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Dockerfile

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+# Use Python base image
+FROM python:3.11-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy files
+COPY requirements.txt .
+COPY app.py .
+COPY random_forest_final_model.pkl .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose port
+EXPOSE 7860
+
+# Run the Flask app
+CMD ["python", "app.py"]

app.py

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+from flask import Flask, request, jsonify
+import pandas as pd
+import joblib
+
+# Load model
+model = joblib.load("random_forest_final_model.pkl")
+
+# Create Flask app
+app = Flask(__name__)
+
+# Route: Home
+@app.route('/')
+def home():
+    return "Welcome to SuperKart Sales Forecasting API"
+
+# Route: Predict
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        data = request.get_json()
+        df = pd.DataFrame([data])  # convert dict to DataFrame
+        prediction = model.predict(df)[0]
+        return jsonify({'Predicted_Sales': round(prediction, 2)})
+    except Exception as e:
+        return jsonify({'error': str(e)})
+
+# Run the app
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860)

random_forest_final_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d953ab4ebf91c355975bea9044c0517d2688e61a4d506b9d4ff9fdd59c335705
+size 51371994

requirements.txt

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+flask
+pandas
+numpy
+fastapi
+uvicorn
+joblib
+scikit-learn==1.6.1
+xgboost