Update app.py

app.py

@@ -32,7 +32,7 @@ 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, "diamond-price-predictor-logs")
+hf_writer = gr.HuggingFaceDatasetSaver(hf_token, "diamond-price-prediction-logs")
 
 model_output = gr.Label(label="Predicted Price")
 
@@ -50,14 +50,15 @@ def predict_price(carat, shape, cut, color, clarity, report, type):
     prediction = price_predictor.predict(data_point).tolist()
     return prediction[0]
 
-demo = gr.Interface(fn=predict_price,
-                    inputs=[carat_input, shape_input, cut_input, color_input, 
-                            clarity_input, report_input, type_input],
-                    outputs=model_output,
-                    title="Diamond Price Predictor",
-                    description="This API allows you to predict the price of a diamond given its attributes",
-                    allow_flagging="manual",
-                    flagging_callback=hf_writer
+demo = gr.Interface(
+    fn=predict_price,
+    inputs=[carat_input, shape_input, cut_input, color_input, 
+            clarity_input, report_input, type_input],
+    outputs=model_output,
+    title="Diamond Price Predictor",
+    description="This API allows you to predict the price of a diamond given its attributes",
+    allow_flagging="manual",
+    flagging_callback=hf_writer
 )
 
 demo.queue(concurrency_count=8)