Update app.py

app.py

@@ -1,3 +1,4 @@
+import os
 import joblib
 
 import gradio as gr
@@ -30,6 +31,9 @@ clarity_input = gr.Dropdown(
 report_input = gr.Dropdown(['GIA', 'IGI', 'HRD', 'AGS'], label="Report")
 type_input = gr.Dropdown(['Natural', 'Lab Grown'], label="Type")
 
+hf_token = os.environ["HF_TOKEN"]
+hf_writer = gr.HuggingFaceDatasetSaver(hf_token, "pgurazada1/diamond-price-predictor-logs")
+
 model_output = gr.Label(label="Predicted Price")
 
 def predict_price(carat, shape, cut, color, clarity, report, type):
@@ -53,7 +57,9 @@ demo = gr.Interface(fn=predict_price,
                     title="Diamond Price Predictor",
                     description="This API allows you to predict the price of a diamond given its attributes",
                     allow_flagging="manual",
-                    flagging_options=["Incorrect", "Correct"])
+                    flagging_options=["Incorrect", "Correct"],
+                    flagging_callback=hf_writer
+)
 
 demo.queue(concurrency_count=8)
 demo.launch(share=False)