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=train, inputs=["text", "text"], outputs="text") iface.launch()