app.py

import gradio as gr
import requests

API_KEY = "one23@123"
LCG_SERVICE_URL_v1 = "http://bore.testsprep.online:8081/v1/theory_lcg"
LCG_SERVICE_URL_v2 = "http://bore.testsprep.online:8082/v1/theory_lcg"
LCG_SERVICE_URL_v3 = "http://bore.testsprep.online:8083/v1/theory_lcg"
LCG_SERVICE_URL_v4 = "http://bore.testsprep.online:8084/v1/theory_lcg"

MODEL2SERVICE = {
    'llama-3.1-sft-awq-v6.3': LCG_SERVICE_URL_v2,
    'storm-llama-3.1-sft-awq-v6.3': LCG_SERVICE_URL_v3
}

weights_mapping = {
    'beginner': {
        'easy': 0.6,
        'medium': 0.2,
        'hard': 0.2
    },
    'intermediate': {
        'easy': 0.2,
        'medium': 0.6,
        'hard': 0.2
    },
    'advanced': {
        'easy': 0.2,
        'medium': 0.2,
        'hard': 0.6
    }
}

LIST_USER_LEVEL = ["beginner", "intermediate", "advanced"]
LIST_MODELS = list(MODEL2SERVICE.keys())

template_los = """0. Identify the challenges in Matching sentence endings: More endings than questions, Some endings may be grammatically correct but not connected to the main ideas in the text, Information for possible endings is placed randomly in the passage
1. Understand and apply the steps for answering Matching sentence endings questions effectively: Read and underline keywords in incomplete sentences and endings, Scan using keywords in incomplete sentences to locate the information area, and Match incomplete sentences with endings and compare to the information area"""


def get_response_message(config):
    headers = {
        'accept': 'application/json',
        'Authorization': f'Bearer {API_KEY}',
        'Content-Type': 'application/json'
    }
    data = {
        "model": config["model_name"],
        "input_data": {
            "user_level": config["user_level"],
            "num_questions": config["num_questions"],
            "question_type": config["question_type"],
            "language": config["language"],
            "explanation_language": config["explanation_language"],
            "context": config["context"],
            "learning_outcomes": [lo.strip() for lo in config['learning_outcomes'].split('\n')],
            "mode": config["mode"],
            "weights": {
                "easy": 0,
                "hard": 0,
                "medium": 0
            }
        },
        "do_sample": True,
        "temperature": 0.7,
        "top_p": 0.9,
        "n": 1,
        "max_tokens": 8192,
        "stop": "string",
        "stream": False
    }
    try:
        response = requests.post(MODEL2SERVICE[config["model_name"]], headers=headers, json=data)
        return response.json()["data"]
    except Exception as e:
        return {"error_message": f"{e}"}


def generate_questions(model_name, user_level, num_questions, question_type, language, explanation_language, context,
                       learning_outcomes, mode):
    if mode == "Reviewing" and not context.strip():
        return {"error": "Với chế độ `revision`, `context` không được để trống."}

    config = {
        "model_name": model_name,
        "user_level": user_level,
        "num_questions": int(num_questions),
        "question_type": question_type,
        "language": language,
        "explanation_language": explanation_language,
        "context": context,
        "learning_outcomes": learning_outcomes,
        "mode": mode
    }
    return get_response_message(config)


iface = gr.Interface(
    fn=generate_questions,
    inputs=[
        gr.Dropdown(LIST_MODELS, label="Model Usage", value=LIST_MODELS[0]),
        gr.Dropdown(LIST_USER_LEVEL, label="User Level", value=LIST_USER_LEVEL[0]),
        gr.Number(value=5, label="Number of Questions"),
        gr.Dropdown(["short_answer", "single_choice", "multiple_choice"], label="Question Type", value="single_choice"),
        gr.Dropdown(["en", "vi"], label="Language", value="en"),
        gr.Dropdown(["en", "vi"], label="Explanation Language", value="en"),
        gr.Textbox(lines=5, placeholder="Enter context here...",
                   label="Context (Lesson content or Reading comprehension passage)"),
        gr.Textbox(lines=5, value=template_los, label="Learning Outcomes"),
        gr.Dropdown(["revision", "practice"], label="Mode", value="practice")
    ],
    outputs=gr.JSON(label="Generated Questions"),
    title="Learning Content Generation",
    description="Generate questions based on user input and learning outcomes."
)

iface.launch()