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import gradio as gr
from todset import todset
import numpy as np
from keras.models import Model
from keras.saving import load_model
from keras.layers import *
from keras.preprocessing.text import Tokenizer
import os
os.mkdir("cache")
emb_size = 128
inp_len = 16
maxshift = 4
def hash_str(data: str):
return hashlib.md5(data.encode('utf-8')).hexdigest()
def train(data: str, message: str):
if "→" not in data or "\n" not in data:
return "Dataset example:\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
if hash_str(data)+".keras" in os.listdir("cache"):
model = load_model(hash_str(data)+".keras")
else:
input_layer = Input(shape=(inp_len,))
emb_layer = Embedding(input_dim=vocab_size, output_dim=emb_size, input_length=inp_len)(input_layer)
attn_layer = MultiHeadAttention(num_heads=4, key_dim=128)(emb_layer, emb_layer, emb_layer)
noise_layer = GaussianNoise(0.1)(attn_layer)
conv1_layer = Conv1D(64, 8, padding='same', activation='relu', strides=1, input_shape=(64, 128))(noise_layer)
conv2_layer = Conv1D(16, 4, padding='valid', activation='relu', strides=1)(conv1_layer)
conv3_layer = Conv1D(8, 2, padding='valid', activation='relu', strides=1)(conv2_layer)
flatten_layer = Flatten()(conv3_layer)
attn_flatten_layer = Flatten()(attn_layer)
conv1_flatten_layer = Flatten()(conv1_layer)
conv3_flatten_layer = Flatten()(conv3_layer)
concat1_layer = Concatenate()([flatten_layer, attn_flatten_layer, conv1_flatten_layer, conv2_layer, conv3_flatten_layer])
dense1_layer = Dense(512, activation="linear")(concat1_layer)
prelu1_layer = PReLU()(dense1_layer)
dropout_layer = Dropout(0.3)(prelu1_layer)
dense2_layer = Dense(256, activation="tanh")(dropout_layer)
dense3_layer = Dense(256, activation="relu")(dense2_layer)
dense4_layer = Dense(100, activation="tanh")(dense3_layer)
concat2_layer = Concatenate()([dense4_layer, prelu1_layer, attn_flatten_layer, conv1_flatten_layer])
dense4_layer = Dense(resps_len, activation="softmax")(concat2_layer)
X = []
y = []
for key in dset:
for p in range(maxshift):
tokens = tokenizer.texts_to_sequences([key,])[0]
X.append(np.array(([0,]*p+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)
model.save("{data_hash}.keras")
tokens = tokenizer.texts_to_sequences([message,])[0]
prediction = model.predict(np.array([(list(tokens)+[0,]*inp_len)[:inp_len],]))[0]
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() |