Upload 2 files

app.py

@@ -0,0 +1,83 @@
+import joblib
+from flask import Flask, request, jsonify
+import pandas as pd
+import numpy as np
+
+# Create the Flask application instance
+app = Flask(__name__)
+
+# Load the pre-trained model and preprocessor
+try:
+    model = joblib.load("xgboost_model.joblib")
+    onehot_encoder = joblib.load("onehot_encoder.joblib")
+    print("Model and encoder loaded successfully.")
+except FileNotFoundError:
+    print("Error: Model or encoder file not found.")
+    model = None
+    onehot_encoder = None
+    
+# Define the categorical columns used in the original training
+categorical_cols = [
+    'Product_Sugar_Content', 'Product_Type', 'Store_Size',
+    'Store_Location_City_Type', 'Store_Type'
+]
+
+# Define the numerical columns used in the original training
+numerical_cols = [
+    'Product_Weight', 'Product_Allocated_Area', 'Product_MRP',
+    'Store_Establishment_Year'
+]
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    """
+    Endpoint to make predictions on new data.
+    Input should be a JSON object with the following keys:
+    - Product_Weight (float)
+    - Product_Sugar_Content (string)
+    - Product_Allocated_Area (float)
+    - Product_Type (string)
+    - Product_MRP (float)
+    - Store_Establishment_Year (int)
+    - Store_Size (string)
+    - Store_Location_City_Type (string)
+    - Store_Type (string)
+    """
+    if model is None or onehot_encoder is None:
+        return jsonify({"error": "Model not loaded. Check server logs."}), 500
+
+    try:
+        # Get JSON data from the request
+        data = request.get_json(silent=True)
+        if not data:
+            return jsonify({"error": "No data provided or invalid JSON format."}), 400
+
+        # Create a DataFrame from the input data
+        input_df = pd.DataFrame([data])
+        
+        # Preprocess the data using the loaded OneHotEncoder
+        encoded_features = onehot_encoder.transform(input_df[categorical_cols]).toarray()
+        
+        # Create a DataFrame for the encoded categorical features
+        encoded_df = pd.DataFrame(encoded_features, columns=onehot_encoder.get_feature_names_out(categorical_cols))
+
+        # Combine numerical and encoded categorical features
+        final_df = pd.concat([input_df[numerical_cols], encoded_df], axis=1)
+
+        # Make a prediction
+        prediction = model.predict(final_df)
+        
+        # Format the response
+        response = {
+            "prediction": float(prediction[0])
+        }
+
+        return jsonify(response), 200
+
+    except KeyError as e:
+        return jsonify({"error": f"Missing feature in request: {e}"}), 400
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000, debug=False)

requirements.txt

@@ -0,0 +1,2 @@
+Flask
+gunicorn