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Dockerfile

@@ -7,7 +7,12 @@ WORKDIR /app
 COPY . .
 
 # Install dependencies from the requirements file without using cache to reduce image size
-RUN pip install --no-cache-dir --upgrade -r requirements.txt
+RUN pip install --upgrade pip
+RUN pip install --no-cache-dir -r requirements.txt
+
+# expose port (Spaces listens on 7860)
+ENV PORT=7860
+EXPOSE 7860
 
 # Define the command to start the application using Gunicorn with 4 worker processes
 # - '-w 4': Uses 4 worker processes for handling requests

app.py

@@ -1,4 +1,5 @@
 
+# backend_files/app.py
 import numpy as np
 import pandas as pd
 import joblib
@@ -7,28 +8,21 @@ from flask import Flask, request, jsonify
 # initiate flask application
 sales_price_prediction_api = Flask("SuperKart Sales Price Prediction API")
 
-# Load the trained model
+# Load the trained model (ensure file exists in repo root or adjust path)
 model = joblib.load("superkart_prediction.joblib")
 
 
-# -------------------- HOME ROUTE --------------------
+# HOME ROUTE
 @sales_price_prediction_api.get("/")
 def home():
     return "Welcome to the SuperKart Sales Prediction API!"
 
 
-# -------------------- SINGLE SALES PREDICTION --------------------
+# SINGLE SALES PREDICTION
 @sales_price_prediction_api.post("/v1/sales")
 def predict_sales_single():
-    """
-    Handles POST requests to predict sales for a single product.
-    Expects JSON input with required features.
-    """
-
     try:
-        sales_data = request.get_json()
-
-        # Extract features
+        sales_data = request.get_json(force=True)
         sample = {
             'Product_Id': sales_data.get('Product_Id'),
             'Product_Weight': sales_data.get('Product_Weight'),
@@ -42,66 +36,36 @@ def predict_sales_single():
             'Store_Location_City_Type': sales_data.get('Store_Location_City_Type'),
             'Store_Type': sales_data.get('Store_Type')
         }
-
-        # Convert into DataFrame
         input_df = pd.DataFrame([sample])
-
-        # Predict log-sales
         log_pred = model.predict(input_df)[0]
-
-        # Convert log prediction to actual sales
         predicted_sale = round(float(np.exp(log_pred)), 2)
-
-        return jsonify({"predicted_sales_in_dollars": predicted_sale})
-
+        return jsonify({"predicted_sales_in_dollars": predicted_sale}), 200
     except Exception as e:
         return jsonify({"error": str(e)}), 400
 
 
-# -------------------- BATCH SALES PREDICTION --------------------
+# BATCH SALES PREDICTION
 @sales_price_prediction_api.post("/v1/sales/batch")
 def predict_sales_batch():
-    """
-    Handles batch prediction using uploaded CSV.
-    Returns a dict of ID → predicted_sales.
-    """
-
     try:
-        # Get uploaded CSV file
         file = request.files.get("file")
         if file is None:
             return jsonify({"error": "No CSV file uploaded under key 'file'"}), 400
-
         df = pd.read_csv(file)
-
-        # Predict log-sales
         log_preds = model.predict(df).tolist()
-
-        # Convert log values to sales
         predictions = [round(float(np.exp(p)), 2) for p in log_preds]
-
-        # Determine ID column
-        id_col = None
-        for col in ["id", "ID", "Product_Id"]:
-            if col in df.columns:
-                id_col = col
-                break
-
+        id_col = next((c for c in ("id", "ID", "Product_Id") if c in df.columns), None)
         if id_col:
             ids = df[id_col].astype(str).tolist()
             result = dict(zip(ids, predictions))
         else:
-            # fallback to index-based prediction
             result = {str(i): predictions[i] for i in range(len(predictions))}
-
-        return jsonify({"predictions": result})
-
+        return jsonify({"predictions": result}), 200
     except Exception as e:
         return jsonify({"error": str(e)}), 400
 
 
-# -------------------- RUN APP LOCALLY --------------------
-# Hugging Face uses gunicorn to run "app", so we expose the variable below:
+# expose WSGI callable expected by Gunicorn
 app = sales_price_prediction_api
 
 if __name__ == "__main__":

requirements.txt

@@ -1,13 +1,13 @@
-pandas == 2.2.2
-numpy == 2.0.2
+Flask==2.2.2
+flask-cors==3.0.10
+joblib==1.4.2
+numpy==2.0.2
+pandas==2.2.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+gunicorn==20.1.0
+requests==2.28.1
 scikit-learn == 1.6.1
-xgboost == 2.1.4
-joblib == 1.4.2
 Werkzeug == 2.2.2
-flask == 2.2.2
-gunicorn == 20.1.0
-requests == 2.28.1
 uvicorn[standard]
 streamlit == 1.43.2
-flask-cors
-Flask