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app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+MODEL_NAME = "vinai/phobert-base-v2"
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, num_labels=3)
+model.load_state_dict(torch.load("best_model_state.bin", map_location=device))
+model.to(device)
+model.eval()
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+label_map = {0: "An toàn", 1: "Tiêu cực", 2: "Nguy cơ bạo lực"}
+
+def predict(text):
+    inputs = tokenizer.encode_plus(
+        text.lower(), return_tensors="pt", max_length=128, padding="max_length", truncation=True
+    )
+    input_ids = inputs["input_ids"].to(device)
+    attention_mask = inputs["attention_mask"].to(device)
+
+    with torch.no_grad():
+        outputs = model(input_ids=input_ids, attention_mask=attention_mask)
+        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        pred = torch.argmax(probs, dim=1).item()
+
+    return f"{label_map[pred]} (độ tin cậy: {probs[0][pred]:.2f})"
+
+demo = gr.Interface(fn=predict, inputs="text", outputs="text", title="PhoBERT - Phân tích cảm xúc học sinh")
+demo.launch()

best_model_state.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:40fa85d61d0837ebd176638652e12fcc74562c07d30f9cdb558c29b591950415
+size 540087061

requirements.txt

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+torch
+transformers
+gradio