Spaces:
Sleeping
Sleeping
File size: 2,082 Bytes
d3bc923 7c9a8a2 9e6c667 d3bc923 44188f8 76b74a3 44188f8 d3bc923 11dae3a d3bc923 3ac7ed9 d3bc923 3ac7ed9 d3bc923 76b74a3 d3bc923 ba1fa51 d3bc923 ea0e63e d3bc923 |
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 |
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
maxshift = 4
def train(data: str, message: str):
if "→" not in data or "\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:
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)
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() |