🚀 Initial upload of my app

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+demo/demo.mp4 filter=lfs diff=lfs merge=lfs -text
+models/lgb_sales_model.txt filter=lfs diff=lfs merge=lfs -text

LICENSE

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+MIT License
+
+Copyright (c) 2025 Eslam Tarek
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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----
-title: SaleSight
-emoji: 🐨
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 5.47.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Sales Forecasting with LightGBM
+
+A retail sales prediction application built with LightGBM and Gradio for interactive forecasting.
+
+## 📊 Demo
+
+![Demo Screenshot](./demo/demo.png)
+
+[Watch Demo Video](./demo/demo.mp4)
+
+## ✨ Features
+
+- Interactive web interface for sales prediction
+- Takes into account various features including:
+  - Promotional events
+  - Holiday status
+  - Historical sales data (various lags and rolling means)
+  - Temporal features (day, month, year, day of week)
+- Built with LightGBM for fast and accurate predictions
+- Simple and intuitive user interface
+
+## 🚀 Installation
+
+1. Clone the repository:
+   ```bash
+   git clone https://github.com/yourusername/sales-forecasting.git
+   cd sales-forecasting
+   ```
+
+2. Create and activate a virtual environment:
+   ```bash
+   # Create a virtual environment
+   python -m venv .venv
+   
+   # Activate it
+   # On Linux/Mac:
+   source .venv/bin/activate
+   # On Windows:
+   .venv\Scripts\activate
+   ```
+
+3. Install the required dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+## 🛠️ Usage
+
+1. Run the application:
+   ```bash
+   python app.py
+   ```
+
+2. Open your web browser and navigate to the URL shown in the terminal (typically http://localhost:7860)
+
+3. Input the required information:
+   - Promo status (0 or 1)
+   - Holiday status (0 or 1)
+   - Date in YYYY-MM-DD format
+   - Sales lags and rolling means
+
+4. Click "Predict Sales" to see the prediction
+
+## 📦 Dependencies
+
+- gradio >= 3.50.0
+- joblib >= 1.3.0
+- lightgbm >= 4.0.0
+- pandas >= 2.0.0
+
+## 🤝 Contributing
+
+Contributions are welcome! Please feel free to submit a Pull Request.
+
+1. Fork the repository
+2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
+3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
+4. Push to the branch (`git push origin feature/AmazingFeature`)
+5. Open a Pull Request
+
+## 📄 License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## 🙏 Acknowledgements
+
+- [LightGBM](https://github.com/microsoft/LightGBM) - The gradient boosting framework used for predictions
+- [Gradio](https://gradio.app/) - For the simple web interface
+- [Pandas](https://pandas.pydata.org/) - For data manipulation and analysis

app.py

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+from utils import load_artifacts, predict_sales
+from ui import build_ui
+
+if __name__ == "__main__":
+    model, feature_cols = load_artifacts()
+    iface = build_ui(model, feature_cols, predict_sales)
+    iface.launch()

demo/demo.mp4

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models/feature_cols.pkl

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models/lgb_sales_model.txt

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requirements.txt

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+gradio>=3.50.0
+joblib>=1.3.0
+lightgbm>=4.0.0
+pandas>=2.0.0

ui.py

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+import gradio as gr
+
+def build_ui(model, feature_cols, predict_fn):
+    with gr.Blocks() as demo:
+        gr.Markdown("## 🛒 Retail Sales Prediction App")
+
+        with gr.Row():
+            promo = gr.Radio([0, 1], label="Promo", value=0)
+            holiday = gr.Radio([0, 1], label="Holiday", value=0)
+
+        date = gr.Textbox(label="Date (YYYY-MM-DD)", value="2023-11-01")
+
+        with gr.Row():
+            lag_1 = gr.Number(label="Sales Lag 1 Day", value=100)
+            lag_7 = gr.Number(label="Sales Lag 7 Days", value=120)
+            mean_3 = gr.Number(label="Rolling Mean (3 Days)", value=110)
+            mean_7 = gr.Number(label="Rolling Mean (7 Days)", value=115)
+
+        predict_btn = gr.Button("Predict Sales")
+        output = gr.Number(label="Predicted Sales", precision=2)
+
+        predict_btn.click(
+            fn=lambda p, h, d, l1, l7, m3, m7: predict_fn(model, feature_cols, p, h, d, l1, l7, m3, m7),
+            inputs=[promo, holiday, date, lag_1, lag_7, mean_3, mean_7],
+            outputs=output
+        )
+
+    return demo

utils.py

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+import joblib
+import lightgbm as lgb
+import pandas as pd
+
+# Load artifacts
+def load_artifacts():
+    model = lgb.Booster(model_file="models/lgb_sales_model.txt")
+    feature_cols = joblib.load("models/feature_cols.pkl")
+    return model, feature_cols
+
+# Preprocess new input row into model-ready features
+def preprocess_input(promo, holiday, date, past_sales):
+    """
+    Args:
+
+        promo: int (0/1)
+        holiday: int (0/1)
+        date: datetime-like
+        past_sales: dict with keys ['lag_1','lag_7','mean_3','mean_7']
+
+    Returns:
+        pd.DataFrame with a single row ready for prediction
+    """
+    date = pd.to_datetime(date)
+
+    features = {
+        "promo": promo,
+        "holiday": holiday,
+        "day": date.day,
+        "month": date.month,
+        "year": date.year,
+        "day_of_week": date.weekday(),
+        "is_weekend": 1 if date.weekday() >= 5 else 0,
+        "sales_lag_1": past_sales.get("lag_1", 0),
+        "sales_lag_7": past_sales.get("lag_7", 0),
+        "rolling_mean_3": past_sales.get("mean_3", 0),
+        "rolling_mean_7": past_sales.get("mean_7", 0),
+    }
+
+    return pd.DataFrame([features])
+
+# Prediction
+def predict_sales(model, feature_cols, promo, holiday, date, lag_1, lag_7, mean_3, mean_7):
+    past_sales = {
+        "lag_1": lag_1,
+        "lag_7": lag_7,
+        "mean_3": mean_3,
+        "mean_7": mean_7,
+    }
+    X = preprocess_input(promo, holiday, date, past_sales)
+    X = X[feature_cols]  # ensure correct column order
+    prediction = model.predict(X)[0]
+    return round(prediction, 2)