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import gradio as gr | |
from todset import todset | |
import numpy as np | |
from keras.models import Sequential | |
from keras.layers import Embedding, Dense, Dropout, Flatten, PReLU | |
from keras.preprocessing.text import Tokenizer | |
from keras_self_attention import SeqSelfAttention, SeqWeightedAttention | |
emb_size = 128 | |
inp_len = 16 | |
def train(data: str, message: str): | |
if "→" not in data and "\n" not in data: | |
return "Dataset should be like:\nquestion→answer\nquestion→answer\netc." | |
dset, responses = todset(data) | |
resps_len = len(responses) | |
tokenizer = Tokenizer() | |
tokenizer.fit_on_texts(list(dset.keys())) | |
vocab_size = len(tokenizer.word_index) + 1 | |
model = Sequential() | |
model.add(Embedding(input_dim=vocab_size, output_dim=emb_size, input_length=inp_len)) | |
model.add(SeqSelfAttention()) | |
model.add(Flatten()) | |
model.add(Dense(1024, activation="relu")) | |
model.add(Dropout(0.5)) | |
model.add(Dense(512, activation="relu")) | |
model.add(Dense(512, activation="relu")) | |
model.add(Dense(256, activation="relu")) | |
model.add(Dense(resps_len, activation="softmax")) | |
X = [] | |
y = [] | |
for key in dset: | |
tokens = tokenizer.texts_to_sequences([key,])[0] | |
X.append(np.array((list(tokens)+[0,]*inp_len)[:inp_len])) | |
output_array = np.zeros(resps_len) | |
output_array[dset[key]] = 1 | |
y.append(output_array) | |
X = np.array(X) | |
y = np.array(y) | |
model.compile(loss="categorical_crossentropy", metrics=["accuracy",]) | |
model.fit(X, y, epochs=10, batch_size=8, workers=4, use_multiprocessing=True) | |
tokens = tokenizer.texts_to_sequences([message,])[0] | |
prediction = model.predict(np.array((list(tokens)+[0,]*inp_len)[:inp_len])) | |
max_o = 0 | |
max_v = 0 | |
for ind, i in enumerate(prediction): | |
if max_v < i: | |
max_v = i | |
max_o = ind | |
return responses[ind] | |
iface = gr.Interface(fn=train, inputs=["text", "text"], outputs="text") | |
iface.launch() |