import streamlit as st
import os
from langchain import PromptTemplate
from langchain.chat_models import AzureChatOpenAI
import tiktoken
from langchain.docstore.document import Document
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains.summarize import load_summarize_chain
from langchain.chains import AnalyzeDocumentChain


os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_VERSION"] = "2023-05-15"
os.environ["OPENAI_API_BASE"] = "https://cog-mnjbf5r4o6b3e.openai.azure.com/"
os.environ["OPENAI_API_KEY"] = "957f7d98b47a467a98a786f7ca903112"

def generate_response(txt):
    # Instantiate the LLM model
    llm = AzureChatOpenAI(temperature=0, deployment_name='gpt-4-32k', openai_api_version="2023-03-15-preview")
    # Split text
    text_splitter = CharacterTextSplitter()
    texts = text_splitter.split_text(txt)
    # Create multiple documents
    docs = [Document(page_content=t) for t in texts]
    # Text summarization
    prompt_template = """Write a structured report on the quality issues in the following text, are there any similarities across sites?:


    {text}


    Report:"""
    PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
    chain = load_summarize_chain(AzureChatOpenAI(deployment_name="chat", temperature=0), chain_type="map_reduce", return_intermediate_steps=True, map_prompt=PROMPT, combine_prompt=PROMPT, verbose=True)
    output = chain({"input_documents": docs}, return_only_outputs=True)['output_text']


    return output

# Page title
st.set_page_config(page_title='Health Data Summarization App')
st.title('quality issues')

# Text input
txt_input = st.text_area('Enter your quality data', '', height=200)

# Form to accept user's text input for summarization
result = []
with st.form('summarize_form', clear_on_submit=True):
    submitted = st.form_submit_button('Submit')
    if submitted:
        with st.spinner('Calculating...'):
            response = generate_response(txt_input)
            result.append(response)
        

if len(result):
    st.info(response)