temp2.py

import gradio as gr
# import ctranslate2
# from transformers import AutoTokenizer
# from huggingface_hub import snapshot_download
from codeexecutor import get_majority_vote, type_check, postprocess_completion, draw_polynomial_plot
import re
import os

# Define the model and tokenizer loading
model_prompt = "Explain and solve the following mathematical problem step by step, showing all work: "
# 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 = 4

# # 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],
#         max_length=512,
#         sampling_temperature=0.7,
#         sampling_topk=40,
#     )
#     output_tokens = results[0].sequences[0]
#     predicted_answer = tokenizer.convert_tokens_to_string(output_tokens)
#     return predicted_answer

def get_prediction(question):
    return "Solve the following mathematical problem: what is  sum of polynomial 2x+3 and 3x?\n### Solution: To solve the problem of summing the polynomials \\(2x + 3\\) and \\(3x\\), we can follow these steps:\n\n1. Define the polynomials.\n2. Sum the polynomials.\n3. Simplify the resulting polynomial expression.\n\nLet's implement this in Python using the sympy library.\n\n```python\nimport sympy as sp\n\n# Define the variable\nx = sp.symbols('x')\n\n# Define the polynomials\npoly1 = 2*x + 3\npoly2 = 3*x\n\n# Sum the polynomials\nsum_poly = poly1 + poly2\n\n# Simplify the resulting polynomial\nsimplified_sum_poly = sp.simplify(sum_poly)\n\n# Print the simplified polynomial\nprint(simplified_sum_poly)\n```\n```output\n5*x + 3\n```\nThe sum of the polynomials \\(2x + 3\\) and \\(3x\\) is \\(\\boxed{5x + 3}\\).\n"

# Function to parse the prediction to extract the answer and steps
def parse_prediction(prediction):
    lines = prediction.strip().split('\n')
    answer = None
    steps = []
    for line in lines:
        # Check for "Answer:" or "answer:"
        match = re.match(r'^\s*(?:Answer|answer)\s*[:=]\s*(.*)', line)
        if match:
            answer = match.group(1).strip()
        else:
            steps.append(line)
    if answer is None:
        # If no "Answer:" found, assume last line is the answer
        answer = lines[-1].strip()
        steps = lines
    steps_text = '\n'.join(steps).strip()
    return answer, steps_text

def extract_boxed_answer(text):
    # Regular expression to find the content inside \\boxed{}
    match = re.search(r'\\boxed\{(.*?)\}', text)
    if match:
        return match.group(1)  # Return the content inside the \\boxed{}
    return None


# Function to perform majority voting and get steps
def majority_vote_with_steps(question, num_iterations=10):
    all_predictions = []
    all_answers = []
    steps_list = []

    for _ in range(num_iterations):
        prediction = get_prediction(question)
        answer, success = postprocess_completion(prediction, return_status=True, last_code_block=True)
        
        if success:
            all_predictions.append(prediction)
            all_answers.append(answer)
            steps_list.append(prediction)
           
            
        else:
            answer, steps = parse_prediction(prediction)
            all_predictions.append(prediction)
            all_answers.append(answer)
            steps_list.append(steps)
            
    if success:
            majority_voted_ans = get_majority_vote(all_answers)
            expression=majority_voted_ans
            print(type_check(expression))
            if type_check(expression) == "Polynomial":
                plotfile = draw_polynomial_plot(expression) 
    else:
        plotfile = None

            
        
         # Draw plot of polynomial

    # Find the steps corresponding to the majority voted answer
    for i, ans in enumerate(all_answers):
        if ans == majority_voted_ans:
            steps_solution = steps_list[i]
            answer = parse_prediction(steps_solution)
            break
    else:
        answer = majority_voted_ans
        steps_solution = "No steps found"

    return answer, steps_solution, plotfile

# Function to handle chat-like interaction
def chat_interface(history, question):
    # Get the answer and steps from the majority voting method
    final_answer, steps_solution, plotfile = majority_vote_with_steps(question, iterations)
    
    # Append the question and answer to the chat history
    history.append(("User", question))
    history.append(("MathBot", f"Answer: {final_answer}\nSteps:\n{steps_solution}"))
    
    return history, plotfile

# Gradio app setup with chat UI
interface = gr.Interface(
    fn=chat_interface,
    inputs=[
        gr.Chatbot(label="Chat with MathBot", elem_id="chat_history"),
        gr.Textbox(label="Your Question", placeholder="Ask a math question...", elem_id="math_question"),
    ],
    outputs=[
        gr.Chatbot(label="Chat History"),  # Chat-like display of conversation
        gr.Image(label="Polynomial Plot")
    ],
    title="🔢 Math Question Solver - Chat Mode",
    description="Chat with MathBot and ask any math-related question. It will explain the solution step by step and provide a majority-voted answer.",
    allow_flagging="auto",
    flagging_dir="./flagged_data",
)

if __name__ == "__main__":
    interface.launch()
    # history, plotfile=chat_interface(["hello"], ["what is the sum of 2x+3 and 3x"])
    # print(history, plotfile)