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import gradio as gr
from todset import todset
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
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)
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(dset_size, 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(dset_size)
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=greet, inputs=["text", "text"], outputs="text")
iface.launch() |