from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.prompts import PromptTemplate
from langchain_together import Together
import os
from langchain.memory import ConversationBufferWindowMemory
from langchain.chains import ConversationalRetrievalChain
import streamlit as st
import time
st.set_page_config(page_title="MedChat", page_icon="favicon.png")

col1, col2, col3 = st.columns([1,4,1])
with col2:
    st.image("https://github.com/harshitv804/MedChat/assets/100853494/0aa18d7e-5305-4d8e-89d8-09fffce1589e")

st.markdown(
    """
    <style>
div.stButton > button:first-child {
    background-color: #ffd0d0;
}
div.stButton > button:active {
    background-color: #ff6262;
}

   div[data-testid="stStatusWidget"] div button {
        display: none;
        }
    
    .reportview-container {
            margin-top: -2em;
        }
        #MainMenu {visibility: hidden;}
        .stDeployButton {display:none;}
        footer {visibility: hidden;}
        #stDecoration {display:none;}
    button[title="View fullscreen"]{
    visibility: hidden;}
        </style>
""",
    unsafe_allow_html=True,
)

def reset_conversation():
  st.session_state.messages = []
  st.session_state.memory.clear()

if "messages" not in st.session_state:
    st.session_state.messages = []

if "memory" not in st.session_state:
    st.session_state.memory = ConversationBufferWindowMemory(k=3, memory_key="chat_history",return_messages=True) 

embeddings = HuggingFaceEmbeddings(model_name="BAAI/llm-embedder")
db = FAISS.load_local("fdb_pg1_a", embeddings)
db_retriever = db.as_retriever(search_type="similarity",search_kwargs={"k": 3})

custom_prompt_template = """This is a chat tempalte and you are a medical practitioner lmm who provides correct medical information. Use the given following pieces of information to answer the user's question correctly. Utilize the provided knowledge base and search for relevant information. Follow the question format closely. The information should be abstract and concise. Understand all the context given here and generate only the answer. If you don't know the answer, just say that you don't know, don't try to make up an answer.

CONTEXT: {context}

CHAT HISTORY: {chat_history}

QUESTION: {question}

ANSWER
"""
prompt = PromptTemplate(template=custom_prompt_template,
                        input_variables=['context', 'question', 'chat_history'])

TOGETHER_AI_API= os.environ['TOGETHER_AI']
llm = Together(
    model="mistralai/Mistral-7B-Instruct-v0.2",
    temperature=0.7,
    max_tokens=1024,
    together_api_key=f"{TOGETHER_AI_API}"
)

qa = ConversationalRetrievalChain.from_llm(
    llm=llm,
    memory=st.session_state.memory,
    retriever=db_retriever,
    combine_docs_chain_kwargs={'prompt': prompt}
)

for message in st.session_state.messages:
    with st.chat_message(message.get("role")):
        st.write(message.get("content"))

input_prompt = st.chat_input("Say something")

if input_prompt:
    with st.chat_message("user"):
        st.write(input_prompt)

    st.session_state.messages.append({"role":"user","content":input_prompt})

    with st.chat_message("assistant"):
        with st.status("Thinking 💡...",expanded=True):
            result = qa.invoke(input=input_prompt)

            message_placeholder = st.empty()

            full_response = "⚠️ **_Note: Information provided may be inaccurate. Consult a qualified doctor for accurate advice._** \n\n\n"
        for chunk in result["answer"]:
            full_response+=chunk
            time.sleep(0.02)
            
            message_placeholder.markdown(full_response+" ▌")
        st.button('Reset All Chat 🗑️', on_click=reset_conversation)

    st.session_state.messages.append({"role":"assistant","content":result["answer"]})