Upload folder using huggingface_hub

app.py

@@ -73,41 +73,41 @@ 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
+# @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 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)
+#     # 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)
+#     # 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)}
+#     # 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)
+#     return jsonify(response)
 
 
 if __name__ == '__main__':