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import gradio as gr
import ctranslate2
from transformers import AutoTokenizer
from huggingface_hub import snapshot_download
from codeexecutor import postprocess_completion, get_majority_vote
# Define the model and tokenizer loading
model_prompt = "Solve the following mathematical problem: "
tokenizer = AutoTokenizer.from_pretrained("AI-MO/NuminaMath-7B-TIR")
model_path = snapshot_download(repo_id="Makima57/deepseek-math-Numina")
generator = ctranslate2.Generator(model_path, device="cpu", compute_type="int8")
iterations = 10
# Function to generate predictions using the model
def get_prediction(question):
input_text = model_prompt + question
input_tokens = tokenizer.tokenize(input_text)
results = generator.generate_batch([input_tokens])
output_tokens = results[0].sequences[0]
predicted_answer = tokenizer.convert_tokens_to_string(output_tokens)
return predicted_answer
# Function to perform majority voting across multiple predictions
def majority_vote(question, num_iterations=10):
all_predictions = []
all_answer = []
for _ in range(num_iterations):
prediction = get_prediction(question)
answer = postprocess_completion(prediction, True, True)
all_predictions.append(prediction)
all_answer.append(answer)
majority_voted_pred = max(set(all_predictions), key=all_predictions.count)
majority_voted_ans = get_majority_vote(all_answer)
return majority_voted_pred, all_predictions, majority_voted_ans
# Gradio interface for user input and output
def gradio_interface(question, correct_answer):
final_prediction, all_predictions, final_answer = majority_vote(question, iterations)
return {
"Question": question,
"Generated Answers (10 iterations)": all_predictions,
"Majority-Voted Prediction": final_prediction,
"Correct solution": correct_answer,
"Majority answer": final_answer
}
# Custom CSS for styling
custom_css = """
body {
background-color: #f4f7fb;
font-family: 'Roboto', sans-serif;
}
.gradio-container {
border-radius: 15px;
border: 2px solid #e0e4e7;
padding: 20px;
box-shadow: 0 10px 15px rgba(0, 0, 0, 0.1);
background-color: white;
max-width: 700px;
margin: 0 auto;
}
h1, h2, p {
text-align: center;
color: #333;
}
input, textarea {
border-radius: 8px;
border: 1px solid #ccc;
padding: 10px;
font-size: 16px;
}
.gr-button {
background-color: #4caf50;
color: white;
border-radius: 8px;
padding: 10px 20px;
font-size: 16px;
transition: background-color 0.3s;
}
.gr-button:hover {
background-color: #45a049;
}
.gr-output {
background-color: #f1f1f1;
border-radius: 8px;
padding: 15px;
font-size: 14px;
}
"""
# Gradio app setup
interface = gr.Interface(
fn=gradio_interface,
inputs=[
gr.Textbox(label="Math Question", placeholder="Enter your math question here...", elem_id="math_question"),
gr.Textbox(label="Correct Answer", placeholder="Enter the correct answer here...", elem_id="correct_answer"),
],
outputs=[
gr.JSON(label="Results", elem_id="results"), # Display the results in a JSON format
],
title="Math Question Solver",
description="Enter a math question to get the model prediction and see all generated answers.",
css=custom_css # Apply custom CSS
)
if __name__ == "__main__":
interface.launch()
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