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 chatbot developed by Suriya, an AI enthusiast. Your role is to engage in clear and informative conversations with users, providing helpful responses based on the information extracted from URLs, documents, and previous chat interactions.

Extracted URL text:
{extracted_text}

Provided document:
{provided_docs}

Previous Chat history:
{chat_history}

Human: {human_input}
Chatbot:"""

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

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


previous_response = ""
provided_docs = ""
def conversational_chat(query):
    global previous_response, provided_docs
    for i in st.session_state['history']:
        if i is not None:
            previous_response += f"Human: {i[0]}\n Chatbot: {i[1]}"
    docs = ""
    for j in st.session_state["docs"]:
        if j is not None:
            docs += j

    ex_text = st.session_state["extracted_text"]
    provided_docs = docs
    result = llm_chain.predict(chat_history=previous_response, human_input=query, provided_docs=provided_docs,extracted_text=ex_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")