Update app.py

app.py

@@ -14,9 +14,10 @@ class DataList:
         self.table_header = '''
             <tr>
                 <td width="15%">Name</td>
-                <td width="52%">Description</td>
+                <td width="30%">Description</td>
                 <td width="15%">Module</td>
                 <td width="20%">Type (Format)</td>
+                <td width="20%">Hashtags (Format)</td>
             </tr>'''
 
     def _preprocess_table(self) -> None:
@@ -32,34 +33,38 @@ class DataList:
                     <td>{row.Description}</td>
                     <td>{row.Module}</td>
                     <td>{row.Type}</td>
+                    <td>{row.Hashtags}</td>
                 </tr>'''
             rows.append(row)
         self.table['html_table_content'] = rows
 
     def render(self, search_query: str,
             filter_names: list[str],
-            filter_names2: list[str], ) -> tuple[int, str]:
+            filter_names2: list[str],
+            filter_names3: list[str],) -> tuple[int, str]:
         self.table = pd.read_csv(csv_url)
         self._preprocess_table()
 
         self.table_header = '''
             <tr>
                 <td width="15%">Name</td>
-                <td width="52%">Description</td>
+                <td width="30%">Description</td>
                 <td width="15%">Module</td>
                 <td width="20%">Type (Format)</td>
+                <td width="20%">Hashtags (Format)</td>
             </tr>'''
         df = self.table
         if search_query:
                 df = df[df.name_lowercase.str.contains(search_query.lower())]
-        df = self.filter_table(df, filter_names,filter_names2)
+        df = self.filter_table(df, filter_names,filter_names2,filter_names3)
         result = self.to_html(df, self.table_header)
         return result
 
     @staticmethod
-    def filter_table(df: pd.DataFrame, filter_names: list[str], filter_names2: list[str]) -> pd.DataFrame:
+    def filter_table(df: pd.DataFrame, filter_names: list[str], filter_names2: list[str], filter_names3: list[str]) -> pd.DataFrame:
         df = df.loc[df.Module.isin(set(filter_names))]
         df= df[df.Type.str.contains('|'.join(filter_names2),case=False, regex=True)]
+        df= df[df.Type.str.contains('|'.join(filter_names3),case=False, regex=True)]
         return df
 
     @staticmethod
@@ -98,11 +103,121 @@ with gr.Blocks(css=css) as demo:
     search_box = gr.Textbox( label='Search Name', placeholder='You can search for titles with regular expressions. e.g. (?<!sur)face',max_lines=1, scale = 5)  
     filter_names = gr.CheckboxGroup(choices=["Responsible AI 101", "Responsible AI Governance", "AI System Risk and Assurance", "Generative and Agentic AI", "AI Regulation and Compliance", "AI Procurement"], value=["Responsible AI 101", "Responsible AI Governance", "AI System Risk and Assurance", "Generative and Agentic AI", "AI Regulation and Compliance", "AI Procurement"], label='Type')
     filter_names2 = gr.CheckboxGroup(choices=['Deck','Document/Report','Infogrphic','Video/Audio','Repository/Directory',], value=['Deck','Document/Report','Infogrphic','Video/Audio','Repository/Directory',], label='Type (Format)')
+    filter_names3 = gr.CheckboxGroup(choices=[
+  "#AIImpacts",
+  "#AIAssessment&Audit",
+  "#AIDesign",
+  "#AIDevelopment",
+  "#AIDeployment",
+  "#AIMonitoring",
+  "#AIOperating&Maintenance",
+  "#AIDecommissioning",
+  "#AITesting&Evaluation",
+  "#AITraining",
+  "#AIStandards",
+  "#AIUseCase",
+  "#Accountability&Transparency",
+  "#CheatSheet",
+  "#Checklist",
+  "#CaseStudy",
+  "#Cybersecurity",
+  "#Data&AI",
+  "#EUAIAct",
+  "#EUGDPR",
+  "#EmergingTopic",
+  "#Explainability&Interpretability",
+  "#FactSheet",
+  "#Fairness&Bias",
+  "#FederalRegulations",
+  "#Frameworks",
+  "#FrontierAI",
+  "#ForAuditors",
+  "#ForDevelopers",
+  "#ForDeployers",
+  "#ForLeadership",
+  "#GlobalStandards",
+  "#Glossary",
+  "#Government&PublicSector",
+  "#Guide",
+  "#Healthcare",
+  "#HR",
+  "#ISO42001",
+  "#Industrial",
+  "#Manufacturing",
+  "#Marketing",
+  "#News&Journalism",
+  "#Privacy",
+  "#Report",
+  "#Retail",
+  "#Safety",
+  "#Security&Resiliency",
+  "#StateRegulations",
+  "#Template",
+  "#Toolkit",
+  "#Training",
+  "#Validity&Reliability",
+  "#WhitePaper"
+]
+, value=[
+  "#AIImpacts",
+  "#AIAssessment&Audit",
+  "#AIDesign",
+  "#AIDevelopment",
+  "#AIDeployment",
+  "#AIMonitoring",
+  "#AIOperating&Maintenance",
+  "#AIDecommissioning",
+  "#AITesting&Evaluation",
+  "#AITraining",
+  "#AIStandards",
+  "#AIUseCase",
+  "#Accountability&Transparency",
+  "#CheatSheet",
+  "#Checklist",
+  "#CaseStudy",
+  "#Cybersecurity",
+  "#Data&AI",
+  "#EUAIAct",
+  "#EUGDPR",
+  "#EmergingTopic",
+  "#Explainability&Interpretability",
+  "#FactSheet",
+  "#Fairness&Bias",
+  "#FederalRegulations",
+  "#Frameworks",
+  "#FrontierAI",
+  "#ForAuditors",
+  "#ForDevelopers",
+  "#ForDeployers",
+  "#ForLeadership",
+  "#GlobalStandards",
+  "#Glossary",
+  "#Government&PublicSector",
+  "#Guide",
+  "#Healthcare",
+  "#HR",
+  "#ISO42001",
+  "#Industrial",
+  "#Manufacturing",
+  "#Marketing",
+  "#News&Journalism",
+  "#Privacy",
+  "#Report",
+  "#Retail",
+  "#Safety",
+  "#Security&Resiliency",
+  "#StateRegulations",
+  "#Template",
+  "#Toolkit",
+  "#Training",
+  "#Validity&Reliability",
+  "#WhitePaper"
+], label='Type (Format)')
     search_button = gr.Button('Search', size = 'sm', scale =1)
     table = gr.HTML(show_label=False)
         
-    demo.load(fn=data_list.render, inputs=[search_box, filter_names,filter_names2,], outputs=[table,])
-    search_button.click(fn=data_list.render, inputs=[search_box, filter_names,filter_names2,], outputs=[table,])    
+    demo.load(fn=data_list.render, inputs=[search_box, filter_names,filter_names2,filter_names3,], outputs=[table,])
+    search_button.click(fn=data_list.render, inputs=[search_box, filter_names,filter_names2,filter_names3,], outputs=[table,])    
 
 demo.queue()
 demo.launch(share=False)