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import gradio as gr |
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import yfinance as yf |
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from sklearn.linear_model import LinearRegression |
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import plotly.graph_objs as go |
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import numpy as np |
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def train_predict_wrapper(ticker, start_date, end_date, prediction_days): |
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""" |
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Downloads stock data, trains a linear regression model, and predicts future prices. |
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Args: |
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ticker: The ticker symbol of the stock. |
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start_date: The start date for the data (YYYY-MM-DD format). |
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end_date: The end date for the data (YYYY-MM-DD format). |
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prediction_days: The number of days to predict. |
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Returns: |
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A plot of predicted closing prices for the next `prediction_days`. |
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""" |
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data = yf.download(ticker, start=start_date, end=end_date) |
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data = data["Close"] |
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data = data.reset_index() |
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data['Date'] = data['Date'].map(mdates.date2num) |
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X = np.array(data.index).reshape(-1, 1) |
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y = data['Close'].values |
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model = LinearRegression() |
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model.fit(X[:-prediction_days], y[:-prediction_days]) |
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future_indices = np.array(range(len(X), len(X) + prediction_days)).reshape(-1, 1) |
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predicted_prices = model.predict(future_indices) |
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dates = [mdates.num2date(date).strftime('%Y-%m-%d') for date in data['Date']] |
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future_dates = [mdates.num2date(date).strftime('%Y-%m-%d') for date in future_indices.flatten()] |
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fig = go.Figure() |
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fig.add_trace(go.Scatter(x=dates, y=y, mode='lines', name='Historical Prices')) |
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fig.add_trace(go.Scatter(x=future_dates, y=predicted_prices, mode='lines', name='Predicted Prices')) |
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fig.update_layout(title='Stock Price Prediction', xaxis_title='Date', yaxis_title='Price') |
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return fig |
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iface = gr.Interface( |
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fn=train_predict_wrapper, |
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inputs=[ |
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gr.inputs.Textbox(label="Ticker Symbol"), |
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gr.inputs.Textbox(label="Start Date (YYYY-MM-DD)"), |
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gr.inputs.Textbox(label="End Date (YYYY-MM-DD)"), |
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gr.inputs.Slider(minimum=1, maximum=30, step=1, default=5, label="Prediction Days") |
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], |
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outputs="plot" |
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) |
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iface.launch() |
