.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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# Compile and train the model
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model.compile(optimizer='adam', loss='mean_squared_error')
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model.fit(X_train, y_train, batch_size=1, epochs=1) # Short training for demo, increase epochs for better results
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# Predict future prices
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X_test, _ = create_sequences(test_data, seq_length)
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predictions = model.predict(X_test)
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return predictions, data.index[-len(predictions):]
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# Function to plot historical and predicted prices
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def plot_stock_data(data, predictions, pred_dates):
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plt.figure(figsize=(10,6))
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plt.plot(data['Close'], label='Historical Data')
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plt.plot(pred_dates, predictions, label='Predicted Data')
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plt.xlabel('Date')
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plt.ylabel('Close Price (USD)')
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plt.legend()
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plt.grid()
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plt.show()
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# Main function to wrap everything into Gradio interface
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def stock_prediction(ticker, start_date, end_date):
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data = fetch_stock_data(ticker, start_date, end_date)
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predictions, pred_dates = train_lstm(data)
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plot_stock_data(data, predictions, pred_dates)
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# Calculate percentage change, highest and lowest values
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percentage_change = ((data['Close'][-1] - data['Close'][0]) / data['Close'][0]) * 100
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highest_value = data['Close'].max()
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lowest_value = data['Close'].min()
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# Buy/Sell decision based on prediction (buy if next value is higher)
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decision = 'Buy' if predictions[-1] > data['Close'][-1] else 'Sell'
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return f"Percentage Change: {percentage_change:.2f}%", f"Highest Value: {highest_value:.2f}", f"Lowest Value: {lowest_value:.2f}", f"Decision: {decision}"
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# Gradio UI
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interface = gr.Interface(
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fn=stock_prediction,
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inputs=[
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gr.inputs.Dropdown(choices=stock_tickers, label="Select Stock Ticker"),
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gr.inputs.Date(label="Start Date"),
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gr.inputs.Date(label="End Date")
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],
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outputs=[
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gr.outputs.Textbox(label="Percentage Change"),
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gr.outputs.Textbox(label="Highest Value"),
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gr.outputs.Textbox(label="Lowest Value"),
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gr.outputs.Textbox(label="Prediction: Buy/Sell")
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],
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title="Stock Prediction App",
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description="Predict if you should buy or sell a stock based on historical performance."
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)
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interface.launch()
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