import streamlit as st
import pickle
#from Summarizer_Helper import Summary_Gen

from gpt4all import GPT4All
import textwrap

with open('examples.pkl', 'rb') as f:
    example_list = pickle.load(f)

# Model initialization (assuming the model is already downloaded)
from huggingface_hub import hf_hub_download
model_path = "models"
model_name = "Llama-2-7b-MOM_Summar.Q2_K.gguf"
# hf_hub_download(repo_id="sasvata/Llama2-7b-MOM-Summary-Finetuned-GGUF", filename=model_name, local_dir=model_path, local_dir_use_symlinks=False)

print("Start the model init process")
model = model = GPT4All(model_name, model_path, allow_download = False, device="cpu")
print("Finish the model init process")

## Function to convert plain text to markdown format
def to_markdown(text):
  text = text.replace('•', '  *')
  return textwrap.indent(text, '> ', predicate=lambda _: True)

# Default system prompt for generating conversation summaries
DEFAULT_SYSTEM_PROMPT = """
Below is a conversation between a human and an AI agent. Write a summary of the conversation.
""".strip()

def generate_prompt(
    Transcript: str, system_prompt: str = DEFAULT_SYSTEM_PROMPT
) -> str:
    return f"""### Instruction: {system_prompt}

### Input:
{Transcript.strip()}

### Response:
""".strip()

# Function to generate summary with the help of fine-tuned model
def Summary_Gen(Transcript):
    prompt = generate_prompt(Transcript)
    summary = model.generate(prompt=prompt,max_tokens=4096)
    # summary_output = to_markdown(summary)
    return summary_output

# Function for text summarization
def summarize_text(text):
    # Your text summarization logic here (replaced with Summary_Gen)
    summarized_text = Summary_Gen(text)
    return summarized_text


st.set_page_config(layout="wide", page_title="MOM-Summary-Generator📑", page_icon="📑")
st.title("Minutes Of Meeting (MOM) - Summary Generator 📑")

# Text input and output elements
option = st.selectbox(
   "Choose Example",
   example_list,
   index=None,
   placeholder="Choose Example",
)


summary_output = ""
col1, col2 = st.columns(2)

col1.title('Input')   
col1.container(height=500, border=True).text_area("", option, height=1000)
if col1.button("Summarize"):
    with st.spinner('Wait for it...'):
        summary_output = summarize_text(option)
    

col2.title('Output')   
col2.container(height=500, border=True).markdown(summary_output, unsafe_allow_html=True)