app.py

import gc, json, torch, gradio as gr
from huggingface_hub import hf_hub_download
import tiktoken

from mingpt.model import GPT 

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
REPO_ID = "to0ony/final-thesis-plotgen"

state = {"model": None, "enc": tiktoken.get_encoding("gpt2")}

def load_model():
    """Lazy-load model iz Hugging Face repozitorija"""
    if state["model"] is not None:
        return state["model"]

    # skinuti config i model.pt
    cfg_path = hf_hub_download(repo_id=REPO_ID, filename="config.json")
    mdl_path = hf_hub_download(repo_id=REPO_ID, filename="model.pt")

    # učitati config
    with open(cfg_path, "r", encoding="utf-8") as f:
        cfg = json.load(f)

    gcfg = GPT.get_default_config()
    gcfg.vocab_size = cfg["vocab_size"]
    gcfg.block_size = cfg["block_size"]
    gcfg.n_layer = cfg["n_layer"]
    gcfg.n_head = cfg["n_head"]
    gcfg.n_embd = cfg["n_embd"]

    model = GPT(gcfg)
    sd = torch.load(mdl_path, map_location="cpu")
    model.load_state_dict(sd, strict=True)
    model.to(DEVICE)
    model.eval()

    state["model"] = model
    return model

@torch.inference_mode()
def generate(prompt, max_new_tokens=200, temperature=0.9, top_k=50):
    """Generiranje teksta iz prompta"""
    model = load_model()
    enc = state["enc"]

    x = torch.tensor([enc.encode(prompt)], dtype=torch.long, device=DEVICE)

    y = model.generate(
        x,
        max_new_tokens=int(max_new_tokens),
        temperature=float(temperature),
        top_k=int(top_k) if top_k > 0 else None
    )

    return enc.decode(y[0].tolist())

# Gradio UI
with gr.Blocks(title="🎬 Final Thesis Plot Generator") as demo:
    gr.Markdown("## 🎬 Film Plot Generator\nUnesi prompt i generiraj radnju filma.")

    prompt = gr.Textbox(label="Prompt", lines=5, placeholder="E.g. A young detective arrives in a coastal town...")
    max_new_tokens = gr.Slider(32, 512, value=200, step=16, label="Max new tokens")
    temperature = gr.Slider(0.1, 1.5, value=0.9, step=0.1, label="Temperature")
    top_k = gr.Slider(0, 100, value=50, step=5, label="Top-K (0 = off)")
    btn = gr.Button("Generate")
    output = gr.Textbox(label="Output", lines=15)

    btn.click(generate, [prompt, max_new_tokens, temperature, top_k], output)

if __name__ == "__main__":
    demo.launch()