Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("tuantc/flysugarbot")
+model = AutoModelForSequenceClassification.from_pretrained("tuantc/flysugarbot")
+model.eval()
+
+
+def predict_intent(model, tokenizer, text_instance,label_map):
+    device = torch.device("cpu")
+    encoding = tokenizer(text_instance, return_tensors='pt', padding=True, truncation=True, max_length=512)
+    input_ids = encoding['input_ids'].to(device)
+    attention_masks = encoding['attention_mask'].to(device)
+    with torch.no_grad():
+        outputs = model(input_ids, attention_mask=attention_masks)
+    logits = outputs[0]
+    predictions = torch.argmax(logits, dim=-1).item()
+    predicted_label = label_map[predictions]
+    prob = F.softmax(logits, dim=1).max().item()
+    return predicted_label, prob
+def bot_response(message, history):
+    predicted_intent, prob = predict_intent(model=model, tokenizer=tokenizer, text_instance=message,label_map=label_map)
+    return f"Intent này là {predicted_intent} với confidence {prob:.2f}"
+demo = gr.ChatInterface(random_response)
+
+
+gr.Interface(
+    fn=bot_response,
+    inputs=["text", "state"],
+    outputs=["chatbot", "state"],
+    theme="finlaymacklon/boxy_violet",
+).launch()