app.py

import os
import openai

os.environ["TOKENIZERS_PARALLELISM"] = "false"
os.environ["OPENAI_API_KEY"]


def save_docs(docs):

    import shutil
    import os

    output_dir = "/home/user/app/docs/"

    if os.path.exists(output_dir):
        shutil.rmtree(output_dir)

    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    for doc in docs:
        shutil.copy(doc.name, output_dir)

    return "Successful!"


def process_docs():

    from langchain.document_loaders import PyPDFLoader
    from langchain.document_loaders import DirectoryLoader
    from langchain.document_loaders import TextLoader
    from langchain.document_loaders import Docx2txtLoader
    from langchain.document_loaders.csv_loader import CSVLoader
    from langchain.document_loaders import UnstructuredExcelLoader
    from langchain.vectorstores import FAISS
    from langchain_openai import OpenAIEmbeddings
    from langchain.text_splitter import RecursiveCharacterTextSplitter

    loader1 = DirectoryLoader(
        "/home/user/app/docs/", glob="./*.pdf", loader_cls=PyPDFLoader
    )
    document1 = loader1.load()

    loader2 = DirectoryLoader(
        "/home/user/app/docs/", glob="./*.txt", loader_cls=TextLoader
    )
    document2 = loader2.load()

    loader3 = DirectoryLoader(
        "/home/user/app/docs/", glob="./*.docx", loader_cls=Docx2txtLoader
    )
    document3 = loader3.load()

    loader4 = DirectoryLoader(
        "/home/user/app/docs/", glob="./*.csv", loader_cls=CSVLoader
    )
    document4 = loader4.load()

    loader5 = DirectoryLoader(
        "/home/user/app/docs/", glob="./*.xlsx", loader_cls=UnstructuredExcelLoader
    )
    document5 = loader5.load()

    document1.extend(document2)
    document1.extend(document3)
    document1.extend(document4)
    document1.extend(document5)

    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000, chunk_overlap=200, length_function=len
    )

    docs = text_splitter.split_documents(document1)
    embeddings = OpenAIEmbeddings()

    docs_db = FAISS.from_documents(docs, embeddings)
    docs_db.save_local("/home/user/app/docs_db/")

    return "Successful!"


global agent


def create_agent():

    from langchain_openai import ChatOpenAI
    from langchain.chains.conversation.memory import ConversationSummaryBufferMemory
    from langchain.chains import ConversationChain

    global agent

    llm = ChatOpenAI(model_name="gpt-3.5-turbo-16k")
    memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=1000)
    agent = ConversationChain(llm=llm, memory=memory, verbose=True)

    return "Successful!"


def formatted_response(docs, question, response, state):

    formatted_output = response + "\n\nSources"

    for i, doc in enumerate(docs):
        source_info = doc.metadata.get("source", "Unknown source")
        page_info = doc.metadata.get("page", None)

        doc_name = source_info.split("/")[-1].strip()

        if page_info is not None:
            formatted_output += f"\n{doc_name}\tpage no {page_info}"
        else:
            formatted_output += f"\n{doc_name}"

    state.append((question, formatted_output))
    return state, state


def search_docs(prompt, question, state):

    from langchain_openai import OpenAIEmbeddings
    from langchain.vectorstores import FAISS
    from langchain.callbacks import get_openai_callback

    global agent
    agent = agent

    state = state or []

    embeddings = OpenAIEmbeddings()
    docs_db = FAISS.load_local(
        "/home/user/app/docs_db/", embeddings, allow_dangerous_deserialization=True
    )
    docs = docs_db.similarity_search(question)

    prompt += "\n\n"
    prompt += question
    prompt += "\n\n"
    prompt += str(docs)

    with get_openai_callback() as cb:
        response = agent.predict(input=prompt)
        print(cb)

    return formatted_response(docs, question, response, state)


import gradio as gr

css = """
.col{
    max-width: 75%;
    margin: 0 auto;
    display: flex;
    flex-direction: column;
    justify-content: center;
    align-items: center;
}
"""

with gr.Blocks(css=css) as demo:
    gr.Markdown("## <center>Your AI Medical Assistant</center>")

    with gr.Tab("Your AI Medical Assistant"):
        with gr.Column(elem_classes="col"):

            with gr.Tab("Query Documents"):
                with gr.Column():
                    create_agent_button = gr.Button("Create Agent")
                    create_agent_output = gr.Textbox(label="Output")

                    docs_prompt_input = gr.Textbox(label="Custom Prompt")

                    docs_chatbot = gr.Chatbot(label="Chats")
                    docs_state = gr.State()

                    docs_search_input = gr.Textbox(label="Question")
                    docs_search_button = gr.Button("Search")

                    gr.ClearButton(
                        [docs_prompt_input, docs_search_input, create_agent_output]
                    )

    #########################################################################################################

    create_agent_button.click(create_agent, inputs=None, outputs=create_agent_output)

    docs_search_button.click(
        search_docs,
        inputs=[docs_prompt_input, docs_search_input, docs_state],
        outputs=[docs_chatbot, docs_state],
    )

    #########################################################################################################

demo.queue()
demo.launch()