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Knowledge_Graph_with_LLM

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Knowledge_Graph_with_LLM
File size: 4,812 Bytes 
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import re

import gradio as gr
import requests


BACKEND_ADDR = "http://46.8.230.12:8585/post"

# Initialize an empty list to store user queries
queries_list = []

# Convert model name to the corresponding model id that backend understands
llm_name2id = {
    "Llama-3.1-70B-Versatile": "llama-3.1-70b-versatile",
    "Llama-3-70B-8192": "llama3-70b-8192",
    "Llama-3-8B-8192": "llama3-8b-8192"
}

# Some default values
DEFAULT_TEMP = 0.2
DEFAULT_MODEL = "Llama-3-70B-8192"
DEFAULT_USE_RAG = True


# Function to handle the 'Clear' button
def clear_queries():
    global queries_list

    queries_list = []
    return "", DEFAULT_MODEL, DEFAULT_TEMP, DEFAULT_USE_RAG, "", ""


# Function to handle the 'Add' button
def add_to_list(query_txt, model, temperature, use_rag):
    global queries_list

    if len(query_txt) > 0:
        queries_list.append(
            {
                "prompt": query_txt,
                "temperature": str(temperature),
                "model": llm_name2id[model],
                "use_rag": str(use_rag),
            }
        )

    return "", generate_queries_str(queries_list)


# Function to handle the 'Submit' button
def submit(query_txt, model, temperature, use_rag):
    global queries_list

    if len(query_txt) > 0:
        _, queries = add_to_list(query_txt, model, temperature, use_rag)
    else:
        queries = generate_queries_str(queries_list)

    if len(queries_list) > 0:
        response = requests.post(BACKEND_ADDR, json=queries_list)
        answers = generate_answers_str(response.json())

        # Re-initialize the user's query list
        queries_list = []
    else:
        answers = ""

    return "", queries, answers


# Helper function to generate string representation of user queries
def generate_queries_str(queries_list):
    delimiter = f"\n{'-' * 120}\n"
    queries_str = delimiter.join([q["prompt"] for q in queries_list])
    return queries_str


# Helper function to generate string representation of model answers
def generate_answers_str(answers_list):
    delimiter = f"\n{'-' * 120}\n"
    answers_str = delimiter.join([clean(a["answer"]) for a in answers_list])
    return answers_str


# Helper function to clean a model-generated answer
def clean(answer_str):
    answer_str = re.sub('^\s*:', '', answer_str)

    garbages = [
        "Here is the generated paragraph:",
        "Let me know if this meets your requirements!",
    ]
    for g in garbages:
        answer_str = answer_str.replace(g, "")
    answer_str = answer_str.strip()
    return answer_str


if __name__ == "__main__":
    # Gradio interface
    with gr.Blocks(theme=gr.themes.Default()) as demo:
        with gr.Row():
            with gr.Column(scale=2):
                query_txt = gr.Textbox(
                    placeholder="پرسش خود را این‌جا وارد کنید...",
                    label="Query", rtl=True)
            with gr.Column(scale=1):
                model = gr.Radio(
                    choices=[
                        "Llama-3-8B-8192",
                        "Llama-3-70B-8192",
                        "Llama-3.1-70B-Versatile",
                    ],
                    value=DEFAULT_MODEL,
                    label="LLM"
                )
                use_rag = gr.Checkbox(value=DEFAULT_USE_RAG, label="Use RAG?")
                temperature = gr.Slider(minimum=0, maximum=1,
                                        value=DEFAULT_TEMP,
                                        step=0.1, label="Temperature")

        with gr.Row():
            clear_btn = gr.Button("Clear", variant="stop")
            add_btn = gr.Button("Add", variant="secondary")
            submit_btn = gr.Button("Submit", variant="primary")

        with gr.Row():
            with gr.Column():
                queries_box = gr.Textbox(
                    placeholder="پرسش‌های شما این‌جا نمایش داده خواهد شد...",
                    label="Queries", interactive=False, rtl=True)
            with gr.Column():
                answers_box = gr.Textbox(
                    placeholder="پاسخ‌های مدل این‌جا نمایش داده خواهد شد...",
                    label="Answers", interactive=False, rtl=True)

        clear_btn.click(
            fn=clear_queries,
            inputs=[],
            outputs=[query_txt, model, temperature, use_rag,
                     queries_box, answers_box]
        )
        add_btn.click(
            fn=add_to_list,
            inputs=[query_txt, model, temperature, use_rag],
            outputs=[query_txt, queries_box]
        )
        submit_btn.click(
            fn=submit,
            inputs=[query_txt, model, temperature, use_rag],
            outputs=[query_txt, queries_box, answers_box]
        )

    demo.launch()