Sync App files

app.py

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+import os
+import gradio as gr
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import skops.io as sio
+
+
+class StockPredictor:
+    """
+    A class used to load stock prediction models, process historical stock data,
+    and forecast stock prices.
+
+    Attributes
+    ----------
+    model_dir : str
+        Directory containing the trained models.
+    data_dir : str
+        Directory containing the historical stock data CSV files.
+    models : dict
+        Dictionary of loaded models.
+
+    Methods
+    -------
+    load_models(model_dir):
+        Loads the models from the specified directory.
+    load_stock_data(ticker):
+        Loads and processes historical stock data from a CSV file.
+    forecast(ticker, days):
+        Forecasts stock prices for the specified ticker and number of days.
+    """
+
+    def __init__(self, model_dir="model/SKLearn_Models", data_dir="data"):
+        """
+        Initializes the StockPredictor class by loading the models and setting the data directory.
+
+        Parameters
+        ----------
+        model_dir : str
+            Directory containing the trained models.
+        data_dir : str
+            Directory containing the historical stock data CSV files.
+        """
+        self.models = self.load_models(model_dir)
+        self.data_dir = data_dir
+
+    def load_models(self, model_dir):
+        """
+        Loads the models from the specified directory.
+
+        Parameters
+        ----------
+        model_dir : str
+            Directory containing the trained models.
+
+        Returns
+        -------
+        dict
+            Dictionary of loaded models.
+        """
+        models = {}
+        for file in os.listdir(model_dir):
+            if file.endswith(".skops"):
+                ticker = file.split("_")[0]
+                models[ticker] = sio.load(os.path.join(model_dir, file))
+        return models
+
+    def load_stock_data(self, ticker):
+        """
+        Loads and processes historical stock data from a CSV file.
+
+        Parameters
+        ----------
+        ticker : str
+            Stock ticker symbol.
+
+        Returns
+        -------
+        pandas.DataFrame
+            Processed historical stock data.
+        """
+        # Construct the CSV file path
+        csv_path = os.path.join(self.data_dir, f"{ticker}.csv")
+        data = pd.read_csv(csv_path)
+
+        # Convert 'date' to datetime
+        data["date"] = pd.to_datetime(data["date"])
+
+        # Filter the data to start from the year 2000
+        data = data[data["date"] >= "2000-01-01"]
+
+        # Sort by date
+        data.sort_values("date", inplace=True)
+
+        # Feature engineering: create new features such as moving averages
+        data["ma_5"] = data["close"].rolling(window=5).mean()
+        data["ma_10"] = data["close"].rolling(window=10).mean()
+
+        # Drop rows with NaN values created by rolling window
+        data.dropna(inplace=True)
+
+        return data
+
+    def forecast(self, ticker, days):
+        """
+        Forecasts stock prices for the specified ticker and number of days.
+
+        Parameters
+        ----------
+        ticker : str
+            Stock ticker symbol.
+        days : int
+            Number of days for forecasting.
+
+        Returns
+        -------
+        tuple
+            A tuple containing a DataFrame with dates, actual close values, and predicted close values,
+            and the file path of the generated plot.
+        """
+        model = self.models.get(ticker)
+        if model:
+            # Load historical stock data
+            data = self.load_stock_data(ticker)
+
+            # Take the last 'days' worth of data for prediction
+            data = data.tail(days)
+
+            # Define features
+            features = ["open", "high", "low", "ma_5", "ma_10"]
+
+            X = data[features]
+
+            # Make predictions
+            predictions = model.predict(X)
+
+            # Round predictions to 2 decimal places
+            predictions = np.round(predictions, 2)
+
+            # Create a DataFrame with dates and predicted close values
+            result_df = pd.DataFrame(
+                {
+                    "date": data["date"],
+                    "actual_close": data["close"],
+                    "predicted_close": predictions,
+                }
+            )
+
+            # Plot the actual and predicted close values
+            plt.figure(figsize=(10, 5))
+            plt.plot(result_df["date"], result_df["actual_close"], label="Actual Close")
+            plt.plot(
+                result_df["date"], result_df["predicted_close"], label="Predicted Close"
+            )
+            plt.xlabel("Date")
+            plt.ylabel("Close Price")
+            plt.title(f"{ticker} Stock Price Prediction")
+            plt.legend()
+            plt.grid(True)
+            plt.xticks(rotation=45)
+
+            # Save the plot to a file
+            plot_path = f"{ticker}_prediction_plot.png"
+            plt.savefig(plot_path)
+            plt.close()
+
+            return result_df, plot_path
+        else:
+            return pd.DataFrame({"Error": ["Model not found"]}), None
+
+
+def create_gradio_interface(stock_predictor):
+    """
+    Creates the Gradio interface for the stock predictor.
+
+    Parameters
+    ----------
+    stock_predictor : StockPredictor
+        Instance of the StockPredictor class.
+
+    Returns
+    -------
+    gradio.Interface
+        The Gradio interface.
+    """
+    tickers = list(stock_predictor.models.keys())
+    dropdown = gr.Dropdown(choices=tickers, label="Select Ticker")
+    slider = gr.Slider(
+        minimum=1,
+        maximum=30,
+        step=1,
+        label="Number of Days for Forecasting",
+    )
+
+    iface = gr.Interface(
+        fn=stock_predictor.forecast,
+        inputs=[dropdown, slider],
+        outputs=[
+            gr.DataFrame(headers=["date", "actual_close", "predicted_close"]),
+            gr.Image(),
+        ],
+        title="Stock Price Forecasting",
+        description="Select a ticker and number of days to forecast stock prices.",
+    )
+
+    return iface
+
+
+if __name__ == "__main__":
+    # Initialize StockPredictor and create Gradio interface
+    stock_predictor = StockPredictor(
+        model_dir="model/SKLearn_Models",
+        data_dir="data/Cleaned_Kaggle_NASDAQ_Daily_Data",
+    )
+    iface = create_gradio_interface(stock_predictor)
+
+    # Launch the app
+    iface.launch()