import streamlit as st
from functions import *
from langchain.chains import QAGenerationChain
import itertools


st.set_page_config(page_title="Earnings Question/Answering", page_icon="🔎")

st.sidebar.header("Semantic Search")

st.markdown("Earnings Semantic Search with LangChain, OpenAI & SBert")

starter_message = "Ask me anything about the Earnings Call!"

st.markdown(
    """
    <style>
    
    #MainMenu {visibility: hidden;
    # }
        footer {visibility: hidden;
        }
        .css-card {
            border-radius: 0px;
            padding: 30px 10px 10px 10px;
            background-color: black;
            box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
            margin-bottom: 10px;
            font-family: "IBM Plex Sans", sans-serif;
        }
        
        .card-tag {
            border-radius: 0px;
            padding: 1px 5px 1px 5px;
            margin-bottom: 10px;
            position: absolute;
            left: 0px;
            top: 0px;
            font-size: 0.6rem;
            font-family: "IBM Plex Sans", sans-serif;
            color: white;
            background-color: green;
            }
            
        .css-zt5igj {left:0;
        }
        
        span.css-10trblm {margin-left:0;
        }
        
        div.css-1kyxreq {margin-top: -40px;
        }
        
       
   
        
      
    </style>
    """,
    unsafe_allow_html=True,
)

bi_enc_dict = {'mpnet-base-v2':"all-mpnet-base-v2",
              'instructor-base': 'hkunlp/instructor-base',
              'FlagEmbedding': 'BAAI/bge-base-en'}

search_input = st.text_input(
        label='Enter Your Search Query',value= "What key challenges did the business face?", key='search')
        
sbert_model_name = st.sidebar.selectbox("Embedding Model", options=list(bi_enc_dict.keys()), key='sbox')

st.sidebar.markdown('Earnings QnA Generator')
        
chunk_size = 1000
overlap_size = 50

try:

        
    if "sen_df" in st.session_state and "earnings_passages" in st.session_state:
    
        ## Save to a dataframe for ease of visualization
        sen_df = st.session_state['sen_df']

        title = st.session_state['title']

        earnings_text = st.session_state['earnings_passages']

        print(f'earnings_to_be_embedded:{earnings_text}')

        st.session_state.eval_set = generate_eval(
        earnings_text, 10, 3000)

        # Display the question-answer pairs in the sidebar with smaller text
        for i, qa_pair in enumerate(st.session_state.eval_set):
            st.sidebar.markdown(
                f"""
                <div class="css-card">
                <span class="card-tag">Question {i + 1}</span>
                    <p style="font-size: 12px;">{qa_pair['question']}</p>
                    <p style="font-size: 12px;">{qa_pair['answer']}</p>
                </div>
                """,
                unsafe_allow_html=True,
            )

        embedding_model = bi_enc_dict[sbert_model_name]
                        
        with st.spinner(
            text=f"Loading {embedding_model} embedding model and Generating Response..."
        ):
            
            docsearch = create_vectorstore(earnings_text,title, embedding_model)

            memory, agent_executor = create_memory_and_agent(search_input,docsearch)

        if "messages" not in st.session_state or st.sidebar.button("Clear message history"):
            st.session_state["messages"] = [AIMessage(content=starter_message)]

        for msg in st.session_state.messages:
            if isinstance(msg, AIMessage):
                st.chat_message("assistant").write(msg.content)
            elif isinstance(msg, HumanMessage):
                st.chat_message("user").write(msg.content)
            memory.chat_memory.add_message(msg)

        if user_question := st.chat_input(placeholder=starter_message):
            st.chat_message("user").write(user_question)
    
            with st.chat_message("assistant"):
                
                st_callback = StreamlitCallbackHandler(st.container())
                
                response = agent_executor(
                    {"input": user_question, "history": st.session_state.messages},
                    callbacks=[st_callback],
                    include_run_info=True,
                )

                answer = response["output"]
                
                st.session_state.messages.append(AIMessage(content=answer))
                
                st.write(answer)
                
                memory.save_context({"input": user_question}, response)
                
                st.session_state["messages"] = memory.buffer
                
                run_id = response["__run"].run_id
        
                col_blank, col_text, col1, col2 = st.columns([10, 2, 1, 1])

            
            with st.expander(label='Query Result with Sentiment Tag', expanded=True):
                
                sentiment_label = gen_sentiment(answer)
                df = pd.DataFrame.from_dict({'Text':[answer],'Sentiment':[sentiment_label]})
                text_annotations = gen_annotated_text(df)[0] 
                annotated_text(text_annotations)
                
            
    else:
    
        st.write('Please ensure you have entered the YouTube URL or uploaded the Earnings Call file')  
        
except RuntimeError:
  
    st.write('Please ensure you have entered the YouTube URL or uploaded the Earnings Call file')