app.py

import gradio as gr
from huggingface_hub import InferenceClient
import time

# Initialize the client with your model
client = InferenceClient("zhangchenxu/TinyV-1.5B")

# The prompt template for the LLM verifier
LV_PROMPT = """
You are an AI tasked with identifying false negatives in answer verification. A false negative occurs when a model's answer is essentially correct but is marked as incorrect due to minor discrepancies or formatting issues. Your job is to analyze the given question, ground truth answer, and model answer to determine if the model's answer is actually correct despite appearing different from the ground truth.

<question>{question}</question>

<ground_truth_answer>{ground_truth}</ground_truth_answer>

<model_answer>{model_answer}</model_answer>

Return "True" if the model's answer is correct, otherwise return "False".
"""

# Define our example sets
EXAMPLES = [
    {
        "name": "Order-Insensitive",
        "question": "Determine all real values of $x$ for which $(x+8)^{4}=(2 x+16)^{2}$.",
        "ground_truth": "-6,-8,-10",
        "model_answer": "-10, -8, -6",
        "temp": 0.3,
        "top_p": 0.95,
        "tokens": 2
    },
    {
        "name": "Latex Expression",
        "question": "A bag contains 3 green balls, 4 red balls, and no other balls. Victor removes balls randomly from the bag, one at a time, and places them on a table. Each ball in the bag is equally likely to be chosen each time that he removes a ball. He stops removing balls when there are two balls of the same colour on the table. What is the probability that, when he stops, there is at least 1 red ball and at least 1 green ball on the table?",
        "ground_truth": "$\\frac{4}{7}$",
        "model_answer": "4/7",
        "temp": 0.3,
        "top_p": 0.95,
        "tokens": 2
    },
    {
        "name": "Variable Labeling",
        "question": "If $T=x^{2}+\\frac{1}{x^{2}}$, determine the values of $b$ and $c$ so that $x^{6}+\\frac{1}{x^{6}}=T^{3}+b T+c$ for all non-zero real numbers $x$.",
        "ground_truth": "-3,0",
        "model_answer": "b=-3, c=0",
        "temp": 0.3,
        "top_p": 0.95,
        "tokens": 2
    },
    {
        "name": "Paraphrase",
        "question": "Peter has 8 coins, of which he knows that 7 are genuine and weigh the same, while one is fake and differs in weight, though he does not know whether it is heavier or lighter. Peter has access to a balance scale, which shows which side is heavier but not by how much. For each weighing, Peter must pay Vasya one of his coins before the weighing. If Peter pays with a genuine coin, Vasya will provide an accurate result; if a fake coin is used, Vasya will provide a random result. Peter wants to determine 5 genuine coins and ensure that none of these genuine coins are given to Vasya. Can Peter guaranteedly achieve this?",
        "ground_truth": "Petya can guarantee finding 5 genuine coins.",
        "model_answer": "Yes, Peter can guarantee finding 5 genuine coins while ensuring that none of these genuine coins are paid to Vasya.",
        "temp": 0.3,
        "top_p": 0.95,
        "tokens": 2
    },
    {
        "name": "False Example",
        "question": "What is the tallest mountain in the world?",
        "ground_truth": "Mount Everest is the tallest mountain in the world.",
        "model_answer": "K2 is the tallest mountain on Earth.",
        "temp": 0.3,
        "top_p": 0.95,
        "tokens": 2
    }
]

# Main verification function
def verify_answer(question, ground_truth, model_answer, temperature, top_p, max_tokens):
    # Format the prompt with user inputs
    prompt = LV_PROMPT.format(
        question=question,
        ground_truth=ground_truth,
        model_answer=model_answer
    )
    
    # Prepare the message format required by the API
    messages = [
        {"role": "user", "content": prompt}
    ]
    
    # Initialize response
    response_text = ""
    
    try:
        # Stream the response for better UX
        for message in client.chat_completion(
            messages,
            max_tokens=max_tokens,
            stream=True,
            temperature=temperature,
            top_p=top_p,
        ):
            token = message.choices[0].delta.content
            if token:
                response_text += token
                yield response_text
    except Exception as e:
        yield f"Error: {str(e)}"

# Function to load an example when its button is clicked
def load_example(example_index):
    example = EXAMPLES[example_index]
    return (
        example["question"],
        example["ground_truth"],
        example["model_answer"],
        example["temp"],
        example["top_p"],
        example["tokens"]
    )

# Create the Gradio interface
with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", font=[gr.themes.GoogleFont("Inter"), "ui-sans-serif", "system-ui", "sans-serif"]), title="TinyV") as demo:
    # Header with title and description
    with gr.Row():
        with gr.Column():
            gr.Markdown(
                """
                # TinyV - Answer Verification Tool
                
                This tool verifies if an answer is correct compared to a ground truth answer for RL.
                """
            )
    
    # Main interface
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown(
                """
                ## How to Use
                
                1. Enter the question in the first box
                2. Enter the ground truth answer
                3. Enter the model's answer to verify
                4. Adjust model parameters if needed
                5. Click "Verify Answer" to see the result
                
                ### What this tool does
                
                This tool determines if a model's answer is semantically correct compared to a ground truth answer using a fine-tuned LLM.
                
                The model analyzes both answers and returns:
                - **True** if the model answer is correct
                - **False** if the model answer is incorrect
                
                ### API Usage Example
                ```python
                from gradio_client import Client
                
                client = Client("zhangchenxu/TinyV")
                result = client.predict(
                    question="Determine all real values of $x$ for which $(x+8)^{4}=(2 x+16)^{2}$.",
                    ground_truth="-6,-8,-10",
                    model_answer="-10, -8, -6",
                    temperature=0.3,
                    top_p=0.95,
                    max_tokens=1,
                    api_name="/verify_answer"
                )
                print(result)
                ```
                """
            )
            
            # Model parameters (hidden in a collapsible section)
            with gr.Accordion("Advanced Settings", open=False):
                temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.3, step=0.1, label="Temperature")
                top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
                max_tokens = gr.Slider(minimum=1, maximum=256, value=1, step=1, label="Max Tokens")
                
        with gr.Column(scale=1):
            gr.Markdown("## Input")
            question = gr.Textbox(lines=3, label="Question", placeholder="Enter the question here...")
            ground_truth = gr.Textbox(lines=5, label="Ground Truth Answer", placeholder="Enter the correct answer here...")
            model_answer = gr.Textbox(lines=5, label="Model Answer", placeholder="Enter the answer to verify here...")
            
            # Examples section as buttons
            gr.Markdown("### Try an example:")
            with gr.Row():
                example_buttons = []
                for i, example in enumerate(EXAMPLES):
                    btn = gr.Button(example["name"], size="sm")
                    example_buttons.append(btn)
                    # Connect each button to the load_example function
                    btn.click(
                        fn=lambda idx=i: load_example(idx),
                        outputs=[question, ground_truth, model_answer, temperature, top_p, max_tokens]
                    )
            
            verify_btn = gr.Button("Verify Answer", variant="primary")
            
            gr.Markdown("## Result")
            result = gr.Textbox(label="Verification Result", placeholder="Result will appear here...", lines=5)
                
    # Connect the interface to the verification function
    verify_btn.click(
        verify_answer,
        inputs=[question, ground_truth, model_answer, temperature, top_p, max_tokens],
        outputs=result
    )
    
    # Run verification when an example is loaded (optional)
    for btn in example_buttons:
        btn.click(
            fn=verify_answer,
            inputs=[question, ground_truth, model_answer, temperature, top_p, max_tokens],
            outputs=result,
            _js="() => {setTimeout(() => document.querySelector('#verify-btn').click(), 100)}",
            queue=False
        )

# Define the public API
demo.queue()
# Launch the app
if __name__ == "__main__":
    demo.launch()