Update app.py

app.py

@@ -1,14 +1,19 @@
+# import sklearn
 import gradio as gr
+# import joblib
 import pandas as pd
 import numpy as np
 import lightgbm as lgb
 from sklearn.model_selection import train_test_split
 from PIL import Image
+# import datasets
+
+# pipe = joblib.load("./model.pkl")
 
 title = "RegMix"
 description = "TBD."
 
-def infer(inputs):
+def infer(inputs, additional_inputs):
     df = pd.DataFrame(inputs, columns=headers)
     
     X_columns = df.columns[0:-1]
@@ -166,7 +171,7 @@ def infer(inputs):
         gr.Image(Image.open('tmp.png')),
         df_val[['Target', 'Prediction']], ]
 
-def display_csv(file):
+def upload_csv(file):
     df = pd.read_csv(file.name, 
                     #  encoding='utf-8'
                      )
@@ -181,12 +186,20 @@ inputs = [gr.Dataframe(headers=headers, row_count = (8, "dynamic"), datatype='nu
 outputs = [gr.ScatterPlot(), gr.Image(), gr.Dataframe(row_count = (2, "dynamic"), col_count=(2, "fixed"), datatype='number', label="Results", headers=["True Loss", "Pred Loss"])]
 
 with gr.Blocks() as demo:
+
+    ####
     upload_button = gr.UploadButton(label="Upload", file_types = ['.csv'], 
                                     # live=True, 
-                                    file_count = "single")    
-    upload_button.upload(fn=display_csv, inputs=upload_button, outputs=inputs, api_name="upload_csv")
+                                    file_count = "single", render=False)    
+    upload_button.upload(fn=upload_csv, inputs=upload_button, outputs=inputs, api_name="upload_csv")
+    ####
 
-    gr.Interface(infer, inputs=inputs, outputs=outputs, title = title,
-                description = description, examples=[df], cache_examples=False, allow_flagging='never')
+    gr.Interface(infer, inputs=inputs, outputs=outputs, title=title,
+                additional_inputs = [upload_button], 
+                additional_inputs_accordion='Upload CSV',
+                description = description, 
+                examples=[[df], []], 
+                cache_examples=False, allow_flagging='never')
     
+
 demo.launch(debug=False)