udpated so that every model uploaded is verified - Adithya S K

app.py

@@ -68,6 +68,7 @@ def main():
             for item in data:
                 model_name = item.get("name")
                 language = item.get("language")
+                is_verified= item.get("is_verified")
                 try:
                     ALL = item["result"]["all"]["acc_norm"]
                 except KeyError:
@@ -110,6 +111,7 @@ def main():
                     "Boolq": Boolq,
                     "MMLU": MMLU,
                     "Translation": Translation,
+                    "Verified": is_verified,
                 })
 
             df = pd.DataFrame(table_data)
@@ -124,7 +126,11 @@ def main():
         
         title = st.text_input('Model', placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...")
         
-        on = st.checkbox('Sort by Language')
+        option_column1, option_column2 = st.columns(2)
+        with option_column1:
+            on = st.checkbox('Sort by Language')
+        with option_column2:    
+            is_verified = st.checkbox('Verified')
         
 
         col1, col2 = st.columns(2)
@@ -137,14 +143,11 @@ def main():
                 'Pick Languages',
                 ['kannada', 'hindi', 'tamil', 'telegu','gujarati','marathi','malayalam',"english"],['kannada', 'hindi', 'tamil', 'telegu','gujarati','marathi','malayalam',"english"])
         if on:
-            # Loop through each selected language
             for language in language_options:
-                filtered_df = df[df['Language'] == language]
-                        # Check if the filtered dataframe is not empty
+                filtered_df = df[(df['Language'] == language) & (df['Verified'] == is_verified)]
                 if not filtered_df.empty:
                     st.subheader(f"{language.capitalize()[0]}{language[1:]}")
                     filtered_df.reset_index(drop=True, inplace=True)
-                    # Display filtered dataframe
                     filtered_df = get_model_info(filtered_df)
                     if title:
                         if ';' in title:
@@ -152,25 +155,19 @@ def main():
                             filtered_df = df[df['Model'].isin(model_names)]
                         else:
                             filtered_df = df[df['Model'].str.contains(title, case=False, na=False)]
-                        
+
+                        filtered_df = filtered_df[filtered_df['Language'] == language]
+                        filtered_df = filtered_df[filtered_df['Verified'] == is_verified]
                         filtered_df = filtered_df[df.columns.intersection(['Model', 'Language'] + benchmark_options)]
 
-                        # Calculate average across selected benchmark columns
                         filtered_df['Average'] = filtered_df[benchmark_options].mean(axis=1)
                         filtered_df.index += 1
                         st.dataframe(filtered_df, use_container_width=True)
                     elif benchmark_options or language_options:
                         filtered_df = filtered_df[df.columns.intersection(['Model', 'Language'] + benchmark_options)]
-
-                        # Calculate average across selected benchmark columns
                         filtered_df['Average'] = filtered_df[benchmark_options].mean(axis=1)
-                        
-                        filtered_df = get_model_info(filtered_df)
-                        filtered_df.index += 1
                         st.dataframe(filtered_df, use_container_width=True)
-            # st.write('Feature activated!')
         else:
-
             if title:
                 if ';' in title:
                     model_names = [name.strip() for name in title.split(';')]
@@ -179,22 +176,18 @@ def main():
                     filtered_df = df[df['Model'].str.contains(title, case=False, na=False)]
                 
                 filtered_df = filtered_df[filtered_df['Language'].isin(language_options)]
+                filtered_df = filtered_df[filtered_df['Verified'] == is_verified]
                 filtered_df = filtered_df[df.columns.intersection(['Model', 'Language'] + benchmark_options)]
 
-                # Calculate average across selected benchmark columns
                 filtered_df['Average'] = filtered_df[benchmark_options].mean(axis=1)
                 filtered_df.index += 1
-                # Display the filtered DataFrame
                 st.dataframe(filtered_df, use_container_width=True)
             elif benchmark_options or language_options:
                 filtered_df = df[df['Language'].isin(language_options)]
+                filtered_df = filtered_df[filtered_df['Verified'] == is_verified]
                 filtered_df = filtered_df[df.columns.intersection(['Model', 'Language'] + benchmark_options)]
 
-                # Calculate average across selected benchmark columns
                 filtered_df['Average'] = filtered_df[benchmark_options].mean(axis=1)
-                
-                filtered_df = get_model_info(filtered_df)
-                filtered_df.index += 1
                 st.dataframe(filtered_df, use_container_width=True)