Upload folder using huggingface_hub

Dockerfile

@@ -0,0 +1,17 @@
+# Use an official Python runtime as a parent image
+FROM python:3.9-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the current directory contents into the container at /app
+COPY . /app
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Make port 5000 available to the world outside this container
+EXPOSE 5000
+
+# Run the application using gunicorn
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:5000", "app:app"]

app.py

@@ -0,0 +1,56 @@
+import os
+from flask import Flask, request, jsonify
+import joblib
+import pandas as pd
+
+# Create a Flask application instance
+app = Flask(__name__)
+
+# Define model path
+MODEL_DIR = "model_artifacts"
+MODEL_FILENAME = "best_sales_forecast_model.joblib"
+MODEL_PATH = os.path.join(MODEL_DIR, MODEL_FILENAME)
+
+# Load model at startup
+try:
+    model = joblib.load(MODEL_PATH)
+    print(" Model loaded successfully!")
+except Exception as e:
+    print(f" Error loading model: {e}")
+    model = None
+
+# Health check route
+@app.route("/", methods=["GET"])
+def index():
+    return jsonify({"status": "Backend is running!"})
+
+# Prediction route
+@app.route("/predict", methods=["POST"])
+def predict():
+    if model is None:
+        return jsonify({"error": "Model not loaded"}), 500
+
+    try:
+        # Get request JSON
+        data = request.get_json(force=True)
+        if not data:
+            return jsonify({"error": "No input data provided"}), 400
+
+        # Convert to DataFrame
+        df = pd.DataFrame(data)
+
+        # Drop ID column if present, as it's not used in prediction
+        if "Product_Id" in df.columns:
+            df = df.drop("Product_Id", axis=1)
+
+        # Predict
+        predictions = model.predict(df)
+
+        return jsonify({"predictions": predictions.tolist()})
+
+    except Exception as e:
+        return jsonify({"error": str(e)}), 400
+
+# Entry point
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=5000)

requirements.txt

@@ -0,0 +1,5 @@
+Flask==3.0.3
+joblib==1.4.2
+pandas==2.2.2
+scikit-learn==1.6.1
+gunicorn==22.0.0