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| 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": 1 | |
| }, | |
| { | |
| "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": 1 | |
| }, | |
| { | |
| "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": 1 | |
| }, | |
| { | |
| "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": 1 | |
| }, | |
| { | |
| "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": 1 | |
| } | |
| ] | |
| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| # Initialize the client with the 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". | |
| """ | |
| # Example sets for quick testing | |
| 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 | |
| } | |
| ] | |
| # Verification function | |
| def verify_answer(question, ground_truth, model_answer, temperature, top_p, max_tokens): | |
| if not question or not ground_truth or not model_answer: | |
| return "Please fill in all fields: Question, Ground Truth Answer, and Model Answer." | |
| # Format the prompt with user inputs | |
| prompt = LV_PROMPT.format( | |
| question=question, | |
| ground_truth=ground_truth, | |
| model_answer=model_answer | |
| ) | |
| # Prepare messages for 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 | |
| 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 proper component initialization | |
| with gr.Blocks(title="TinyV") as demo: | |
| # Define states (invisible components to store values) | |
| temperature = gr.State(value=0.3) | |
| top_p = gr.State(value=0.95) | |
| max_tokens = gr.State(value=2) | |
| # Header | |
| gr.Markdown( | |
| """ | |
| # TinyV - LLM-Based Verifier for RL | |
| Verify if model-generated answers are semantically correct compared to ground truth. | |
| """ | |
| ) | |
| # Main content area | |
| with gr.Row(): | |
| # Left column - Inputs | |
| with gr.Column(scale=3): | |
| question = gr.Textbox( | |
| lines=3, | |
| label="Question", | |
| placeholder="Enter the mathematical problem or question here..." | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| ground_truth = gr.Textbox( | |
| lines=3, | |
| label="Ground Truth Answer", | |
| placeholder="Enter the correct answer here..." | |
| ) | |
| with gr.Column(): | |
| model_answer = gr.Textbox( | |
| lines=3, | |
| label="Model Answer", | |
| placeholder="Enter the answer to verify here..." | |
| ) | |
| verify_btn = gr.Button("Verify Answer", variant="primary") | |
| # Right column - Result | |
| with gr.Column(scale=2): | |
| result = gr.Textbox( | |
| label="Verification Result", | |
| placeholder="The verification result will appear here...", | |
| lines=10 | |
| ) | |
| # Examples section | |
| gr.Markdown("### Examples") | |
| with gr.Row(): | |
| for i, ex in enumerate(EXAMPLES): | |
| btn = gr.Button(ex["name"]) | |
| btn.click( | |
| fn=lambda idx=i: load_example(idx), | |
| outputs=[question, ground_truth, model_answer, temperature, top_p, max_tokens] | |
| ) | |
| # Also run verification when example is loaded | |
| btn.click( | |
| fn=verify_answer, | |
| inputs=[question, ground_truth, model_answer, temperature, top_p, max_tokens], | |
| outputs=result, | |
| queue=False | |
| ) | |
| # Advanced Settings in accordion | |
| with gr.Accordion("Advanced Settings", open=False): | |
| with gr.Row(): | |
| temp_slider = gr.Slider(0, 1, value=0.3, step=0.1, label="Temperature") | |
| top_p_slider = gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)") | |
| max_tokens_slider = gr.Slider(1, 128, value=2, step=1, label="Max Tokens") | |
| # Connect sliders to state values | |
| temp_slider.change(lambda x: x, inputs=[temp_slider], outputs=[temperature]) | |
| top_p_slider.change(lambda x: x, inputs=[top_p_slider], outputs=[top_p]) | |
| max_tokens_slider.change(lambda x: x, inputs=[max_tokens_slider], outputs=[max_tokens]) | |
| # API usage in accordion | |
| with gr.Accordion("API Usage", open=False): | |
| gr.Markdown( | |
| """ | |
| ```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=1, | |
| api_name="/verify_answer" | |
| ) | |
| print(result) | |
| ``` | |
| """ | |
| ) | |
| # Footer | |
| gr.Markdown( | |
| """ | |
| Powered by TinyV-1.5B model. This tool verifies semantic equivalence between answers, allowing for different formatting, ordering, notation, and phrasing. | |
| """ | |
| ) | |
| # Connect the interface to the verification function | |
| verify_btn.click( | |
| fn=verify_answer, | |
| inputs=[question, ground_truth, model_answer, temperature, top_p, max_tokens], | |
| outputs=result | |
| ) | |
| # Define the public API | |
| demo.queue() | |
| # Launch the app | |
| if __name__ == "__main__": | |
| demo.launch() |