app.py

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 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;
    }
    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;
    }
    label {
        font-family: 'Poppins', sans-serif;
        font-size: 20px;
        color: #007acc;
        margin-bottom: 5px;
        display: inline-block;
    }
    #math_question, #correct_answer {
        background-color: #e6f2ff;
        color: #333;
        border-radius: 8px;
        padding: 12px;
        font-weight: bold;  /* Apply bold */
    }
    #results {
        font-family: 'Courier New', Courier, monospace;
        background-color: #f3f4f6;
        color: #005f99;
        font-size: 16px;
        padding: 10px;
        border-radius: 8px;
    }
    /* Make the JSON {} icon smaller */
    .gr-output-json .json-key {
        font-size: 20px !important;
    }
    .gr-output-json .json-key img {
        width: 20px !important;
        height: 20px !important;
    }
"""

# 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", elem_classes="bold-label"),
        gr.Textbox(label="Correct Answer", placeholder="Enter the correct answer here...", elem_id="correct_answer", elem_classes="bold-label"),
    ],
    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()