Update app.py

app.py

@@ -9,7 +9,7 @@ df = df.drop(['id'],axis=1)
 
 headers = ["diagnosis","radius_mean","texture_mean","perimeter_mean","area_mean","smoothness_mean","compactness_mean","concavity_mean","concave points_mean","symmetry_mean","fractal_dimension_mean","radius_se","texture_se","perimeter_se","area_se","smoothness_se","compactness_se","concavity_se","concave points_se","symmetry_se","fractal_dimension_se","radius_worst","texture_worst","perimeter_worst","area_worst","smoothness_worst","compactness_worst","concavity_worst","concave points_worst","symmetry_worst","fractal_dimension_worst"]
 inputs = [gr.Dataframe(headers = headers, row_count = (2, "dynamic"), col_count=(31,"dynamic"), label="Input Data", interactive=False)]
-outputs = [gr.Dataframe(row_count = (2, "dynamic"), col_count=(1, "fixed"), label="Predictions", headers=["Results"])]
+outputs = [gr.Dataframe(row_count = (2, "dynamic"), col_count=(1, "fixed"), label="Predictions", headers=["results"])]
 
 def classify_cell(df_input):
     # Dropping diagnosis
@@ -35,14 +35,15 @@ def classify_cell(df_input):
         output_string = f"{cancer_classes[cell_pred[i].argmax()]} Cancer Cell : {cell_prob}% confident"
         # Appending formatted string for a cell to cell results list
         cell_results.append(output_string)
-    return pd.DataFrame(cell_results,columns=["Results"])
+    return pd.DataFrame(cell_results,columns=["results"])
 
 demo = gr.Interface(classify_cell,
                         inputs = inputs,
-                        examples=[df.iloc[17:22]], 
                         outputs = outputs,
                         title = "Classify Cancer Cell", 
-                        description="Classifies Cancer Cell into <b>Malignant</b> or <b>Benign</b> based on its features. Click on the example to classify 5 cells! </br> First Column shows what the cell should be classified as"
-                    )
+                        description="Classifies Cancer Cell into <b>Malignant</b> or <b>Benign</b> based on its features. Click on the example to classify 5 cells! </br> First Column shows what the cell should be classified as",
+                        examples=[df.iloc[17:22]],
+                        cache_examples=False
+                   )
                     
 demo.launch(inline=False)