Create app.py

app.py

@@ -0,0 +1,104 @@
+from pathlib import Path
+from functools import partial
+
+from joeynmt.prediction import predict
+from joeynmt.helpers import (
+    check_version,
+    load_checkpoint,
+    load_config,
+    parse_train_args,
+    resolve_ckpt_path,
+
+)
+from joeynmt.model import build_model
+from joeynmt.tokenizers import build_tokenizer
+from joeynmt.vocabulary import build_vocab
+from joeynmt.datasets import build_dataset
+
+import gradio as gr
+
+# INPUT = "سلاو لە ناو گلی کرد"
+
+cfg_file = 'config.yaml'
+ckpt = './models/Sorani-Arabic/best.ckpt'
+
+cfg = load_config(Path(cfg_file))
+    # parse and validate cfg
+model_dir, load_model, device, n_gpu, num_workers, _, fp16 = parse_train_args(
+    cfg["training"], mode="prediction")
+test_cfg = cfg["testing"]
+src_cfg = cfg["data"]["src"]
+trg_cfg = cfg["data"]["trg"]
+
+load_model = load_model if ckpt is None else Path(ckpt)
+ckpt = resolve_ckpt_path(load_model, model_dir)
+
+src_vocab, trg_vocab = build_vocab(cfg["data"], model_dir=model_dir)
+
+model = build_model(cfg["model"], src_vocab=src_vocab, trg_vocab=trg_vocab)
+
+# load model state from disk
+model_checkpoint = load_checkpoint(ckpt, device=device)
+model.load_state_dict(model_checkpoint["model_state"])
+
+if device.type == "cuda":
+    model.to(device)
+
+tokenizer = build_tokenizer(cfg["data"])
+sequence_encoder = {
+    src_cfg["lang"]: partial(src_vocab.sentences_to_ids, bos=False, eos=True),
+    trg_cfg["lang"]: None,
+}
+
+test_cfg["batch_size"] = 1  # CAUTION: this will raise an error if n_gpus > 1
+test_cfg["batch_type"] = "sentence"
+
+test_data = build_dataset(
+    dataset_type="stream",
+    path=None,
+    src_lang=src_cfg["lang"],
+    trg_lang=trg_cfg["lang"],
+    split="test",
+    tokenizer=tokenizer,
+    sequence_encoder=sequence_encoder,
+)
+# test_data.set_item(INPUT.rstrip())
+
+
+def _translate_data(test_data, cfg=test_cfg):
+    """Translates given dataset, using parameters from outer scope."""
+    _, _, hypotheses, trg_tokens, trg_scores, _ = predict(
+        model=model,
+        data=test_data,
+        compute_loss=False,
+        device=device,
+        n_gpu=n_gpu,
+        normalization="none",
+        num_workers=num_workers,
+        cfg=cfg,
+        fp16=fp16,
+    )
+    return hypotheses[0]
+
+
+
+def normalize(text):
+    test_data.set_item(text)
+    result = _translate_data(test_data)
+    return result
+
+examples = [
+    ["ياخوا تةمةن دريژبيت بوئةم ميللةتة"],
+    ["سلاو برا جونی؟"],
+]
+
+
+
+demo = gr.Interface(
+    fn=normalize,
+    inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
+    outputs=gr.outputs.Textbox(label="Output Text" ),
+    examples=examples
+)
+
+demo.launch()