app.py

import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
import streamlit as st
#from streamlit_chat import message

from streamlit.runtime.scriptrunner.script_run_context import get_script_run_ctx
from datetime import datetime
import pandas as pd
# from services.qa_chat_mode import *
# from services.qna import *
# from services.utils import *
# from services.transcripts import *
# from langchain import HuggingFaceHub
# from services.smart_prompt import PDSCoverageChain


def build_experimental_ui():
 
    with st.sidebar:
        tabs = st.sidebar.selectbox('SELECT TASK', [
            "Question & Answer",
            "Question & Answer (Chat Mode)",
            "Transcript Intelligence",
        ])
        st.markdown('---')
        # Filters
        if tabs=='Question & Answer':
            selected_model = st.selectbox("Select Model:", options=[], index=0)
            selected_embeddings = st.selectbox("Select Embeddings:", options=[], index=0)
    #        strategy = st.selectbox("Select Strategy:", options=STRATEGY_OPTIONS, index=1, disabled=True)
            strategy = ''
        elif tabs=='Question & Answer (Chat Mode)':
            selected_model = st.selectbox("Select Model:", options=[], index=0)
            selected_embeddings = st.selectbox("Select Embeddings:", options=[], index=0)
        elif tabs == 'Transcript Intelligence':
            selected_model = st.selectbox("Select Model:", options=[], index=0)
    #        selected_embeddings = st.selectbox("Select Embeddings:", options=EMBEDDING_OPTIONS, index=0)
    #        claimnumber = st.selectbox("Select Claim:", options=CLAIM_OPTIONS, index=0, disabled=True)

    st.error("Disclaimer: All data processed in this application will be sent to OpenAI API based in the United States.")

    st.markdown('## '+tabs)

    if tabs=='Question & Answer':
        
        # pdf_docs = st.file_uploader('Upload a PDF file', type=['pdf'],accept_multiple_files=True)
        # st.session_state['pdf_file'] = pdf_docs
        # Process = st.button("Process", disabled=(pdf_docs==[]))
        
        # if Process: 
        #     if pdf_docs:
        #         if pdf_docs!=st.session_state['pdf_file']:
        #             st.session_state['pdf_file'] = pdf_docs
        #         with st.spinner('Creating embeddings...'):
        #             texts = load_pdf_document(pdf_docs=pdf_docs)
        #             retriever = get_retriever_from_text(texts, embeddings[selected_embeddings])
        #             st.session_state['retriever'] = retriever
                        
                
        st.markdown('---')
        
        # # Question & Answer
        # if st.session_state['retriever'] is None:
        #     disable_query = True
        # else:
        #     disable_query = False
            
        prompt = st.text_input('Input your prompt', disabled=False, key="text")

        questions_file =  st.file_uploader('Upload a CSV file with questions', type=['csv'],accept_multiple_files=False)

        if questions_file:
            questions_df = pd.read_csv(questions_file))

        

        return 
        
        chat_content = {}
        chat_content['question'] = st.session_state.query
        
        if strategy=='Without Chain-of-Thought':
            instruction = st.text_area('Input your instruction (optional)', value=st.session_state['qa_instruction'], disabled=disable_query)
            with st.expander("Sample instruction"):
                sample_instruction = "Answer the question based on the context provided. Explain with reason in bullet points. Let's think step by step."
                button_sample_instruction = st.button(sample_instruction, key='instruction1', disabled=disable_query, on_click=set_qa_instruction, args=(sample_instruction,))
            
        if st.session_state.query is None:
            disable = True
        else:
            disable = False
            
        button_query = st.button('Submit', disabled=disable)
        
        
        if button_query:

            print('---- run query ----')
            print(f'model: {selected_model}  embeddings: {selected_embeddings}')
            if selected_embeddings!=st.session_state['selected_embeddings']:
                st.session_state['selected_embeddings'] = selected_embeddings
                texts = load_pdf_document(pdf_docs)
                st.session_state['retriever'] = get_retriever_from_text(texts, embeddings[selected_embeddings])
    #        qa = RetrievalQA.from_chain_type(llm=models[selected_model], chain_type="stuff", 
    #                                         retriever=st.session_state['retriever'], return_source_documents=True)
            st.session_state['docs'] = st.session_state['retriever'].get_relevant_documents(st.session_state.query)
            context = '\n\n'.join([doc.page_content for doc in st.session_state['docs']])
            st.session_state['context'] = context
            source_files = get_pdf_file_names(st.session_state['pdf_file'])
            #st.session_state['conversation']= get_conversation_chain(st.session_state['retriever'])

            if strategy=='Without Chain-of-Thought':
                user_token = model_configs[selected_model]['USER_TOKEN']
                end_token = model_configs[selected_model]['END_TOKEN']
                assistant_token = model_configs[selected_model]['ASSISTANT_TOKEN']
                prompt_pattern, prompt = create_prompt(user_token, instruction, st.session_state.query, end_token, assistant_token, context)
                updated_context = truncate_context(prompt_pattern, context, 
                                                   max_token_len=model_configs[selected_model]['MAX_TOKENS'],
                                                   max_new_token_length=model_configs[selected_model]['MAX_NEW_TOKEN_LENGTH'])
                updated_prompt = prompt_pattern.replace('{context}', updated_context)
                print(updated_prompt)

                
                with st.spinner():
                    answer = models[selected_model].generate([updated_prompt]).generations[0][0].text.strip()
                st.write(answer)
                chat_content['answer'] = answer
                chat_content['source'] = source_files
                chat_content['context']=st.session_state['context']
                chat_content['time']=datetime.now().strftime("%d-%m-%Y %H:%M:%S")
                if st.session_state['chat_history']: 
                    st.session_state['chat_history'].append(chat_content)
                else:
                    st.session_state['chat_history']=[chat_content]
                print('------chat history-----',st.session_state['chat_history'])
                if updated_prompt!=prompt:
                    st.caption(f"Note: The context has been truncated to fit model max tokens of {model_configs[selected_model]['MAX_TOKENS']}. Original context contains {len(context.split())} words. Truncated context contains {len(updated_context.split())} words.")

            else:
                chain = PDSCoverageChain()
                with st.spinner():
                    answer = chain.generate(models[selected_model], model_configs[selected_model], st.session_state.query, context)
                st.write(answer)
                chat_content['answer'] = answer
                chat_content['source'] = source_files
                chat_content['context']=st.session_state['context']
                chat_content['time']=datetime.now().strftime("%d-%m-%Y %H:%M:%S")
                if st.session_state['chat_history']: 
                    st.session_state['chat_history'].append(chat_content)
                else:
                    st.session_state['chat_history']=[chat_content]
                print('------chat history-----',st.session_state['chat_history'])
               
        
        if st.session_state['docs']:

            docs = st.session_state['docs']
            
            col3, col4, col5, col6 = st.columns([0.2,0.35, 0.65, 3.8])
            if st.session_state.query is None:
                disable_query = True
            else:
                disable_query = False
            chat_history = st.session_state['chat_history']
            with col3:
                st.button(":thumbsup:",  on_click = get_feedback,disabled=disable_query,
                            kwargs=dict(upvote=True, downvote=False,
                                        button='upvote'))
            with col4:   
                st.button(":thumbsdown:",  on_click = get_feedback,disabled=disable_query,
                            kwargs=dict(upvote=False, downvote=True,
                                        button='downvote'))
        
        
            with st.expander("References"):
                for doc in docs:
                    print('-------',doc)
                    #st.markdown('###### Page {}'.format(doc.metadata['page']))
                    st.write(doc.page_content.replace('\n','\n\n').replace('$','\$').replace('**',''))
        
                
            
            st.button("End Chat",  on_click = get_feedback,
                    kwargs=dict(button='end-chat',
                                chat_history=chat_history))
            


    
    else:
        st.info("Under Development")
        
build_experimental_ui()