"""## WEBSITE CHAT BOT"""

# Commented out IPython magic to ensure Python compatibility.
# %%capture
# !pip  install langchain
# !pip install bitsandbytes accelerate transformers
# !pip install sentence_transformers
# !pip install unstructured
# !pip install faiss-cpu

!huggingface-cli login

# Commented out IPython magic to ensure Python compatibility.
# %%capture
# !pip install numpy==1.24.4

# Commented out IPython magic to ensure Python compatibility.
# %%capture
# pip install -U langchain-community

from langchain.document_loaders import UnstructuredURLLoader

URLs = [
    "https://fullstackacademy.in/"
]

loaders=UnstructuredURLLoader(urls=URLs)

data=loaders.load()

# creating the chunks
from langchain.text_splitter import RecursiveCharacterTextSplitter

text_chunks=RecursiveCharacterTextSplitter(chunk_size=1000,chunk_overlap=200)

chunks=text_chunks.split_documents(data)

len(chunks)

# calling huggingfaceembeddings class
from langchain.embeddings import HuggingFaceEmbeddings

embeddings=HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')

# calling faiss vector database from langchain
from langchain.vectorstores import FAISS

vectordatabase=FAISS.from_documents(chunks,embeddings)

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM,pipeline
from langchain import HuggingFacePipeline

model="google/flan-t5-large"

tokenizer = AutoTokenizer.from_pretrained(model)
model1 = AutoModelForSeq2SeqLM.from_pretrained(model)
pipe = pipeline("text2text-generation", model=model1, tokenizer=tokenizer)
llm = HuggingFacePipeline(
    pipeline = pipe,
    model_kwargs={"temperature": 0, "max_length": 500},
)

from langchain.prompts import PromptTemplate

template = """use the context to provide a concise answer and if you don't know just say don't now.
{context}
Question: {question}
Helpful Answer:"""
QA_CHAIN_PROMPT = PromptTemplate.from_template(template)

from langchain.chains import RetrievalQA

qa_chain = RetrievalQA.from_chain_type(
    llm, retriever=vectordatabase.as_retriever(), chain_type="stuff",chain_type_kwargs={"prompt": QA_CHAIN_PROMPT}
)

question = "Who is the co-founder?"
result = qa_chain({"query": question})
result["result"]

question = "What is the data science course duration?"
result = qa_chain({"query": question})
result["result"]

# Commented out IPython magic to ensure Python compatibility.
# %%capture
# pip install gradio

import gradio as gr

def fetch(question, history):
  result = qa_chain({"query": question})
  return result["result"]

gr.ChatInterface(
    fetch,
    chatbot=gr.Chatbot(height=300),
    textbox=gr.Textbox(placeholder="Ask me a yes or no question", container=False, scale=7),
    title="Yes Man",
    description="Ask Yes Man any question",
    theme="soft",
    examples=["contact details", "data science bootcamp fee?", "placements info","MERN fee"],
    cache_examples=True,
    retry_btn=None,
    undo_btn="Delete Previous",
    clear_btn="Clear",
).launch(share=True)