Create app.py

app.py

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+from gradio_app_builder import app
+import yfinance as yf
+from sklearn.linear_model import LinearRegression
+import json
+
+@app.route("/")
+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 list 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 and set as index
+  data = data["Close"].to_frame().set_index(data.index.values)
+
+  # Train linear regression model
+  X = data.index.values[:-prediction_days].reshape(-1, 1)
+  y = data.values[:-prediction_days]
+  model = LinearRegression()
+  model.fit(X, y)
+
+  # Predict future prices
+  future_dates = data.index.values[-prediction_days:]
+  X_future = future_dates.reshape(-1, 1)
+  predicted_prices = model.predict(X_future).tolist()
+
+  # Return predicted prices as JSON
+  return json.dumps(predicted_prices)
+
+# Launch the Gradio application
+app.launch()