import pandas as pd
from deta import Deta 
import streamlit as st  
 
import streamlit as st

with st.echo():
    st.write(f"streamlit version: {st.__version__}")


st.set_page_config(page_title="🤗 Persian LLM Leaderboard", page_icon=":bar_chart:", layout="wide")

with open('.streamlit/style.css') as f:
    st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)

st.markdown("<h1>🤗 Open Persian LLM Leaderboard</h1>", unsafe_allow_html=True)


DETA_KEY = st.secrets["DETA_KEY"]
deta = Deta(DETA_KEY)

database = deta.Base("submitted-models")

def insert_model(data): return database.put(data)
def fetch_all_models(): return database.fetch().items
def get_model_name(model): return database.get(model)


st.markdown("<br>", unsafe_allow_html=True)


tab1, tab2, tab3 = st.tabs([ "\u2001\u2001\u2001 LLM Benchmark\u2001\u2001\u2001",  
                            "\u2001\u2001\u2001 Submit A Model\u2001\u2001\u2001", 
                            "\u2001\u2001\u2001 About Leaderboard\u2001\u2001\u2001"])



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)