app.py

import gradio as gr
import os
import time


from langchain.document_loaders import OnlinePDFLoader #for laoding the pdf
from langchain.embeddings import OpenAIEmbeddings # for creating embeddings
from langchain.vectorstores import Chroma # for the vectorization part
from langchain.chains import RetrievalQA # for conversing with chatGPT
from langchain.chat_models import ChatOpenAI # the LLM model we'll use (ChatGPT)
from langchain import PromptTemplate




def load_doc(pdf_doc, open_ai_key):
    if openai_key is not None:
        os.environ['OPENAI_API_KEY'] = open_ai_key
        #Load the pdf file
        loader = OnlinePDFLoader(pdf_doc.name)
        pages = loader.load_and_split()
        
        #Create an instance of OpenAIEmbeddings, which is responsible for generating embeddings for text
        embeddings = OpenAIEmbeddings()

        #To create a vector store, we use the Chroma class, which takes the documents (pages in our case), the embeddings instance, and a directory to store the vector data
        vectordb = Chroma.from_documents(pages, embedding=embeddings)
        
        #Finally, we create the bot using the RetrievalQAChain class
        global pdf_qa

        prompt_template = """Use the following pieces of context to answer the question at the end. If you do not know the answer, just return the question followed by N/A. If you encounter a date, return it in mm/dd/yyyy format.

        {context}

        Question: {question}
        Return the key fields from the question followed by the answer :"""
        PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
        chain_type_kwargs = {"prompt": PROMPT}
        pdf_qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(temperature=0, model_name="gpt-4"),chain_type="stuff", retriever=vectordb.as_retriever(), chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
        
        
        return "Ready"
    else:
        return "Please provide an OpenAI API key"


def answer_query(query):
    question = query
    return pdf_qa.run(question)

html = """
<div style="text-align:center; max width: 700px;">
    <h1>ChatPDF</h1>
    <p> Upload a PDF File, then click on Load PDF File <br>
    Once the document has been loaded you can begin chatting with the PDF =)
</div>"""
css = """container{max-width:700px; margin-left:auto; margin-right:auto,padding:20px}"""
with gr.Blocks(css=css,theme=gr.themes.Monochrome()) as demo:
    gr.HTML(html)
    with gr.Column():
        openai_key = gr.Textbox(label="Your GPT-4 OpenAI API key", type="password")
        pdf_doc = gr.File(label="Load a pdf",file_types=['.pdf'],type='file')
        
        with gr.Row():
            status = gr.Textbox(label="Status", placeholder="", interactive=False)
            load_pdf = gr.Button("Load PDF to LangChain")
        with gr.Row():
            input = gr.Textbox(label="Type in your question")
            output = gr.Textbox(label="Answer")
        submit_query = gr.Button("Submit")
        
 
    load_pdf.click(load_doc, inputs=[pdf_doc, openai_key], outputs=status)
        
    submit_query.click(answer_query,input,output)


#forcing a save in order to re-build the container.
demo.launch()