Update app.py

app.py

@@ -1,24 +1,13 @@
-# 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."
 
-df = pd.read_csv('data.csv')
-headers = df.columns.tolist()
-
-inputs = [gr.Dataframe(headers=headers, row_count = (8, "dynamic"), datatype='number', col_count=(4,"fixed"), label="Dataset", interactive=1)]
-outputs = [gr.ScatterPlot(), gr.Image(), gr.Dataframe(row_count = (2, "dynamic"), col_count=(2, "fixed"), datatype='number', label="Results", headers=["True Loss", "Pred Loss"])]
-
 def infer(inputs):
     df = pd.DataFrame(inputs, columns=headers)
     
@@ -177,5 +166,27 @@ def infer(inputs):
         gr.Image(Image.open('tmp.png')),
         df_val[['Target', 'Prediction']], ]
 
-gr.Interface(infer, inputs = inputs, outputs = outputs, title = title,
-                description = description, examples=[df], cache_examples=False, allow_flagging='never').launch(debug=False)
+def display_csv(file):
+    df = pd.read_csv(file.name, 
+                    #  encoding='utf-8'
+                     )
+    # Return as formatted string
+    # print(df.head())
+    return df
+
+df = pd.read_csv('data.csv')
+headers = df.columns.tolist()
+
+inputs = [gr.Dataframe(headers=headers, row_count = (8, "dynamic"), datatype='number', col_count=(4,"fixed"), label="Dataset", interactive=1)]
+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")
+
+    gr.Interface(infer, inputs=inputs, outputs=outputs, title = title,
+                description = description, examples=[df], cache_examples=False, allow_flagging='never')
+    
+demo.launch(debug=False)