Update app.py

app.py

@@ -65,13 +65,40 @@ outputs = [gr.Dataframe(
     #return pd.DataFrame(predictions, columns=["Depression"])
 
 
+#def infer(inputs):
+    #data = pd.DataFrame(inputs, columns=headers)
+    
+    # Replace empty strings with NaN
+    #data = data.replace('', np.nan)
+    
+    # Add missing columns with default values (e.g., 0)
+    #for col in all_headers:
+        #if col not in data.columns:
+            #data[col] = 0
+    
+    # Ensure the order of columns matches the training data
+    #data = data[all_headers]
+    
+    # Fill NaN values with default values (e.g., 0)
+    #data = data.fillna(0)
+    
+    # Convert all data to float
+    #data = data.astype(float)
+    
+    #predictions = pipe.predict(data)
+    #return pd.DataFrame(predictions, columns=["Name", "Depression"])
+    #return pd.DataFrame({
+        #'Name': data['Name'],
+        #'Depression': predictions
+    #})
+
 def infer(inputs):
     data = pd.DataFrame(inputs, columns=headers)
     
     # Replace empty strings with NaN
     data = data.replace('', np.nan)
     
-    # Add missing columns with default values (e.g., 0)
+    # Add missing columns with default values
     for col in all_headers:
         if col not in data.columns:
             data[col] = 0
@@ -79,21 +106,26 @@ def infer(inputs):
     # Ensure the order of columns matches the training data
     data = data[all_headers]
     
-    # Fill NaN values with default values (e.g., 0)
+    # Fill NaN values
     data = data.fillna(0)
     
-    # Convert all data to float
-    data = data.astype(float)
+    # Store the Name column before conversion
+    names = data['Name'].copy()
+    
+    # Convert numeric columns to float, excluding 'Name'
+    numeric_columns = [col for col in all_headers if col != 'Name']
+    data[numeric_columns] = data[numeric_columns].astype(float)
     
+    # Make predictions
     predictions = pipe.predict(data)
-    #return pd.DataFrame(predictions, columns=["Name", "Depression"])
+    
+    # Create output DataFrame with original names and predictions
     return pd.DataFrame({
-        'Name': data['Name'],
+        'Name': names,
         'Depression': predictions
     })
 
 
-
 gr.Interface(
     fn=infer,
     inputs=inputs,