File size: 2,825 Bytes
800491f | 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 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | import numpy as np
import json
import gradio as gr
# === LOAD CORPUS & DATASET ===
with open("corpus.txt", "r", encoding="utf-8") as f:
corpus = f.read().splitlines()
with open("dataset.json", "r", encoding="utf-8") as f:
qa_data = json.load(f)
# === BUILD VOCAB ===
all_texts = corpus + list(qa_data.keys()) + list(qa_data.values())
vocab = list(set(" ".join(all_texts).split()))
word2id = {w: i for i, w in enumerate(vocab)}
id2word = {i: w for w, i in word2id.items()}
vocab_size = len(vocab)
def one_hot(word):
vec = np.zeros(vocab_size)
if word in word2id:
vec[word2id[word]] = 1
return vec
# === PARAMETER MODEL ===
np.random.seed(42)
hidden_size = 512 # bisa diubah
W1 = np.random.randn(vocab_size, hidden_size) * 0.01
W2 = np.random.randn(hidden_size, vocab_size) * 0.01
lr = 0.05
def softmax(x):
e = np.exp(x - np.max(x))
return e / e.sum()
def train_step(sentence):
global W1, W2
words = sentence.split()
loss = 0
for i in range(len(words)-1):
x = one_hot(words[i])
y_true = one_hot(words[i+1])
h = np.dot(x, W1)
o = np.dot(h, W2)
y_pred = softmax(o)
loss += np.mean((y_true - y_pred)**2)
grad_o = y_pred - y_true
dW2 = np.outer(h, grad_o)
dW1 = np.outer(x, np.dot(W2, grad_o))
W1 -= lr * dW1
W2 -= lr * dW2
return loss
# === PRETRAIN DENGAN CORPUS ===
for epoch in range(200):
total_loss = 0
for line in corpus:
total_loss += train_step(line + " <END>")
if epoch % 50 == 0:
print(f"Pretrain Epoch {epoch}, Loss: {total_loss:.4f}")
# === FINE-TUNE DENGAN Q&A ===
for epoch in range(200):
total_loss = 0
for q, a in qa_data.items():
total_loss += train_step(q + " " + a)
if epoch % 50 == 0:
print(f"Finetune Epoch {epoch}, Loss: {total_loss:.4f}")
# === GENERATE JAWABAN ===
def generate_reply(question, max_len=30):
words = question.split()
if words[0] not in word2id:
return "Maaf, aku belum ngerti kata itu 🥺"
x = one_hot(words[0])
reply = []
for _ in range(max_len):
h = np.dot(x, W1)
o = np.dot(h, W2)
y_pred = softmax(o)
pred_id = np.argmax(y_pred) # argmax biar konsisten
pred_word = id2word[pred_id]
if pred_word == "<END>":
break
reply.append(pred_word)
x = one_hot(pred_word)
return " ".join(reply)
# === GRADIO INTERFACE ===
def chatbot(input_text):
return generate_reply(input_text)
demo = gr.Interface(fn=chatbot,
inputs="text",
outputs="text",
title="Chatbot Numpy ala Cici 🤭",
description="Mini chatbot dengan training 2 tahap: corpus + Q&A")
if __name__ == "__main__":
demo.launch() |