Create app.py

app.py

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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()
+