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Dockerfile

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+FROM python:3.9-slim
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Copy all files from current directory to container
+COPY . .
+
+# Install dependencies
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Run Flask app with Gunicorn
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:superkart_api"]

Gradient-Boosting-Regressor-Tuned.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c173a318702c4f688b645fcebdf75eb410a1dc4db64db148c83e7ae485123798
+size 53708

app.py

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+
+# Import necessary libraries
+import numpy as np
+import joblib  # For loading the serialized model
+import polars as pl  # Polars instead of pandas
+from flask import Flask, request, jsonify  # Flask API
+
+# Initialize Flask app with a name
+superkart_api = Flask("SuperKart_API")
+
+# Load trained model pipeline
+model = joblib.load("backend_files/Gradient-Boosting-Regressor-Tuned.joblib")
+
+# Home route
+@superkart_api.get('/')
+def home():
+    return "SuperKart Sales Prediction API is running"
+
+# Prediction endpoint
+@superkart_api.post('/v1/predict')
+def predict_sales():
+    # Get JSON input
+    data = request.get_json()
+
+    # Extract features
+    required_fields = [
+        'Product_Weight',
+        'Product_Sugar_Content',
+        'Product_Allocated_Area',
+        'Product_MRP',
+        'Store_Size',
+        'Store_Location_City_Type',
+        'Store_Type',
+        'Product_Id_char',
+        'Store_Age_Years',
+        'Product_Type_Category'
+    ]
+
+    # Check for missing or null values
+    missing_fields = [
+        field for field in required_fields
+        if field not in data or data[field] is None or data[field] == ""
+    ]
+
+    # If any field is missing → return error
+    if missing_fields:
+        return jsonify({
+            "error": "Missing or empty fields in request body",
+            "missing_fields": missing_fields,
+            "expected_format": {
+                "Product_Weight": "float",
+                "Product_Sugar_Content": "string",
+                "Product_Allocated_Area": "float",
+                "Product_MRP": "float",
+                "Store_Size": "string",
+                "Store_Location_City_Type": "string",
+                "Store_Type": "string",
+                "Product_Id_char": "string",
+                "Store_Age_Years": "float",
+                "Product_Type_Category": "string"
+            }
+        }), 400
+
+    # Safe to build sample
+    sample = {
+        'Product_Weight': data['Product_Weight'],
+        'Product_Sugar_Content': data['Product_Sugar_Content'],
+        'Product_Allocated_Area': data['Product_Allocated_Area'],
+        'Product_MRP': data['Product_MRP'],
+        'Store_Size': data['Store_Size'],
+        'Store_Location_City_Type': data['Store_Location_City_Type'],
+        'Store_Type': data['Store_Type'],
+        'Product_Id_char': data['Product_Id_char'],
+        'Store_Age_Years': data['Store_Age_Years'],
+        'Product_Type_Category': data['Product_Type_Category']
+    }
+
+    # Convert to Polars DataFrame
+    input_data = pl.DataFrame([sample])
+
+    # Convert to numpy for sklearn compatibility (safe step)
+    prediction = model.predict(input_data.to_pandas()).tolist()[0]
+
+    return jsonify({'Sales': prediction})
+
+
+# Run app
+if __name__ == '__main__':
+    superkart_api.run(debug=True)

requirements.txt

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+annotated-types==0.7.0
+anyio==4.13.0
+argon2-cffi==25.1.0
+argon2-cffi-bindings==25.1.0
+arrow==1.4.0
+asttokens==3.0.1
+async-lru==2.3.0
+attrs==26.1.0
+babel==2.18.0
+beautifulsoup4==4.14.3
+bleach==6.3.0
+blinker==1.9.0
+certifi==2026.4.22
+cffi==2.0.0
+charset-normalizer==3.4.7
+click==8.3.3
+comm==0.2.3
+contourpy==1.3.3
+cycler==0.12.1
+dacite==1.9.2
+debugpy==1.8.20
+decorator==5.2.1
+defusedxml==0.7.1
+executing==2.2.1
+fastjsonschema==2.21.2
+fg-data-profiling==4.19.1
+gunicorn==21.2.0