Create app.py

app.py

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+from transformers import GPT2LMHeadModel, TrainingArguments, Trainer, DataCollatorForLanguageModeling, GPT2Tokenizer
+import gradio as gr
+
+# Create a list of 30 emoji math problems with their solutions.
+# Format: "Q: [emoji math equation]\nA: [solution]"
+data = [
+    "Q: 🍎 + 🍎 + 🍎 = 12\nA: 4",
+    "Q: 🎲 + 🎲 = 12\nA: 6",
+    "Q: 🚗 + 🚗 + 🚗 + 🚗 = 20\nA: 5",
+    "Q: 🍌 + 🍌 + 🍌 + 🍌 + 🍌 = 15\nA: 3",
+    "Q: 🍓 + 🍓 = 8\nA: 4",
+    "Q: 🍕 + 🍕 + 🍕 = 18\nA: 6",
+    "Q: 🍩 + 🍩 + 🍩 + 🍩 = 20\nA: 5",
+    "Q: 🌟 + 🌟 + 🌟 = 9\nA: 3",
+    "Q: 🎈 + 🎈 = 14\nA: 7",
+    "Q: 🎂 + 🎂 + 🎂 = 15\nA: 5",
+    "Q: 🍪 + 🍪 + 🍪 + 🍪 = 16\nA: 4",
+    "Q: 🍭 + 🍭 + 🍭 = 15\nA: 5",
+    "Q: 🧁 + 🧁 = 10\nA: 5",
+    "Q: 🥑 + 🥑 + 🥑 = 12\nA: 4",
+    "Q: 🍇 + 🍇 = 10\nA: 5",
+    "Q: 🍒 + 🍒 + 🍒 = 15\nA: 5",
+    "Q: 🍍 + 🍍 = 14\nA: 7",
+    "Q: 🍉 + 🍉 + 🍉 + 🍉 = 20\nA: 5",
+    "Q: 🥭 + 🥭 = 16\nA: 8",
+    "Q: 🍈 + 🍈 + 🍈 = 9\nA: 3",
+    "Q: 🍑 + 🍑 + 🍑 + 🍑 = 20\nA: 5",
+    "Q: 🍏 + 🍏 = 10\nA: 5",
+    "Q: 🍋 + 🍋 + 🍋 = 12\nA: 4",
+    "Q: 🍊 + 🍊 = 10\nA: 5",
+    "Q: 🥝 + 🥝 + 🥝 = 15\nA: 5",
+    "Q: 🍐 + 🍐 = 8\nA: 4",
+    "Q: 🍆 + 🍆 + 🍆 + 🍆 = 16\nA: 4",
+    "Q: 🥕 + 🥕 = 10\nA: 5",
+    "Q: 🌽 + 🌽 + 🌽 = 9\nA: 3",
+    "Q: 🥔 + 🥔 + 🥔 + 🥔 = 20\nA: 5"
+]
+
+# For training with Hugging Face's datasets, we create a dictionary.
+from datasets import Dataset
+dataset = Dataset.from_dict({"text": data})
+
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+tokenizer.pad_token = tokenizer.eos_token
+
+def tokenize_function(example):
+    return tokenizer(example["text"], truncation=True, max_length=128, padding="max_length")
+
+tokenized_dataset = dataset.map(tokenize_function, batched=True)
+tokenized_dataset.set_format(type="torch", columns=["input_ids", "attention_mask"])
+
+# Load GPT-2 model
+model = GPT2LMHeadModel.from_pretrained("gpt2")
+model.config.pad_token_id = tokenizer.eos_token_id
+
+# Create a data collator for language modeling that will handle padding dynamically
+data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
+
+# Define training arguments
+training_args = TrainingArguments(
+    output_dir="./emoji_math_model",
+    overwrite_output_dir=True,
+    num_train_epochs=8,
+    per_device_train_batch_size=4,
+    save_steps=50,
+    save_total_limit=2,
+    logging_steps=10,
+    learning_rate=1e-5
+)
+
+# Initialize the Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_dataset,
+    data_collator=data_collator,
+)
+
+# Start training
+trainer.train()
+
+import logging
+logging.getLogger("transformers").setLevel(logging.ERROR)  # Suppress transformer warnings
+
+import re
+
+def generate_single_answer(prompt, max_new_tokens=10):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    input_ids = inputs["input_ids"].to(model.device)
+    attention_mask = inputs["attention_mask"].to(model.device)
+
+    output = model.generate(
+        input_ids,
+        attention_mask=attention_mask,
+        max_new_tokens=max_new_tokens,
+        num_return_sequences=1,
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.eos_token_id,
+        do_sample=False,
+        repetition_penalty=2.0
+    )
+
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+
+    if prompt in generated_text:
+        generated_text = generated_text.split(prompt, 1)[1]
+
+    match = re.search(r'\b(\d+)\b', generated_text)
+    if match:
+        answer = match.group(1)
+    else:
+        answer = generated_text.strip()
+
+    return answer
+
+# Gradio UI
+def gradio_interface(prompt):
+    return generate_single_answer(prompt)
+
+iface = gr.Interface(fn=gradio_interface, inputs="text", outputs="text", title="Emoji Math Solver", description="Enter an emoji math problem to get the answer.")
+iface.launch()