import streamlit as st
st.set_page_config(layout="wide")
import uuid
from pathlib import Path
import os
import pandas as pd
import json
import docx
# import openai
# from langchain.chat_models import ChatOpenAI
# from langchain.prompts import PromptTemplate

st.title("Passage Name with Gen-AI")
os.environ["OPENAI_API_KEY"] = os.environ["OPEN_API_KEY"]
from openai import OpenAI
client = OpenAI()
list_dict = [] 
LIMIT_WORDS_PARA = 7



# Function to guess a single topic for a given text
def guess_topic(clause_text):
    response = client.chat.completions.create(
      model="gpt-4",
      messages=[
        {
          "role": "system",
          "content": "You are a legal contract assistant. Find type or title of the paragraph, e.g. Governing Law, Confidentiality etc."
        },
        {
          "role": "user",
          "content": "THIS AGREEMENT SHALL BE GOVERNED BY AND CONSTRUED IN ACCORDANCE WITH THE LAWS OF THE STATE OF NEW YORK."
        },
        {
          "role": "assistant",
          "content": "Governing Law"
        },
        {
          "role": "user",
          "content": "The provisions of this Agreement will be held in strictest confidence by you and the Company and will not be publicized or disclosed in any manner whatsoever; provided, however, that: (a) you may disclose this Agreement to your immediate family; (b) the parties may disclose this Agreement in confidence to their respective attorneys, accountants, auditors, tax preparers, and financial advisors; (c) the Company may disclose this Agreement as necessary to fulfill standard or legally required corporate reporting or disclosure requirements; and (d) the parties may disclose this Agreement insofar as such disclosure may be necessary to enforce its terms or as otherwise required by law."
        },
        {
          "role": "assistant",
          "content": "Confidentiality"
        },
        {
          "role": "user",
          "content": clause_text
        },
      ],
      temperature=0,
      max_tokens=10,
      top_p=1,
      frequency_penalty=0,
      presence_penalty=0
    )
    return(response)
    
with st.form("my_form"):
    multi = '''
    
    1. Upload many files (DOCX) Only
    
    2. Press Calculate and Get Name of Clauses
    
    '''
    st.markdown(multi)
    uploaded_files = st.file_uploader('Upload your files',accept_multiple_files=True)
    for f in uploaded_files:
        if True:
            save_path = Path(os.getcwd(), f.name)
            with open(save_path, mode='wb') as w:
                w.write(f.getvalue())
            doc = docx.Document(save_path)
            paragraphs = doc.paragraphs
            for para in paragraphs:
                text = para.text
                words = text.split()
                if len(words) > LIMIT_WORDS_PARA:
                    list_dict.append({"file":f.name, "example":text})  
    ###### 
    print('len(list_dict)',len(list_dict))
    df_new_trimmed = pd.DataFrame(list_dict)
    ######
    submitted = st.form_submit_button("Calculate")
    if submitted and (uploaded_files is not None):
        topics_list = []
        for doc in list(df_new_trimmed['example']):
            tmp_keywords_custom = guess_topic(doc).choices[0].message.content
            topics_list.append(tmp_keywords_custom)
        df_new_trimmed['Predicted_Clause'] = topics_list

        print(topics_list[0])

        st.dataframe(df_new_trimmed)
        st.write('-----')