app.py

import gradio as gr
from transformers import pipeline

# Load the model and tokenizer
def load_model():
    # Load the NuminaMath-72B-CoT model
    pipe = pipeline(
        "text-generation",
        model="AI-MO/NuminaMath-72B-CoT",
        torch_dtype="auto",
        device_map="auto"  # Automatically map to available GPU/CPU
    )
    return pipe

# Initialize the pipeline
model_pipeline = load_model()

# Define the function to process inputs
def solve_math_question(prompt):
    # Generate output using the model
    outputs = model_pipeline(prompt, max_new_tokens=1024, do_sample=False)
    return outputs[0]["generated_text"]

# Define the Gradio interface
with gr.Blocks() as app:
    gr.Markdown("# NuminaMath-72B-CoT Math Question Solver")
    gr.Markdown(
        "Ask a math-related question, and the model will attempt to solve it with reasoning!"
    )

    with gr.Row():
        question = gr.Textbox(
            label="Your Math Question",
            placeholder="E.g., For how many values of the constant k will the polynomial x^2 + kx + 36 have two distinct integer roots?",
        )
        output = gr.Textbox(label="Model Output")

    submit_button = gr.Button("Solve")
    submit_button.click(solve_math_question, inputs=question, outputs=output)

# Launch the app
app.launch()