app.py

import nltk
import numpy as np
from nltk.stem.porter import PorterStemmer
import json
import torch
import torch.nn as nn
import random
import gradio as gr

nltk.download('punkt')
nltk.download('punkt_tab')

with open('dataset.json', 'r') as file:
    dataset = json.load(file)

class NeuralNetwork(nn.Module):
  def __init__(self, input_size, hidden_size, num_classes):
    super(NeuralNetwork, self).__init__()
    self.l1 = nn.Linear(input_size, hidden_size)
    self.l2 = nn.Linear(hidden_size, hidden_size)
    self.l3 = nn.Linear(hidden_size, num_classes)
    self.relu = nn.ReLU()

  def forward(self, x):
    out = self.l1(x)
    out = self.relu(out)
    out = self.l2(out)
    out = self.relu(out)
    out = self.l3(out)
    return out

data = torch.load('model_chatbot.pth', map_location=torch.device('cpu'))
input_size = data["input_size"]
hidden_size = data["hidden_size"]
output_size = data["output_size"]
all_words = data["all_words"]
tags = data["tags"]
model_state = data["model_state"]

device = torch.device('cpu')
model = NeuralNetwork(input_size, hidden_size, output_size).to(device)
model.load_state_dict(model_state)
model.eval()

# Fungsi untuk tokenisasi, stemming, dan bag-of-words
stemmer = PorterStemmer()

def tokenize(sentence):
    return nltk.word_tokenize(sentence)

def stem(word):
    return stemmer.stem(word.lower())

def bag_of_words(tokenized_sentence, all_words):
    tokenized_sentence = [stem(w) for w in tokenized_sentence]
    bag = np.zeros(len(all_words), dtype=np.float32)
    for idx, w in enumerate(all_words):
        if w in tokenized_sentence:
            bag[idx] = 1.0

    return bag

# Fungsi prediksi
def predict(sentence):
    sentence = tokenize(sentence)
    X = bag_of_words(sentence, all_words)
    X = X.reshape(1, X.shape[0])
    X = torch.from_numpy(X).to(device)

    output = model(X)
    _, predicted = torch.max(output, dim=1)
    tag = tags[predicted.item()]

    probs = torch.softmax(output, dim=1)
    prob = probs[0][predicted.item()]

    if prob.item() > 0.75:
        for intent in dataset['intents']:
            if tag == intent["tag"]:
                return random.choice(intent['response'])
    else:
        return "Saya tidak mengerti pertanyaan Anda."

# Buat interface Gradio
iface = gr.Interface(
    fn=predict, 
    inputs=gr.Textbox(placeholder="Silahkan masukkan pertanyaan...."), 
    outputs="markdown", 
    title="Chatbot Hukum Pajak",
    description="Selamat Datang di chatbot pajak.\n\nChatbot ini bisa hanya bisa menjawab beberapa pertanyaan dasar tentang hukum pajak dari pasal 21, pasal 22, pasal 23, dan pasal 24.\n\nContoh pertanyaan seperti berikut:\n\n1. Apa itu pajak?\n\n2. Apa itu pajak penghasilan?\n\n3. Bisakah kamu jelaskan apa itu pajak penghasilan berdasarkan pasal 22?\n\n4. dan lain-lain.\n\nJika ingin melihat bagaimana chatbot ini dilatih, kalian bisa melihat dataset sederhana yang saya buat:\n\nhttps://huggingface.co/chandra10/chatbot_pajak/resolve/main/dataset.json\n\nModel chatbot ini belum sempurna. Jadi, silahkan beri saran di community")

# Jalankan interface
iface.launch(share=True)