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import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel, Trainer, TrainingArguments, DataCollatorForLanguageModeling
from peft import LoraConfig, get_peft_model
from datasets import Dataset
import gradio as gr # Import Gradio for UI
# Define a dataset of 20 incorrect math memes with corrections and explanations.
data = [
{"text": "Incorrect: 8 ÷ 2(2+2) = 1? Correct: 8 ÷ 2(2+2) = 16. Explanation: Evaluate parentheses first then perform division and multiplication sequentially."},
{"text": "Incorrect: 5 + 5 = 20? Correct: 5 + 5 = 10. Explanation: Simple addition error."},
{"text": "Incorrect: 6 * 6 = 36 but 6 / 6 = 6? Correct: 6 / 6 = 1. Explanation: A number divided by itself equals 1."},
{"text": "Incorrect: 2^3 = 6? Correct: 2^3 = 8. Explanation: 2 cubed is 8."},
{"text": "Incorrect: √16 = 5? Correct: √16 = 4. Explanation: The square root of 16 is 4."},
{"text": "Incorrect: 9 - 3 = 3? Correct: 9 - 3 = 6. Explanation: Correct subtraction yields 6."},
{"text": "Incorrect: 4 * 4 = 8? Correct: 4 * 4 = 16. Explanation: Multiplication error."},
{"text": "Incorrect: 10 / 2 = 10? Correct: 10 / 2 = 5. Explanation: Division error."},
{"text": "Incorrect: 15% of 200 = 50? Correct: 15% of 200 = 30. Explanation: 15% of 200 equals 30."},
{"text": "Incorrect: 100 / 4 = 20? Correct: 100 / 4 = 25. Explanation: Division error."},
{"text": "Incorrect: 3 + 7 = 11? Correct: 3 + 7 = 10. Explanation: 3 plus 7 equals 10."},
{"text": "Incorrect: 2 * 3 + 4 = 14? Correct: 2 * 3 + 4 = 10. Explanation: Follow order of operations: multiply then add."},
{"text": "Incorrect: 12 / 3 * 2 = 10? Correct: 12 / 3 * 2 = 8. Explanation: 12 divided by 3 is 4; 4 times 2 is 8."},
{"text": "Incorrect: 7 * 7 = 42? Correct: 7 * 7 = 49. Explanation: Multiplication error."},
{"text": "Incorrect: 14 - 7 = 8? Correct: 14 - 7 = 7. Explanation: Subtraction error."},
{"text": "Incorrect: (3 + 2) * 2 = 12? Correct: (3 + 2) * 2 = 10. Explanation: Add first, then multiply."},
{"text": "Incorrect: 50% of 100 = 60? Correct: 50% of 100 = 50. Explanation: 50% is half of 100."},
{"text": "Incorrect: 9 + 9 = 18 then 18 / 2 = 10? Correct: 18 / 2 = 9. Explanation: Division error."},
{"text": "Incorrect: 5! = 100? Correct: 5! = 120. Explanation: 5 factorial is 120."},
{"text": "Incorrect: 3^2 + 4^2 = 14? Correct: 3^2 + 4^2 = 25. Explanation: 9 + 16 equals 25."}
]
# Convert the list to a Hugging Face Dataset.
dataset = Dataset.from_list(data)
print("Dataset created with", len(dataset), "examples.")
# Load the GPT-2 tokenizer and model.
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
# GPT-2 does not have an official pad token; use the eos_token.
tokenizer.pad_token = tokenizer.eos_token
model = GPT2LMHeadModel.from_pretrained("gpt2")
# Configure LoRA for efficient fine-tuning.
lora_config = LoraConfig(
task_type="CAUSAL_LM", # For language modeling.
r=8,
lora_alpha=32,
lora_dropout=0.1
)
# Wrap the model with LoRA.
model = get_peft_model(model, lora_config)
print("Model loaded and LoRA configured.")
# Tokenize each example.
def tokenize_function(example):
return tokenizer(example["text"], truncation=True, max_length=128, padding="max_length")
tokenized_dataset = dataset.map(tokenize_function, batched=False)
tokenized_dataset.set_format(type="torch", columns=["input_ids", "attention_mask"])
print("Dataset tokenized.")
training_args = TrainingArguments(
output_dir="output",
per_device_train_batch_size=1,
num_train_epochs=5, # Increase epochs to help the model learn from 20 examples.
logging_steps=1,
save_strategy="epoch",
learning_rate=3e-5, # Slightly lower learning rate.
weight_decay=0.01,
report_to="none"
)
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_dataset,
data_collator=data_collator
)
print("Starting training...")
trainer.train()
print("Training complete!")
# Gradio UI for testing the model
def correct_math(prompt):
model.eval() # Set model to evaluation mode.
inputs = tokenizer(prompt, return_tensors="pt", padding=True)
input_ids = inputs.input_ids.to(model.device)
attention_mask = inputs.attention_mask.to(model.device)
outputs = model.generate(
input_ids,
attention_mask=attention_mask,
max_new_tokens=50,
do_sample=True,
temperature=0.7,
top_k=50,
top_p=0.90,
pad_token_id=tokenizer.eos_token_id
)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
return result
# Create Gradio interface
gr.Interface(fn=correct_math, inputs="text", outputs="text", title="Math Correction Model", description="Enter an incorrect math statement to get the correct answer and explanation.").launch()
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