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import gradio as gr
import yfinance as yf
import pandas as pd
from sklearn.linear_model import LinearRegression
import plotly.graph_objects as go
def train_predict_wrapper(ticker, start_date, end_date, prediction_days):
# Download stock data
data = yf.download(ticker, start=start_date, end=end_date)
data = data["Close"]
# Convert index to Unix timestamp (seconds)
data.index = (data.index - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s')
# Train linear regression model
X = data.index.values[:-prediction_days].reshape(-1, 1)
y = data.values[:-prediction_days]
model = LinearRegression()
model.fit(X, y)
# Prepare data for prediction
last_timestamp = data.index[-1]
future_timestamps = pd.date_range(start=pd.to_datetime(last_timestamp, unit='s'),
periods=prediction_days, freq='D')
future_timestamps = (future_timestamps - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s')
X_future = future_timestamps.values.reshape(-1, 1)
# Predict future prices
predicted_prices = model.predict(X_future)
# Prepare data for plotting
historical_prices = go.Scatter(
x=pd.to_datetime(data.index, unit='s'),
y=data.values,
mode="lines",
name="Historical Prices"
)
predicted_prices_trace = go.Scatter(
x=pd.to_datetime(future_timestamps, unit='s'),
y=predicted_prices,
mode="lines",
name="Predicted Prices"
)
# Plot data
fig = go.Figure()
fig.add_trace(historical_prices)
fig.add_trace(predicted_prices_trace)
fig.update_layout(
title="Stock Price Prediction",
xaxis_title="Date",
yaxis_title="Price",
legend_title_text="Data"
)
return fig
# Define Gradio interface
interface = gr.Interface(
fn=train_predict_wrapper,
inputs=[
gr.Textbox(label="Ticker Symbol"),
gr.Textbox(label="Start Date (YYYY-MM-DD)"),
gr.Textbox(label="End Date (YYYY-MM-DD)"),
gr.Slider(minimum=1, maximum=30, step=1, label="Prediction Days"),
],
outputs="plot"
)
# Launch the app
interface.launch() |