File size: 1,878 Bytes
ab69189 5d801dd ab69189 21c8c01 ab69189 f995d18 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch.nn.functional as F
tokenizer = AutoTokenizer.from_pretrained("tuantc/flysugarbot")
model = AutoModelForSequenceClassification.from_pretrained("tuantc/flysugarbot")
model.eval()
classes = ['admin_hr_avi_hr_lead',
'airbus_avi_hr_lead',
'avi_hr_lead',
'bot_challenge',
'bus_info',
'c99_avi_hr_lead',
'card',
'channel_info',
'congras_general',
'contract',
'doc_email_sw',
'dxg_avi_hr_lead',
'empl_password',
'encourage',
'excited',
'fly_avi_hr_lead',
'general_spam',
'goodbye',
'greeting',
'laptop_pc',
'lover_spam',
'meeting_room',
'on_leave',
'palantir_avi_hr_lead',
'printer',
'property',
'salary_insurance_avi_hr_lead',
'services',
'spam_eating',
'sw_install',
'thoi_viec',
'tms',
'union',
'wifi',
'working_hour']
NUM_LABELS = len(classes)
label_map = {}
for l in range(NUM_LABELS):
label_map[l] = classes[l]
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(bot_response)
demo.launch(share=True)
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