# app.py
import os, shutil, subprocess, zipfile
from pathlib import Path
import gradio as gr

ROOT   = Path(_file_).resolve().parent           # /home/user/app
DATA   = ROOT / "dataset.jsonl"
LOG    = ROOT / "train.log"
OUT    = ROOT / "trained_model"
ZIP    = ROOT / "trained_model.zip"

def ls_workspace() -> str:
    rows = []
    for p in sorted(ROOT.iterdir(), key=lambda x: (x.is_file(), x.name.lower())):
        try: size = p.stat().st_size
        except Exception: size = 0
        rows.append(f"{'[DIR]' if p.is_dir() else '     '}\t{size:>10}\t{p.name}")
    return "\n".join(rows) or "(empty)"

def upload_dataset(file):
    if not file:
        return "❌ No file selected.", ls_workspace()
    shutil.copy(file.name, DATA)
    return f"✅ Uploaded → {DATA.name}", ls_workspace()

def start_training():
    # clean previous artifacts
    if OUT.exists(): shutil.rmtree(OUT, ignore_errors=True)
    if ZIP.exists(): ZIP.unlink(missing_ok=True)
    LOG.write_text("🔥 Training started…\n", encoding="utf-8")

    cmd = [
        "python", str(ROOT / "train.py"),
        "--dataset", str(DATA),
        "--output",  str(OUT),
        "--zip_path", str(ZIP),
        "--model_name", "Salesforce/codegen-350M-multi",
        "--epochs", "1",
        "--batch_size", "2",
        "--block_size", "256",
        "--learning_rate", "5e-5",
    ]
    # run training (blocking) and capture logs
    with open(LOG, "a", encoding="utf-8") as lf:
        code = subprocess.Popen(cmd, stdout=lf, stderr=subprocess.STDOUT).wait()

    # after process exits, show result
    if code == 0 and ZIP.exists():
        info = f"✅ Training complete. Saved: {OUT.name} | Zip: {ZIP.name}"
        return info, gr.File.update(value=str(ZIP), visible=True), ls_workspace(), read_logs()
    else:
        info = f"❌ Training failed (exit {code}). See logs."
        return info, gr.File.update(visible=False), ls_workspace(), read_logs()

def read_logs():
    return LOG.read_text(encoding="utf-8")[-20000:] if LOG.exists() else "⏳ Waiting…"

def refresh_download():
    return gr.File.update(value=str(ZIP), visible=ZIP.exists()), ls_workspace()

# ---------------- Test tab ----------------
def list_models():
    out = []
    for p in ROOT.iterdir():
        if p.is_dir() and (p / "config.json").exists() and (
            (p / "tokenizer.json").exists() or (p / "tokenizer_config.json").exists()
        ):
            out.append(str(p))
    if OUT.exists() and str(OUT) not in out:
        out.insert(0, str(OUT))
    return sorted(out)

def import_zip(zfile):
    if not zfile:
        return "❌ No zip selected.", list_models()
    dest = ROOT / f"imported_model"
    if dest.exists(): shutil.rmtree(dest, ignore_errors=True)
    dest.mkdir(parents=True, exist_ok=True)
    with zipfile.ZipFile(zfile.name, "r") as z:
        z.extractall(dest)
    return f"✅ Imported to {dest.name}", list_models()

def generate(model_path, prompt):
    if not model_path: return "❌ Select a model."
    if not prompt or not prompt.strip(): return "❌ Enter a prompt."
    try:
        from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
        tok = AutoTokenizer.from_pretrained(model_path, use_fast=True)
        if tok.pad_token_id is None and tok.eos_token_id is not None:
            tok.pad_token = tok.eos_token
        model = AutoModelForCausalLM.from_pretrained(model_path)
        pipe = pipeline("text-generation", model=model, tokenizer=tok)
        out = pipe(
            prompt, max_new_tokens=220, do_sample=True, temperature=0.2, top_p=0.9,
            repetition_penalty=1.2, no_repeat_ngram_size=4,
            eos_token_id=tok.eos_token_id, pad_token_id=tok.pad_token_id, truncation=True
        )[0]["generated_text"]
        return out
    except Exception as e:
        return f"❌ Error: {e}"

with gr.Blocks(title="Python AI — Train & Test") as app:
    gr.Markdown("## 🧠 Python AI — Train & Test (simple + reliable)\nTrainer zips the model itself. UI just shows the zip.\n")

    with gr.Tab("Train"):
        with gr.Row():
            ds = gr.File(label="📥 Upload JSONL", file_types=[".jsonl", ".jsonl.gz", ".json"])
            ws = gr.Textbox(label="Workspace", lines=16, value=ls_workspace())
        up_status = gr.Textbox(label="Upload Status", interactive=False)
        start = gr.Button("🚀 Start Training", variant="primary")
        logs = gr.Textbox(label="📜 Training Logs", lines=18, value=read_logs())
        status = gr.Textbox(label="Status", interactive=False)
        download_file = gr.File(label="📦 trained_model.zip", visible=ZIP.exists())
        refresh_dl_btn = gr.Button("Refresh Download")

        ds.change(upload_dataset, inputs=ds, outputs=[up_status, ws])
        start.click(start_training, outputs=[status, download_file, ws, logs])
        refresh_dl_btn.click(refresh_download, outputs=[download_file, ws])

    with gr.Tab("Test"):
        gr.Markdown("### Choose a model folder or upload a .zip, then prompt it")
        refresh_btn = gr.Button("↻ Refresh Model List")
        model_list = gr.Dropdown(choices=list_models(), label="Available AIs", interactive=True)
        zip_in = gr.File(label="Or upload a model .zip", file_types=[".zip"])
        import_status = gr.Textbox(label="Import Status", interactive=False)
        prompt = gr.Textbox(label="Prompt", lines=8, placeholder="### Instruction:\nPython: write a function ...\n### Response:\n")
        go = gr.Button("Generate")
        out = gr.Textbox(label="AI Response", lines=20)

        refresh_btn.click(list_models, outputs=model_list)
        zip_in.change(import_zip, inputs=zip_in, outputs=[import_status, model_list])
        go.click(generate, inputs=[model_list, prompt], outputs=out)

app.launch()