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Dockerfile

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+FROM python:3.10-slim
+
+# Set environment variables
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+# Set working directory inside container
+WORKDIR /app
+
+# Copy all files to the container
+COPY . /app
+
+# Install dependencies
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r requirements.txt
+
+# Expose the port Flask will run on (Hugging Face uses 7860)
+EXPOSE 7860
+
+# Command to run the app
+CMD ["python", "app.py"]

app.py

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+from flask import Flask, request, jsonify
+import joblib
+import pandas as pd
+import os
+
+
+# Load the trained model pipeline
+model_path = os.path.join("deployment_files", "super_kart_prediction_model_v1_0.joblib")
+model = joblib.load(model_path)
+
+# Initialize the Flask app
+app = Flask(__name__)
+
+@app.route('/')
+def index():
+    return "SuperKart Sales Forecast API is running!"
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        # Get JSON data and convert to DataFrame
+        data = request.get_json()
+        df = pd.DataFrame(data)
+
+        # Make prediction
+        prediction = model.predict(df)
+
+        return jsonify({"prediction": prediction.tolist()})
+    except Exception as e:
+        return jsonify({"error": str(e)})
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860)

requirements.txt

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+flask
+scikit-learn
+pandas
+numpy
+xgboost
+joblib

super_kart_prediction_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f1ed63f9f7c8aa4cf526967cfdf5245f801b269c45a7eb8b6691ce8240f9ee65
+size 418244