Upload folder using huggingface_hub

app.py

@@ -72,5 +72,43 @@ def predict_sales():
     
     return jsonify({'Predicted Sales Total (in dollars)': predicted_sales})
 
+
+@super_kart_api.post('/v1/salesbatch')
+def predict_sales_batch():
+    """
+    This function handles POST requests to the '/v1/salesbatch' endpoint.
+    It expects a CSV file containing product details for multiple products
+    and returns the predicted sales totals as a dictionary in the JSON response.
+    """
+    if 'file' not in request.files:
+        return jsonify({'error': 'No file part in the request'}), 400
+    file = request.files['file']
+    try:
+        data = pd.read_csv(file)
+    except Exception as e:
+        return jsonify({'error': f'Failed to read CSV: {str(e)}'}), 400
+    
+    # Apply one-hot encoding
+    data = pd.get_dummies(data, columns=['Product_Type', 'Store_Type'], drop_first=True)
+    
+    # Apply ordinal encoding
+    sugar_mapping = {'No Sugar': 0, 'Low Sugar': 1, 'Regular': 2}
+    size_mapping = {'Small': 0, 'Medium': 1, 'High': 2}
+    city_mapping = {'Tier 3': 0, 'Tier 2': 1, 'Tier 1': 2}
+    data['Product_Sugar_Content'] = data['Product_Sugar_Content'].map(sugar_mapping)
+    data['Store_Size'] = data['Store_Size'].map(size_mapping)
+    data['Store_Location_City_Type'] = data['Store_Location_City_Type'].map(city_mapping)
+    
+    # Align with expected columns
+    data = data.reindex(columns=EXPECTED_COLUMNS, fill_value=0)
+    
+    # Make predictions
+    preds = model.predict(data)
+    results = preds.round(2).tolist()
+    response = {str(i): float(val) for i, val in enumerate(results)}
+    
+    return jsonify(response)
+
+
 if __name__ == '__main__':
     super_kart_api.run(debug=True)