Update app.py

app.py

@@ -34,3 +34,250 @@ summ_eval_metrics = ['BLEU', 'CHARF', 'TER']
 qas_eval_metrics = ['F1', 'EXACT-MATCH']
 mts_eval_metrics = ['CHARF', 'BLEU', 'TER']
 mcq_eval_metrics = ['MC1', 'MC2']
+
+
+
+with tab1:
+   c, col1, cc, col2 = st.columns([.55, 2, .3, 2], gap="small")
+
+   with col1:
+      eval_tasks = st.radio( "Select An Evaluation Task:", 
+                    ('Text Summarization', 'Question Answering', 
+                     'Machine Translation', 'Multiple Choice QNs'), 
+                    horizontal=True)
+
+   with col2:
+      model_type = st.radio( "Select A Model Type:", 
+                  ('All', 'Quantized', 'Pretrained', 
+                   'Fine\u2013tuned', 'Instruction\u2014tuned'), 
+                  horizontal=True)
+
+
+   if eval_tasks=='Text Summarization':
+
+      select_eval_metrics = st.multiselect( 'Select Multiple Evaluation Metrics:', summ_eval_metrics, ['BLEU', 'CHARF', 'TER'])
+      
+      st.markdown("<br>", unsafe_allow_html=True)   
+
+      summ_eval_data = { 'Type' : ['Quantized', 'Pretrained', 'Fine\u2013tuned', 'Instruction\u2014tuned'],
+               'Model': ['username/model1', 'username/model2', 'username/model3', 'username/model4'],
+               'BLEU' : [70, 60, 50, 40],
+               'CHARF': [40, 50, 60, 70],
+               'TER'  : [50, 70, 40, 60]}
+
+      llm__dataframe = pd.DataFrame(summ_eval_data)
+
+      if model_type in ['Quantized', 'Pretrained', 'Fine\u2013tuned', 'Instruction\u2014tuned']:
+         llm__dataframe = llm__dataframe.loc[llm__dataframe['Type'] == model_type]
+
+      selected_columns = ['Model', 'Type'] + select_eval_metrics
+
+      llm__dataframe = llm__dataframe[selected_columns]
+
+      llm__dataframe['Model'] = llm__dataframe['Model'].apply(lambda x: f'https://huggingface.co/{x}')
+
+      st.checkbox("Use container width ▶️", value=True, key="use_container_width")
+      
+      st.data_editor(llm__dataframe, column_config={"Model": st.column_config.LinkColumn("Model")}, 
+                     hide_index=True, use_container_width=st.session_state.use_container_width, key="data_editor")
+   
+
+   elif eval_tasks=='Question Answering':
+
+      select_eval_metrics = st.multiselect('Select Multiple Evaluation Metrics:', qas_eval_metrics, ['F1', 'EXACT-MATCH'])
+
+      st.markdown("<br>", unsafe_allow_html=True)
+
+      qas_eval_data = { 'Type' : ['Quantized', 'Pretrained', 'Fine\u2013tuned', 'Instruction\u2014tuned'],
+               'Model': ['username/model1', 'username/model2', 'username/model3', 'username/model4'],
+               'F1' : [70, 60, 50, 40],
+               'EXACT-MATCH': [40, 50, 60, 70]}
+
+      llm__dataframe = pd.DataFrame(qas_eval_data)
+
+      if model_type in ['Quantized', 'Pretrained', 'Fine\u2013tuned', 'Instruction\u2014tuned']:
+         llm__dataframe = llm__dataframe.loc[llm__dataframe['Type'] == model_type]
+
+      selected_columns = ['Model', 'Type'] + select_eval_metrics
+
+      llm__dataframe = llm__dataframe[selected_columns]
+
+      llm__dataframe['Model'] = llm__dataframe['Model'].apply(lambda x: f'https://huggingface.co/{x}')
+
+      st.checkbox("Use container width ▶️", value=True, key="use_container_width")
+      
+      st.data_editor(llm__dataframe, column_config={"Model": st.column_config.LinkColumn("Model")}, 
+                     hide_index=True, use_container_width=st.session_state.use_container_width, key="data_editor1")
+
+
+   if eval_tasks=='Machine Translation':
+
+      select_eval_metrics = st.multiselect( 'Select Multiple Evaluation Metrics:', mts_eval_metrics, ['BLEU', 'CHARF', 'TER'])
+
+      st.markdown("<br>", unsafe_allow_html=True)
+
+      mts_eval_data = { 'Type' : ['Quantized', 'Pretrained', 'Fine\u2013tuned', 'Instruction\u2014tuned'],
+               'Model': ['username/model1', 'username/model2', 'username/model3', 'username/model4'],
+               'BLEU' : [70, 60, 50, 40],
+               'CHARF': [40, 50, 60, 70],
+               'TER'  : [50, 70, 40, 60]}
+
+      llm__dataframe = pd.DataFrame(mts_eval_data)
+
+      if model_type in ['Quantized', 'Pretrained', 'Fine\u2013tuned', 'Instruction\u2014tuned']:
+         llm__dataframe = llm__dataframe.loc[llm__dataframe['Type'] == model_type]
+
+      selected_columns = ['Model', 'Type'] + select_eval_metrics
+
+      llm__dataframe = llm__dataframe[selected_columns]
+
+      llm__dataframe['Model'] = llm__dataframe['Model'].apply(lambda x: f'https://huggingface.co/{x}')
+
+      st.checkbox("Use container width ▶️", value=True, key="use_container_width")
+      
+      st.data_editor(llm__dataframe, column_config={"Model": st.column_config.LinkColumn("Model")}, 
+                     hide_index=True, use_container_width=st.session_state.use_container_width, key="data_editor2")
+
+
+   if eval_tasks=='Multiple Choice QNs':
+
+      select_eval_metrics = st.multiselect('Select Multiple Evaluation Metrics:', mcq_eval_metrics, ['MC1', 'MC2'])
+      
+      st.markdown("<br>", unsafe_allow_html=True)
+      
+      mcq_eval_data = { 'Type' : ['Quantized', 'Pretrained', 'Fine\u2013tuned', 'Instruction\u2014tuned'],
+               'Model': ['username/model1', 'username/model2', 'username/model3', 'username/model4'],
+               'MC1' : [70, 60, 50, 40],
+               'MC2': [40, 50, 60, 70]}
+
+      llm__dataframe = pd.DataFrame(mcq_eval_data)
+
+      if model_type in ['Quantized', 'Pretrained', 'Fine\u2013tuned', 'Instruction\u2014tuned']:
+         llm__dataframe = llm__dataframe.loc[llm__dataframe['Type'] == model_type]
+
+      selected_columns = ['Model', 'Type'] + select_eval_metrics
+
+      llm__dataframe = llm__dataframe[selected_columns]
+
+      llm__dataframe['Model'] = llm__dataframe['Model'].apply(lambda x: f'https://huggingface.co/{x}')
+
+      st.checkbox("Use container width ▶️", value=True, key="use_container_width")
+      
+      st.data_editor(llm__dataframe, column_config={"Model": st.column_config.LinkColumn("Model")}, 
+                     hide_index=True, use_container_width=st.session_state.use_container_width, key="data_editor3")
+   
+
+
+with tab2:
+
+   submitted_models = pd.DataFrame(columns=['Model Name','Model HF Name', 'Model Type','Model Precision','Evaluation Tasks'])
+
+   c, col1 , col2, cc = st.columns([0.2, 1, 3, 0.2], gap="small")
+
+   with col1:
+         model_name = st.text_input("Enter Model Name (required):", placeholder="Enter model's short name", key="model_name")
+
+
+   with col2:
+         model_link = st.text_input("Enter Model HuggingFace Name:", placeholder="Enter model's HF Name: username/model", key="model_link")
+
+
+   c, col1 , col2, col3, cc = st.columns([0.2, 1, 1, 2, 0.2], gap="small")
+
+   with col1:
+         model_type = ['Quantized', 'Pretrained', 'Fine\u2013tuned', 'Instruction\u2014tuned']
+         selected_model_type = st.selectbox('Select Model Type:', (model_type))#, placeholder="Select a model type")
+
+   with col2:
+         model_precision = ['float32', 'float16', 'bfloat16', '8bit (LLM.int8)', '4bit (QLoRA/FP4)']
+         selected_model_precision = st.selectbox('Select Model Precision:', (model_precision))#, placeholder="Select a model precision")
+
+   with col3:
+         eval_tasks = ['All Tasks', 'Text Summarization', 'Question Answering', 'Machine Translation', 'Multiple Choice QNs']
+         selected_eval_tasks = st.selectbox('Select An Evaluation Task:', (eval_tasks))#, placeholder="Select an evaluation task")
+
+
+   st.markdown("##")
+   
+
+   c, col1 , col2, cc = st.columns([2, 1, 1, 2], gap="small")
+
+   with col1:
+      def clear_text():
+         st.session_state["model_name"] = ""
+         st.session_state["model_link"] = ""
+      
+      submit_button = st.button('Submit Model', key="submit")
+
+      if submit_button==True and model_name!='' and model_link!='':
+         response = get_model_name(model_name)
+         if response==None:
+            model_name_exist=False
+            input_data = {'key': model_name, 'Model Name': model_name, 'Model HF Name': model_link, 'Model Type': selected_model_type, 
+                  'Model Precision': selected_model_precision, 'Evaluation Tasks': selected_eval_tasks}
+            insert_model(input_data)
+            submitted_models = submitted_models.append(pd.DataFrame(input_data, index=[0]), ignore_index=True)
+            submitted_models = submitted_models[['Model Name','Model HF Name', 'Model Type','Model Precision','Evaluation Tasks']]
+         else: model_name_exist=True
+         
+      elif submit_button==True and model_name!='' and model_link=='':
+         response = get_model_name(model_name)
+         if response==None:
+            model_name_exist=False
+            input_data = {'key': model_name, 'Model Name': model_name, 'Model HF Name': None, 'Model Type': selected_model_type, 
+                  'Model Precision': selected_model_precision, 'Evaluation Tasks': selected_eval_tasks}
+            insert_model(input_data)
+            submitted_models = submitted_models.append(pd.DataFrame(input_data, index=[0]), ignore_index=True)
+            submitted_models = submitted_models[['Model Name','Model HF Name', 'Model Type','Model Precision','Evaluation Tasks']]
+         else: model_name_exist=True
+         
+      else: pass
+
+
+   with col2:  
+      st.button('Clear Form', on_click=clear_text)
+   
+   st.markdown("##")
+   
+   c, col1 , col2 = st.columns([0.15, 3, 0.15], gap="small")
+   
+   with col1:
+      if submit_button==True and model_name!='' and model_link!='' and model_name_exist==False:
+         st.success("You have submitted your model successfully", icon="")
+         st.data_editor(submitted_models, hide_index=True, use_container_width=st.session_state.use_container_width)
+
+      elif submit_button==True and model_name!='' and model_link=='' and model_name_exist==False:
+         st.warning("You have submitted your model, but the model's HuggingFace name is missing", icon="⚠️")
+         st.data_editor(submitted_models, hide_index=True, use_container_width=st.session_state.use_container_width)
+
+      elif submit_button==True and model_name=='' and model_link!='':
+         st.error("You have not submitted the required information", icon="")
+
+      elif submit_button==True and model_name=='' and model_link=='':
+         st.error("You have not submitted the required information", icon="")
+
+      elif submit_button==True and model_name!='' and model_link!='' and model_name_exist==True:
+         st.error("The model already submitted. Contact admin for help: { info@wishwork.org }", icon="")
+
+      elif submit_button==True and model_name!='' and model_link=='' and model_name_exist==True:
+         st.error("The model already submitted. Contact admin for help: { info@wishwork.org }", icon="")
+
+      else: pass
+
+   st.markdown("##")
+   
+   c, col1 , col2 = st.columns([0.15, 3, 0.15], gap="small")
+   
+   with col1:
+      with st.expander("Recently Submitted Models for Evaluation ⬇️"):
+         try:
+            all_submitted_models = pd.DataFrame(data=fetch_all_models())
+            all_submitted_models = all_submitted_models[['Model Name','Model HF Name', 'Model Type','Model Precision','Evaluation Tasks']]
+            st.data_editor(all_submitted_models, hide_index=True, use_container_width=st.session_state.use_container_width, key="data_editor4")
+         except KeyError:
+            st.info('There are no submitted models for evaluation at this moment 😆', icon="ℹ️")
+
+
+   
+footer="""<div class="footer"> <p class="p1">Copyright © 2023 <a text-align: center;' href="https://www.wishwork.org" target="_blank">Wish Work Inc.</a></p> </div>"""
+st.markdown(footer, unsafe_allow_html=True)