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Dockerfile

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+FROM python:3.9-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements and app code from build context
+# We copy requirements first to leverage layer caching
+COPY ..
+
+# Upgrade pip and install dependencies
+RUN pip install --upgrade pip
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose the port (matches app.run host/port)
+EXPOSE 7860
+
+# Start with gunicorn; 'app:app' expects app.py to define 'app = Flask(...)'
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:app"]

app.py

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+
+# ----------------------------
+# Config / Model path
+# ----------------------------
+MODEL_PATH = os.path.join("backend_files", "superKart_price_prediction_model_v1_0.joblib")
+
+# ----------------------------
+# Initialize app and load model
+# ----------------------------
+app = Flask("SuperKart Sales Predictor")
+
+# Load model
+if not os.path.exists(MODEL_PATH):
+    raise FileNotFoundError(f"Model file not found at {MODEL_PATH}. ")
+model = joblib.load(MODEL_PATH)
+
+# These are the raw input feature names before preprocessing
+NUMERIC_COLS = ['Product_Weight', 'Product_Allocated_Area', 'Product_MRP', 'Store_Age']
+CATEGORICAL_COLS = ['Product_Sugar_Content', 'Product_Type', 'Store_Size',
+                    'Store_Location_City_Type', 'Store_Type']
+
+EXPECTED_COLUMNS = NUMERIC_COLS + CATEGORICAL_COLS
+
+# ----------------------------
+# Utility function
+# ----------------------------
+def validate_and_prepare_input(df: pd.DataFrame):
+    """
+    Ensure the dataframe has the required columns. If Store_Establishment_Year
+    is provided instead of Store_Age, it will be converted to Store_Age.
+    Returns the prepared dataframe and a list of missing columns (empty if ok).
+    """
+    df = df.copy()
+    missing = [c for c in EXPECTED_COLUMNS if c not in df.columns]
+
+    # Code for if user provided Store_Establishment_Year, convert to Store_Age
+    if 'Store_Establishment_Year' in df.columns and 'Store_Age' in missing:
+        df['Store_Age'] = 2025 - df['Store_Establishment_Year']
+        missing = [c for c in EXPECTED_COLUMNS if c not in df.columns]
+
+    return df, missing
+
+# ----------------------------
+# Routes
+# ----------------------------
+@app.get("/")
+def home():
+    """Health check / Landing page"""
+    return jsonify({
+        "service": "SuperKart Sales Predictor",
+        "status": "running"
+    })
+
+@app.post("/v1/predict")
+def predict_single():
+    """
+    Predict sales for a single product-store record.
+
+    Expected JSON schema (example):
+    {
+      "Product_Weight": 12.5,
+      "Product_Allocated_Area": 0.056,
+      "Product_MRP": 149.0,
+      "Store_Age": 16,
+      "Product_Sugar_Content": "Low Sugar",
+      "Product_Type": "Dairy",
+      "Store_Size": "High",
+      "Store_Location_City_Type": "Tier 1",
+      "Store_Type": "Supermarket Type 1"
+    }
+    """
+    try:
+        data = request.get_json(force=True)
+        if not isinstance(data, dict):
+            return jsonify({"error": "Input JSON must be an object/dict"}), 400
+
+        # Convert to DataFrame
+        input_df = pd.DataFrame([data])
+
+        # Validate and prepare
+        input_df, missing = validate_and_prepare_input(input_df)
+        if missing:
+            return jsonify({"error": "Missing required columns", "missing_columns": missing}), 400
+
+        # Keep only expected columns (ignore extra fields)
+        input_df = input_df[EXPECTED_COLUMNS]
+
+        # Predict using pipeline (pipeline will apply preprocessors)
+        pred = model.predict(input_df)
+        prediction_value = float(pred[0])
+
+        return jsonify({"prediction": prediction_value}), 200
+
+    except Exception as e:
+        return jsonify({"error": "Exception during prediction", "details": str(e)}), 500
+
+@app.post("/v1/predict_batch")
+def predict_batch():
+    """
+    Predict sales for a batch of records supplied as a CSV file upload.
+    The CSV should contain the expected columns (or Store_Establishment_Year
+    instead of Store_Age which will be converted automatically).
+    """
+    try:
+        if 'file' not in request.files:
+            return jsonify({"error": "No file part in the request. Upload a CSV file with key 'file'."}), 400
+
+        file = request.files['file']
+        if file.filename == "":
+            return jsonify({"error": "Empty filename. Please upload a CSV file."}), 400
+
+        # Read CSV
+        input_df = pd.read_csv(file)
+
+        input_df, missing = validate_and_prepare_input(input_df)
+        if missing:
+            return jsonify({"error": "Missing required columns in uploaded CSV", "missing_columns": missing}), 400
+
+        # Keep only expected columns and predict
+        input_df = input_df[EXPECTED_COLUMNS]
+        preds = model.predict(input_df)
+
+        # Return predictions aligned with original input index
+        output = input_df.copy()
+        output['predicted_Product_Store_Sales_Total'] = preds.astype(float)
+
+        # Convert to records for JSON response (limit size if necessary)
+        results = output.reset_index().to_dict(orient='records')
+
+        return jsonify({"predictions_count": len(results), "predictions": results}), 200
+
+    except Exception as e:
+        return jsonify({"error": "Exception during batch prediction", "details": str(e)}), 500
+
+# ----------------------------
+# Run app
+# ----------------------------
+if __name__ == "__main__":
+    # Listen on 0.0.0.0 for containerized environments. In dev, use port 7860 or 5000 as required.
+    app.run(host="0.0.0.0", port=7860, debug=False)

requirements.txt

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+numpy==2.0.2
+pandas==2.2.2
+scikit-learn==1.6.1
+joblib==1.4.2
+xgboost==2.1.4
+gunicorn==20.1.0
+flask==3.0.3
+requests==2.32.3
+huggingface_hub==0.30.1

superKart_price_prediction_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3e62fab907d5931f97fd4bc4b69136940967359e97dbf69592fe221a457c91c0
+size 27090851