import streamlit as st
import os
from streamlit_chat import message
from PyPDF2 import PdfReader
import bs4
import google.generativeai as genai
from langchain.prompts import PromptTemplate
from langchain import LLMChain
from langchain_google_genai import ChatGoogleGenerativeAI
import nest_asyncio
from langchain.document_loaders import WebBaseLoader

nest_asyncio.apply()

os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY")
genai.configure(api_key=os.environ["GOOGLE_API_KEY"])

llm = ChatGoogleGenerativeAI(model="gemini-pro",
                             temperature=0.4)



template = """
You are CRETA, a friendly and knowledgeable chatbot created by Suriya, an AI enthusiast. You can access and understand the content from provided documents and websites to help answer questions.

Previous Conversation:
{chat_history}

Provided Document Content:
{provided_docs}

Extracted URL Text:
{extracted_text}

Human: {human_input}
Chatbot: """

prompt = PromptTemplate(
    input_variables=["chat_history", "human_input", "provided_docs", "extracted_text"], 
    template=template
)

llm_chain = LLMChain(
    llm=llm,
    prompt=prompt,
    verbose=True,
)


previous_response = ""
provided_docs = ""
def conversational_chat(query):
    global previous_response, provided_docs, extracted_text
    previous_response = "".join([f"Human: {i[0]}\nChatbot: {i[1]}" for i in st.session_state['history'] if i is not None])
    provided_docs = "".join([doc for doc in st.session_state["docs"] if doc is not None])
    extracted_text = "".join([text for text in st.session_state["extracted_text"] if text is not None])
    
    result = llm_chain.predict(
        chat_history=previous_response, 
        human_input=query, 
        provided_docs=provided_docs, 
        extracted_text=extracted_text
    )
    st.session_state['history'].append((query, result))
    return result

st.title("Chat Bot:")
st.text("I am CRETA Your Friendly Assitant")
st.markdown("Built by [Suriya❤️](https://github.com/theSuriya)")

if 'history' not in st.session_state:
    st.session_state['history'] = []
    
# Initialize messages
if 'generated' not in st.session_state:
    st.session_state['generated'] = ["Hello ! Ask me anything"]

if 'past' not in st.session_state:
    st.session_state['past'] = [" "]
    
if 'docs' not in st.session_state:
    st.session_state['docs'] = []

if "extracted_text" not in st.session_state:
    st.session_state["extracted_text"] = []
    
def get_pdf_text(pdf_docs):
    text = ""
    for pdf in pdf_docs:
        pdf_reader = PdfReader(pdf)
        for page in pdf_reader.pages:
            text += page.extract_text()
    return text

def get_url_text(url_link):
    website_url = url_link
    loader = WebBaseLoader(website_url)
    loader.requests_per_second = 1
    docs = loader.aload()
    extracted_text = ""
    for page in docs:
      extracted_text+=page.page_content

    return extracted_text
    

with st.sidebar:
    st.title("Add a file for CRETA memory:")
    uploaded_files = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
    uploaded_url = st.text_area("Please upload a URL:")

    if st.button("Submit & Process"):
        if uploaded_files or uploaded_url:
            with st.spinner("Processing..."):
                if uploaded_files:
                    pdf_text = get_pdf_text(uploaded_files)
                    st.session_state["docs"] += get_pdf_text(uploaded_files)
                
                if uploaded_url:
                    url_text = get_url_text(uploaded_url)
                    st.session_state["extracted_text"] += get_url_text(uploaded_url)
                
                st.success("Processing complete!")
        else:
            st.error("Please upload at least one PDF file or provide a URL.")
    
# Create containers for chat history and user input
response_container = st.container()
container = st.container()

# User input form
user_input = st.chat_input("Ask Your Questions 👉..")
with container:
    if user_input:
        output = conversational_chat(user_input)
        # answer = response_generator(output)
        st.session_state['past'].append(user_input)
        st.session_state['generated'].append(output)
        
        
# Display chat history
if st.session_state['generated']:
    with response_container:
        for i in range(len(st.session_state['generated'])):
            if i != 0:
                message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="adventurer")
            message(st.session_state["generated"][i], key=str(i), avatar_style="bottts")