story_generator.py

import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel

# ✅ Define cache directory
CACHE_DIR = "/tmp/huggingface"

# ✅ Load Story Generation Model
STORY_MODEL_NAME = "abdalraheemdmd/story-api"
story_tokenizer = GPT2Tokenizer.from_pretrained(STORY_MODEL_NAME, cache_dir=CACHE_DIR)
story_model = GPT2LMHeadModel.from_pretrained(STORY_MODEL_NAME, cache_dir=CACHE_DIR)

# ✅ Load Question Generation Model
QUESTION_MODEL_NAME = "abdalraheemdmd/question-gene"
question_tokenizer = GPT2Tokenizer.from_pretrained(QUESTION_MODEL_NAME, cache_dir=CACHE_DIR)
question_model = GPT2LMHeadModel.from_pretrained(QUESTION_MODEL_NAME, cache_dir=CACHE_DIR)

# ✅ Ensure tokenizer has a pad token
if story_tokenizer.pad_token_id is None:
    story_tokenizer.pad_token_id = story_tokenizer.eos_token_id

if question_tokenizer.pad_token_id is None:
    question_tokenizer.pad_token_id = question_tokenizer.eos_token_id

def generate_story(theme, reading_level, max_new_tokens=400, temperature=0.7):
    """⚡ Ultra-fast, high-quality story generation."""
    prompt = f"A {reading_level} story about {theme}:"
    input_ids = story_tokenizer(prompt, return_tensors="pt").input_ids

    with torch.no_grad():
        output = story_model.generate(
            input_ids,
            max_new_tokens=max_new_tokens,
            temperature=temperature,
            top_k=20,
            top_p=0.7,
            do_sample=True,
            early_stopping=True,
            pad_token_id=story_tokenizer.pad_token_id,
            eos_token_id=story_tokenizer.eos_token_id,
            attention_mask=input_ids.ne(story_tokenizer.pad_token_id)
        )

    return story_tokenizer.decode(output[0], skip_special_tokens=True)

def generate_questions(story, max_new_tokens=150, temperature=0.7):
    """⚡ Ultra-fast, concise question generation."""
    prompt = f"Generate 3 clear comprehension questions for this short story:\n\n{story}"
    input_ids = question_tokenizer(prompt, return_tensors="pt").input_ids

    with torch.no_grad():
        output = question_model.generate(
            input_ids,
            max_new_tokens=max_new_tokens,
            temperature=temperature,
            top_k=20,
            top_p=0.7,
            do_sample=True,
            early_stopping=True,
            pad_token_id=question_tokenizer.pad_token_id,
            eos_token_id=question_tokenizer.eos_token_id,
            attention_mask=input_ids.ne(question_tokenizer.pad_token_id)
        )

    return question_tokenizer.decode(output[0], skip_special_tokens=True)

def generate_story_and_questions(theme, reading_level):
    """⚡ Generates a story and 3 questions ultra-fast."""
    story = generate_story(theme, reading_level)
    questions = generate_questions(story)
    return {"story": story, "questions": questions}