app.py

import gradio as gr
from transformers import pipeline, PipelineException

# ---- Configuration: choose a small model for Free CPU Spaces ----
# If you have more resources you can change this to a larger model,
# but small models (gpt2) are reliable on free CPU.
MODEL_NAME = "gpt2"

def make_generator(model_name=MODEL_NAME):
    try:
        # device=-1 forces CPU (works on Free CPU Spaces)
        generator = pipeline(
            task="text-generation",
            model=model_name,
            device=-1
        )
        return generator
    except Exception as e:
        # Return None on failure and log the error for debugging in Space logs
        print(f"[ERROR] Failed to load model {model_name}: {e}")
        return None

# create pipeline at import time (cached by the Space)
generator = make_generator()

def research(seed: str):
    if not seed or not seed.strip():
        return "Please enter a seed keyword."

    if generator is None:
        return (
            "Model failed to load. Check Space logs or change MODEL_NAME to a small model "
            "(e.g., 'gpt2')."
        )

    prompt = (
        "Generate 100 SEO keywords for: "
        f"{seed.strip()}\n\n"
        "Output a clean numbered list or table with: Keyword | Type | Intent | PriorityScore(0-100)\n"
        "Keep results compact and easy to paste into a spreadsheet."
    )

    try:
        # Use max_new_tokens rather than max_length to avoid token-count confusion.
        # Keep num_return_sequences=1 to save resources.
        out = generator(
            prompt,