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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 and solve the problem with steps
def majority_vote_with_steps(question, num_iterations=10):
all_predictions = []
all_answer = []
steps_to_solve = []
for _ in range(num_iterations):
prediction = get_prediction(question)
# Process prediction to get steps and answer
answer, success = postprocess_completion(prediction, True, True)
all_predictions.append(prediction)
all_answer.append(answer)
if success:
steps_to_solve.append(answer) # Add the steps if code executes successfully
majority_voted_ans = get_majority_vote(all_answer)
# If steps to solve exist, return them, else fallback to "No steps found"
steps_solution = steps_to_solve[0] if steps_to_solve else "No steps found"
return majority_voted_ans, steps_solution
# Gradio interface for user input and output
def gradio_interface(question, correct_answer):
final_answer, steps_solution = majority_vote_with_steps(question, iterations)
return {
"Question": question,
"Majority-Voted Answer": final_answer,
"Steps to Solve": steps_solution,
"Correct Solution": correct_answer
}
# Custom CSS for enhanced design
custom_css = """
body {
background-color: #fafafa;
font-family: 'Open Sans', sans-serif;
}
.gradio-container {
background-color: #ffffff;
border: 3px solid #007acc;
border-radius: 15px;
padding: 20px;
box-shadow: 0 8px 20px rgba(0, 0, 0, 0.15);
max-width: 800px;
margin: 50px auto;
}
h1 {
font-family: 'Poppins', sans-serif;
color: #007acc;
font-weight: bold;
font-size: 32px;
text-align: center;
margin-bottom: 20px;
}
p {
font-family: 'Roboto', sans-serif;
font-size: 18px;
color: #333;
text-align: center;
margin-bottom: 15px;
}
input, textarea {
font-family: 'Montserrat', sans-serif;
font-size: 16px;
padding: 10px;
border: 2px solid #007acc;
border-radius: 10px;
background-color: #f1f8ff;
margin-bottom: 15px;
}
#math_question, #correct_answer {
font-size: 20px;
font-family: 'Poppins', sans-serif;
font-weight: 500px; /* Apply bold */
color: #007acc;
margin-bottom: 5px;
display: inline-block;
}
textarea {
min-height: 150px;
}
.gr-button-primary {
background-color: #007acc !important;
color: white !important;
border-radius: 10px !important;
font-size: 18px !important;
font-weight: bold !important;
padding: 10px 20px !important;
font-family: 'Montserrat', sans-serif !important;
transition: background-color 0.3s ease !important;
}
.gr-button-primary:hover {
background-color: #005f99 !important;
}
.gr-button-secondary {
background-color: #f44336 !important;
color: white !important;
border-radius: 10px !important;
font-size: 18px !important;
font-weight: bold !important;
padding: 10px 20px !important;
font-family: 'Montserrat', sans-serif !important;
transition: background-color 0.3s ease !important;
}
.gr-button-secondary:hover {
background-color: #c62828 !important;
}
.gr-output {
background-color: #e0f7fa;
border: 2px solid #007acc;
border-radius: 10px;
padding: 15px;
font-size: 16px;
font-family: 'Roboto', sans-serif;
font-weight: bold;
color: #00796b;
}
"""
# 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"), # Display the results in a JSON format
],
title="🔢 Math Question Solver",
description="Enter a math question to get the model's majority-voted answer and steps to solve the problem.",
css=custom_css # Apply custom CSS
)
if __name__ == "__main__":
interface.launch()
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