import gradio as gr

from langchain.document_loaders import OnlinePDFLoader

from langchain.text_splitter import CharacterTextSplitter
text_splitter = CharacterTextSplitter(chunk_size=350, chunk_overlap=0)

from langchain.llms import HuggingFaceHub
flan_ul2 = HuggingFaceHub(repo_id="google/flan-ul2", model_kwargs={"temperature":0.1, "max_new_tokens":300})

from langchain.embeddings import HuggingFaceHubEmbeddings
embeddings = HuggingFaceHubEmbeddings()

from langchain.vectorstores import Chroma

from langchain.chains import RetrievalQA

def pdf_changes(pdf_doc):
    loader = OnlinePDFLoader(pdf_doc.name)
    documents = loader.load()
    texts = text_splitter.split_documents(documents)
    db = Chroma.from_documents(texts, embeddings)
    retriever = db.as_retriever()
    global qa 
    qa = RetrievalQA.from_chain_type(llm=flan_ul2, chain_type="stuff", retriever=retriever, return_source_documents=True)
    return "Ready"

def infer(question):
    
    query = question
    result = qa({"query": query})

    return result

with gr.Blocks() as demo:
    with gr.Column():
        pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="file")
        langchain_status = gr.Textbox()
        load_pdf = gr.Button("Load pdf to langchain")
        question = gr.Textbox(label="Your Question")
        answer = gr.Textbox(label="Anwser")
        
        submit_button = gr.Button("Send Question")
    
    load_pdf.click(pdf_changes, pdf_doc, langchain_status, queue=False)
    submit_button.click(infer, inputs=[question], outputs=[answer])
        

demo.launch()