Upload 2 files

app.py

@@ -0,0 +1,64 @@
+import os, json, importlib.util, torch
+import torch.nn.functional as F
+import gradio as gr
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+from transformers import AutoTokenizer
+
+# ===== ปรับได้ผ่าน Settings > Variables (Environment) =====
+REPO_ID = os.getenv("REPO_ID", "Dusit-P/thai-sentiment-wcb")
+DEFAULT_MODEL = os.getenv("DEFAULT_MODEL", "cnn_bilstm")  # หรือ "baseline"
+HF_TOKEN = os.getenv("HF_TOKEN", None)  # ถ้าโมเดลเป็น private ให้เพิ่ม secret ชื่อนี้
+
+CACHE = {}
+
+def _import_models():
+    if "models_module" in CACHE:
+        return CACHE["models_module"]
+    models_py = hf_hub_download(REPO_ID, filename="common/models.py", token=HF_TOKEN)
+    spec = importlib.util.spec_from_file_location("models", models_py)
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    CACHE["models_module"] = mod
+    return mod
+
+def load_model(model_name: str):
+    key = f"model:{model_name}"
+    if key in CACHE:
+        return CACHE[key]
+    cfg_path = hf_hub_download(REPO_ID, filename=f"{model_name}/config.json", token=HF_TOKEN)
+    w_path   = hf_hub_download(REPO_ID, filename=f"{model_name}/model.safetensors", token=HF_TOKEN)
+    with open(cfg_path, "r", encoding="utf-8") as f:
+        cfg = json.load(f)
+    models = _import_models()
+    tok = AutoTokenizer.from_pretrained(cfg["base_model"])
+    model = models.create_model_by_name(cfg["arch"])
+    state = load_file(w_path)
+    model.load_state_dict(state, strict=True)
+    model.eval()
+    CACHE[key] = (model, tok, cfg)
+    return CACHE[key]
+
+def predict_api(text: str, model_choice: str):
+    if not text.strip():
+        return {"negative": 0.0, "positive": 0.0}, ""
+    model_name = "baseline" if model_choice == "baseline" else "cnn_bilstm"
+    model, tok, cfg = load_model(model_name)
+    enc = tok([text], padding=True, truncation=True, max_length=cfg["max_len"], return_tensors="pt")
+    with torch.no_grad():
+        logits = model(enc["input_ids"], enc["attention_mask"])
+        probs = F.softmax(logits, dim=1)[0].tolist()
+    out = {"negative": float(probs[0]), "positive": float(probs[1])}
+    label = "positive" if out["positive"] >= out["negative"] else "negative"
+    return out, label
+
+with gr.Blocks(title="Thai Sentiment API (Dusit-P)") as demo:
+    gr.Markdown("### Thai Sentiment (WangchanBERTa + LSTM Heads)")
+    inp_text  = gr.Textbox(lines=3, label="ข้อความรีวิวภาษาไทย", placeholder="พิมพ์รีวิวที่นี่")
+    inp_model = gr.Radio(choices=["cnn_bilstm","baseline"], value=DEFAULT_MODEL, label="เลือกโมเดล")
+    out_probs = gr.Label(label="Probabilities")
+    out_label = gr.Textbox(label="Prediction", interactive=False)
+    gr.Button("Predict").click(predict_api, [inp_text, inp_model], [out_probs, out_label])
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,5 @@
+torch
+transformers
+safetensors
+gradio
+huggingface_hub