app.py

import gradio as gr
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
from transformers import StopStringCriteria, StoppingCriteriaList

from datasets import load_dataset, concatenate_datasets
import torch
import threading

model_id = "PhysicsWallahAI/Aryabhatta-1.0"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

def process_questions(example):
    example["question_text"] = example["question"]
    options = "\n".join([f"{chr(65+e)}. {op}" for e, op in enumerate(example["options"])])
    example["question_text"] += "\n" + options
    return example

dataset = concatenate_datasets([
    load_dataset("PhysicsWallahAI/JEE-Main-2025-Math", "jan", split="test"),
    load_dataset("PhysicsWallahAI/JEE-Main-2025-Math", "apr", split="test"),
])
examples = dataset.map(process_questions, remove_columns=dataset.column_names)["question_text"]



# add options

stop_strings = ["<|im_end|>", "<|end|>", "<im_start|>", "```python\n", "<|im_start|>", "]}}]}}]"]


def strip_bad_tokens(s, stop_strings):
    for suffix in stop_strings:
        if s.endswith(suffix):
            return s[:-len(suffix)]
    return s

def generate_answer_stream(question):
    messages = [
        {'role': 'system', 'content': 'Think step-by-step; put only the final answer inside \\boxed{}.'},
        {'role': 'user', 'content': question}
    ]

    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )

    inputs = tokenizer([text], return_tensors="pt")

    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
    stopping = StoppingCriteriaList([StopStringCriteria(tokenizer, stop_strings)])

    
    thread = threading.Thread(
        target=model.generate,
        kwargs=dict(
            **inputs,
            streamer=streamer,
            max_new_tokens=4096,
            stopping_criteria=stopping,
        )
    )
    thread.start()
    
    output = ""
    for token in streamer:
        output += token
        output = strip_bad_tokens(output, stop_strings)
        yield output

demo = gr.Interface(
    fn=generate_answer_stream,
    inputs=gr.Textbox(lines=4, label="Enter a Math Question"),
    outputs=gr.Textbox(label="Model's Response", lines=10),
    examples=examples,
    title="Aryabhatta 1.0 Demo",
    description=""
)

if __name__ == "__main__":
    demo.launch()