import streamlit as st
import random
import time
import os
from langchain_together import ChatTogether
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import TextLoader
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.vectorstores import FAISS
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_together import TogetherEmbeddings

os.environ["TOGETHER_API_KEY"] = os.getenv("API_TOKEN")
# os.environ["TOGETHER_API_KEY"] = "bafbab854ae828c3b90f675c45c8263e9404d278b5694909ea0855f437b9d1f3"

#load
loader = TextLoader("Resume_data.txt")
documents = loader.load()

# split it into chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
vectorstore = FAISS.from_documents(docs,
     TogetherEmbeddings(model="togethercomputer/m2-bert-80M-8k-retrieval")
)

retriever = vectorstore.as_retriever()
print("assigning model")
model = ChatTogether(
    # model = "meta-llama/Meta-Llama-3-8B-Instruct-Lite",
    # model = "deepseek-ai/DeepSeek-R1-Distill-Llama-70B-free",
    model = "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
    # model="meta-llama/Llama-3-70b-chat-hf",    
    temperature=0.0,
    max_tokens=500,)

prompt = ChatPromptTemplate([
    ("system", """You are an assistant for question-answering tasks. If you don't know the answer, just say that "i don't know". answer as if real person is responding. and if user greets then greet back"""),
    ("user", "context : {context}, Question: {question}")
])

chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | model
    | StrOutputParser()
)


st.title("Chat with me")

# Initialize chat history
if "messages" not in st.session_state:
    st.session_state.messages = []

# Display chat messages from history on app rerun
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

# Accept user input
if prompt := st.chat_input("What is up?"):
    # Display user message in chat message container
    with st.chat_message("user"):
        st.markdown(prompt)
    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})

############################################
# Streamed response emulator
def response_generator():
    query = f"{prompt}"
    if query != "None":
        for m in chain.stream(query):
            yield m
            time.sleep(0.01)
    else:
        yield "Hi, How can i help you?"
        
###########################################
# Display assistant response in chat message container
with st.chat_message("assistant"):
    response = st.write_stream(response_generator())
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": response})