Delete app.py

app.py

@@ -1,82 +0,0 @@
-from pathlib import Path
-import gradio as gr
-import torch
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-ROOT = Path(__file__).parent
-
-# Your repo has: t5_enzh/checkpoint-50000/...
-MODEL_ROOT = ROOT / "t5_enzh"
-
-def latest_checkpoint(root: Path) -> Path | None:
-    if not root.exists():
-        return None
-    ckpts = [p for p in root.iterdir() if p.is_dir() and p.name.startswith("checkpoint-")]
-    if not ckpts:
-        # maybe you uploaded final model directly into t5_enzh
-        if (root / "config.json").exists():
-            return root
-        return None
-    # pick biggest step number
-    ckpts.sort(key=lambda p: int(p.name.split("-")[-1]))
-    return ckpts[-1]
-
-CKPT = latest_checkpoint(MODEL_ROOT)
-
-_pipe = {"tok": None, "model": None}
-
-def model_ready():
-    return CKPT is not None
-
-def get_model():
-    if _pipe["model"] is None:
-        tok = AutoTokenizer.from_pretrained(CKPT, use_fast=False)
-        model = AutoModelForSeq2SeqLM.from_pretrained(CKPT)
-        if torch.cuda.is_available():
-            model = model.to("cuda")
-        model.eval()
-        _pipe["tok"] = tok
-        _pipe["model"] = model
-    return _pipe["tok"], _pipe["model"]
-
-def translate_text(text: str):
-    text = (text or "").strip()
-    if not text:
-        return ""
-    if not model_ready():
-        return "[Model not ready yet — upload/checkpoint missing.]"
-
-    tok, model = get_model()
-
-    prompt = "translate English to Chinese: " + text  # ✅ key fix
-
-    inputs = tok(prompt, return_tensors="pt", truncation=True, max_length=256)
-    if torch.cuda.is_available():
-        inputs = {k: v.to("cuda") for k, v in inputs.items()}
-
-    with torch.no_grad():
-        out_ids = model.generate(
-            **inputs,
-            max_new_tokens=80,
-            num_beams=4,
-            early_stopping=True,
-        )
-
-    result = tok.decode(out_ids[0], skip_special_tokens=True).strip()
-    return result if result else "[No output — check prompt prefix / checkpoint quality]"
-
-
-title = "EN → ZH Translator"
-status = "✅ Model ready" if model_ready() else "⏳ Model not ready"
-
-with gr.Blocks() as demo:
-    gr.Markdown(f"# {title}")
-    gr.Markdown(f"**Status:** {status}")
-    gr.Markdown(f"**Loaded path:** `{str(CKPT) if CKPT else 'None'}`")
-
-    inp = gr.Textbox(label="English", lines=4, placeholder="Type English here...")
-    out = gr.Textbox(label="Chinese", lines=4)
-    btn = gr.Button("Translate")
-    btn.click(translate_text, inp, out)
-
-demo.launch()