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".
"""

# 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": "system", "content": "You are a helpful AI assistant that verifies answers."},
        {"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)}"

# Create the Gradio interface
with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue"), title="Answer Verification Tool") as demo:
    # Header with title and description
    with gr.Row():
        with gr.Column(scale=1):
            gr.Image("https://huggingface.co/front/assets/huggingface_logo.svg", scale=1, show_label=False, height=64)
        with gr.Column(scale=5):
            gr.Markdown(
                """
                # Answer Verification Tool
                
                This tool verifies if an answer is correct compared to a ground truth answer, even if there are minor differences in formatting or wording.
                """
            )
    
    # 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, even if there are minor discrepancies in formatting or wording.
                
                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="What is the capital of France?",
                    ground_truth="The capital of France is Paris.",
                    model_answer="Paris is the capital of France.",
                    temperature=0.3,
                    top_p=0.95,
                    max_tokens=128,
                    api_name="/verify"
                )
                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=32, maximum=512, value=128, step=32, label="Max Tokens")
                
        with gr.Column(scale=1):
            with gr.Box():
                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...")
                
                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
    )
    
    # Examples
    with gr.Accordion("Examples", open=True):
        gr.Examples(
            examples=[
                [
                    "What is the capital of France?",
                    "The capital of France is Paris.",
                    "Paris is the capital of France.",
                    0.3,
                    0.95,
                    128,
                ],
                [
                    "What is 2+2?",
                    "4",
                    "The answer is 4.",
                    0.3,
                    0.95,
                    128,
                ],
                [
                    "When was the Declaration of Independence signed?",
                    "July 4, 1776",
                    "The Declaration of Independence was signed on July 4th, 1776.",
                    0.3,
                    0.95,
                    128,
                ],
                [
                    "List the first three planets from the sun.",
                    "Mercury, Venus, Earth",
                    "The first three planets from the sun are Mercury, Venus, and Earth.",
                    0.3,
                    0.95,
                    128,
                ],
            ],
            inputs=[question, ground_truth, model_answer, temperature, top_p, max_tokens],
            outputs=result,
        )
    
    # Add footer with extra information
    with gr.Row():
        gr.Markdown(
            """
            ### About
            This tool uses the zhangchenxu/TinyV-1.5B model to verify answers.
            
            The verification is based on semantic similarity rather than exact matching,
            allowing for different phrasings and formats of the same correct answer.
            """
        )

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