File size: 2,838 Bytes
6ef4ac0 20935a2 6ef4ac0 20935a2 6ef4ac0 20935a2 6ef4ac0 20935a2 6ef4ac0 20935a2 6ef4ac0 20935a2 6ef4ac0 20935a2 6ef4ac0 8ff9e02 6ef4ac0 20935a2 6ef4ac0 |
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 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
import os
import logging
import random
import pickle
import torch
from transformers import BertForSequenceClassification, BertTokenizerFast
from huggingface_hub import hf_hub_download
import gradio as gr
# Configuration
MODEL_NAME = "vinapatri/intent-classification-jkn-kis"
CONFIDENCE_THRESHOLD = 0.6 # Adjust this based on your model's performance
# --- Model Functions ---
def load_model_components():
model = BertForSequenceClassification.from_pretrained(MODEL_NAME)
tokenizer = BertTokenizerFast.from_pretrained(MODEL_NAME)
label_encoder_path = hf_hub_download(repo_id=MODEL_NAME, filename="label_encoder.pkl")
responses_path = hf_hub_download(repo_id=MODEL_NAME, filename="tag_to_responses.pkl")
with open(label_encoder_path, "rb") as f:
le = pickle.load(f)
with open(responses_path, "rb") as f:
tag_to_responses = pickle.load(f)
# Ensure we have an 'unknown' response category
if 'unknown' not in tag_to_responses:
tag_to_responses['unknown'] = [
"Maaf, saya tidak mengerti pertanyaan Anda",
"Saya belum bisa menjawab pertanyaan tersebut"
]
return model, tokenizer, le, tag_to_responses
model, tokenizer, le, tag_to_responses = load_model_components()
def predict_intent(text):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
inputs = {k: v.to(device) for k, v in inputs.items()}
with torch.no_grad():
outputs = model(**inputs)
# Get confidence scores
probabilities = torch.softmax(outputs.logits, dim=1)[0]
confidence, predicted_class_id = torch.max(probabilities, 0)
confidence = confidence.item()
# Return 'unknown' if confidence is below threshold
if confidence < CONFIDENCE_THRESHOLD:
return "unknown"
tag = le.inverse_transform([predicted_class_id.item()])[0]
return tag
def get_response(user_input):
tag = predict_intent(user_input)
responses = tag_to_responses.get(tag, tag_to_responses['unknown'])
return random.choice(responses)
# --- Gradio Interface ---
def chat_interface(message, history):
return get_response(message)
gradio_app = gr.ChatInterface(
fn=chat_interface,
title="JKN-KIS Intent Classification",
description="Bot untuk klasifikasi intent terkait JKN-KIS",
examples=[
"Bagaimana cara daftar Mobile JKN?",
"Cara buat surat rujukan BPJS secara online?",
"Kenapa OTP Mobile JKN tidak masuk?"
]
)
if __name__ == "__main__":
logging.basicConfig(
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
level=logging.INFO
)
gradio_app.launch(server_name="0.0.0.0", server_port=7860) |