Update app.py

app.py

@@ -64,9 +64,7 @@ def compute(table: np.array):
     
     ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train_index, cmap=cm_bright)
     
-    classifier = TabPFNClassifier(device='cpu', base_path='/home/hollmann/',
-                                model_string=model_string, N_ensemble_configurations=1
-                          , no_preprocess_mode=False, i=i, feature_shift_decoder=True, multiclass_decoder='permutation')
+    classifier = TabPFNClassifier(base_path=tabpfn_path, device='cpu')
     classifier.fit(x_train[:, 0:2], y_train)
     
     DecisionBoundaryDisplay.from_estimator(
@@ -86,13 +84,14 @@ def upload_file(file, remove_entries=10):
             dataset_format="array"
         )
         df = pd.DataFrame(X_, columns=attribute_names_)
-        headers = df.columns
+        headers = np.arange(len(df.columns))
+        df.columns = headers
     elif file.name.endswith('.csv') or file.name.endswith('.data'):
         df = pd.read_csv(file.name, header='infer')
-        headers = df.columns
-        #df.columns = np.arange(len(df.columns))
+        headers = np.arange(len(df.columns))
+        df.columns = headers
         
-    df.iloc[0:remove_entries, -1] = '(predict)'
+    df.iloc[0:remove_entries, -1] = ''
     return df
 
 
@@ -110,7 +109,7 @@ def update_table(table):
     y_column = empty_inds[1][0]
     eval_lines = empty_inds[0]
     
-    table.iloc[eval_lines, y_column] = '(predict)'
+    table.iloc[eval_lines, y_column] = ''
     table.columns = headers
     
     return table
@@ -124,28 +123,31 @@ gr.Markdown("""This demo allows you to play with the **TabPFN**.
                 """)
 
 with gr.Blocks() as demo:
-    with gr.Tab("Enter Input Data"):
-        inp_file = gr.File(
-        label='Drop either a .csv (without header, only numeric values for all but the labels) or a .arff file.')
-        
-        inp_table = gr.DataFrame(type='numpy', value=upload_file(Path('iris.csv'), remove_entries=10), headers=[''] * 3)
-        inp_table.change(fn=update_table, inputs=inp_table, outputs=inp_table)
-    
-    with gr.Tab("Run Predictions"):
-        
-        btn = gr.Button("Start")
-        out_text = gr.Markdown()
-        out_table = gr.DataFrame()
-        out_plot = gr.Plot()
-    
-    btn.click(fn=compute, inputs=inp_table, outputs=[out_text, out_table, out_plot])
-    
-    examples = gr.Examples(examples=['iris.csv', 'balance-scale.arff'],
+    with gr.Row():
+        with gr.Column(scale=1):
+            inp_table = gr.DataFrame(type='numpy', value=upload_file(Path('iris.csv'), remove_entries=10)
+                                     , headers=[''] * 3)
+            
+            inp_file = gr.File(
+            label='Drop either a .csv (without header, only numeric values for all but the labels) or a .arff file.')
+            
+            examples = gr.Examples(examples=['iris.csv', 'balance-scale.arff'],
                            inputs=[inp_file],
                            outputs=[inp_table],
                            fn=upload_file,
                            cache_examples=True)
+            
+            #inp_table.change(fn=update_table, inputs=inp_table, outputs=inp_table)
+    
+        with gr.Column(scale=1):
+
+            btn = gr.Button("Calculate Predictions")
+            out_text = gr.Markdown()
+            out_plot = gr.Plot()
+            out_table = gr.DataFrame()
+    
+    btn.click(fn=compute, inputs=inp_table, outputs=[out_text, out_table, out_plot])
 
     inp_file.change(fn=upload_file, inputs=inp_file, outputs=inp_table)
 
-demo.launch()
+demo.launch(share=True)