Added initial project files

.gitignore

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+.DS_Store

app.py

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+#+--------------------------------------------------------------------------------------------+
+# Model for Stock Price Prediction via Linear Regression
+# Using today vs tomorrow analysis
+# app.py for deployment on Hugging Face
+# 
+# Written by: Prakash R. Kota
+# Location: East Greenbush, NY
+#
+#
+# Based on: 2a_HF_linear_model_app.ipynb
+# and Based on: 1a_HF_linear_model.ipynb
+#
+# Written on: 25 Mar 2025
+# Last update: 25 Mar 2025
+#+--------------------------------------------------------------------------------------------+
+
+import os
+import joblib
+import yfinance as yf
+import pandas as pd
+import numpy as np
+from datetime import datetime
+from sklearn.metrics import mean_absolute_percentage_error
+from sklearn.linear_model import LinearRegression
+import gradio as gr
+
+# --- Global Variables --- #
+Stock = "NVDA"
+model_path = f"./model/{Stock}_lr_model.pkl"
+start_date = "2020-01-01"
+test_start_date = "2025-01-01"
+today = datetime.today().strftime('%Y-%m-%d')
+
+# --- Load Model --- #
+if not os.path.exists(model_path):
+    raise FileNotFoundError(f"Model not found at {model_path}. Please train and save it first.")
+
+model = joblib.load(model_path)
+
+def run_prediction():
+    # --- Download Data --- #
+    data = yf.download(Stock, start=start_date, end=today)[["Close"]]
+    data.dropna(inplace=True)
+    data.index = pd.to_datetime(data.index)
+
+    # --- Create X and y for prediction --- #
+    xactual = data[:-1]["Close"].values.reshape(-1, 1)  # Today's close
+    ytrue = data[1:]["Close"]                           # Tomorrow's close (Series)
+    dates = data[1:].index
+
+    # Keep only test range (2025+)
+    mask = dates >= test_start_date
+    xactual = xactual[mask]
+    ytrue = ytrue[mask]
+    dates = dates[mask]
+
+    # Predict
+    typred = model.predict(xactual)
+
+    # --- Build DataFrame --- #
+    pred_df = pd.DataFrame({
+        "Date": dates,
+        "Actual Close": ytrue.values,
+        "Predicted Close": typred.flatten()
+    })
+
+    # --- Calculate Metrics --- #
+    pred_df["% Error"] = ((pred_df["Predicted Close"] - pred_df["Actual Close"]) / pred_df["Actual Close"]) * 100
+    mape = np.mean(np.abs(pred_df["% Error"]))
+    sape = np.std(np.abs(pred_df["% Error"]))
+
+    # Add MAPE Range per row to table
+    pred_df["±MAPE Range"] = pred_df["Predicted Close"].apply(
+        lambda x: f"${x * (1 - mape/100):.2f} to ${x * (1 + mape/100):.2f}"
+    )
+
+    # --- Next Day Prediction --- #
+    latest_close = float(data["Close"].iloc[-1])
+    latest_date = data.index[-1].strftime("%Y-%m-%d")
+    next_pred = float(model.predict(np.array([[latest_close]]))[0])
+    next_date = (data.index[-1] + pd.tseries.offsets.BDay(1)).strftime("%Y-%m-%d")
+    now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+    # Expected Ranges
+    mape_range = (next_pred * (1 - mape/100), next_pred * (1 + mape/100))
+    sape_range = (next_pred * (1 - sape/100), next_pred * (1 + sape/100))
+
+    summary = f"""
+Prediction for {Stock} made at {now}:
+Last available date: {latest_date}, Close = ${latest_close:.2f}
+Predicted closing price for next trading day ({next_date}): ${next_pred:.2f}
+Expected range (±MAPE): ${mape_range[0]:.2f} to ${mape_range[1]:.2f}
+Expected range (±SAPE):  ${sape_range[0]:.2f} to ${sape_range[1]:.2f}
+"""
+
+    # Sort and format
+    pred_df = pred_df.sort_values("Date", ascending=False)
+    pred_df["Date"] = pred_df["Date"].dt.strftime("%Y-%m-%d")
+
+    return summary, pred_df
+
+# --- Gradio Interface --- #
+description = f"""<h3>Linear Regression Stock Prediction</h3>
+<p>This app loads a trained linear regression model for <b>{Stock}</b> and predicts the next trading day's close based on the last available price. 
+It also shows historical prediction accuracy from 2025 onward.</p>"""
+
+demo = gr.Interface(
+    fn=run_prediction,
+    inputs=[],
+    outputs=[
+        gr.Textbox(label="Prediction Summary", lines=6),
+        gr.Dataframe(label="Prediction Table (2025+)", wrap=True)
+    ],
+    title="Stock Prediction using Linear Regression",
+    description=description,
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+scikit-learn
+yfinance
+pandas
+numpy
+joblib