Update app.py

app.py

@@ -64,6 +64,18 @@ def get_result_df():
     df = pd.read_csv(CSV_DIR)[COLUMN_NAMES]
     df = df.sort_values(by=AVGACC, ascending=False)
     return df
+    
+def check_json(prediction_content):
+    
+    predictions = prediction_content.split("\n")
+    for prediction in predictions:
+        try:
+            prediction = json.loads(prediction)
+        except json.JSONDecodeError:
+            print(f"Warning: Skipping invalid JSON data in line: {prediction}")
+            return False
+    return True
+    
 def prediction_analyse(prediction_content,questiontype_list):
     predictions = prediction_content.split("\n")
 
@@ -128,6 +140,8 @@ def add_new_eval(
 ):
     if len(model_name_textbox) == 0:
         return "Error! Empty model name!", get_result_df()
+    if len(model_link) == 0:
+        return "Error! Empty model link!", get_result_df()
         
     if input_file is None:
         return "Error! Empty file!", get_result_df()
@@ -144,7 +158,9 @@ def add_new_eval(
         userdata = content.split('\n')
         if len(userdata) != count_lines(GT_PATH):
             return f"Error! The number of lines in the submit file ({len(userdata)}) does not match the number of lines in the AUTO-EVAL-VIDEO.json file ({count_lines(GT_PATH)}).", get_result_df()
-        
+        if not check_json(content):
+            return "JSON DECODE ERROR!", get_result_df()
+            
         prediction = prediction_analyse(content,questiontype)
 
         each_task_accuracy = {i: round(prediction[i]["correct"] / max(1, prediction[i]["total"]) * 100, 1) for i in questiontype}