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