import gradio as gr import numpy as np from keras.models import Model from keras.saving import load_model from keras.layers import * from keras.regularizers import L1 from keras.constraints import Constraint from tensorflow.keras.optimizers import RMSprop from keras.preprocessing.text import Tokenizer import keras.backend as K import os import hashlib import keras os.mkdir("cache") def todset(text: str): lines = [x.rstrip("\n").lower().split("→") for x in text.split("\n")] lines = [(x[0].replace("\\n", "\n"), x[1].replace("\\n", "\n")) for x in lines] responses = [] for i in lines: if i[1] not in responses: responses.append(i[1]) dset = {} for sample in lines: dset[sample[0]] = responses.index(sample[1]) return (dset, responses) def hash_str(data: str): return hashlib.md5(data.encode('utf-8')).hexdigest() def train(message: str = "", regularization: float = 0.0001, dropout: float = 0.1, learning_rate: float = 0.001, epochs: int = 16, emb_size: int = 100, input_len: int = 16, kernels_count: int = 64, kernel_size: int = 4, left_padding: bool = False, end_activation: str = "softmax", data: str = ""): data_hash = None if "→" not in data or "\n" not in data: if data in os.listdir("cache"): # data = filename data_hash = data # set the hash to the file name else: return "Data 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 inp_len = input_len if data_hash is None: if end_activation is not None: data_hash = hash_str(data)+"_"+str(regularization)+"_"+str(dropout)+"_"+str(learning_rate)+"_"+str(epochs)+"_"+str(emb_size)+"_"+str(inp_len)+"_"+str(kernels_count)+"_"+str(kernel_size)+"_"+str(left_padding)+"_"+end_activation+".keras" else: data_hash = hash_str(data)+"_"+str(regularization)+"_"+str(dropout)+"_"+str(learning_rate)+"_"+str(epochs)+"_"+str(emb_size)+"_"+str(inp_len)+"_"+str(kernels_count)+"_"+str(kernel_size)+"_"+str(left_padding)+".keras" if message == "!getmodelhash": return data_hash else: inp_len = int(data_hash.split("_")[-3]) if data_hash in os.listdir("cache"): model = load_model("cache/"+data_hash) else: input_layer = Input(shape=(inp_len,)) emb_layer = Embedding(input_dim=vocab_size, output_dim=emb_size, input_length=inp_len)(input_layer) dropout1_layer = Dropout(dropout)(emb_layer) attn_layer = MultiHeadAttention(num_heads=4, key_dim=128)(dropout1_layer, dropout1_layer, dropout1_layer) noise_layer = GaussianNoise(0.1)(attn_layer) conv1_layer = Conv1D(kernels_count, kernel_size, padding='same', activation='relu', strides=1, input_shape=(64, 128), kernel_regularizer=L1(regularization))(noise_layer) conv2_layer = Conv1D(16, 4, padding='same', activation='relu', strides=1, kernel_regularizer=L1(regularization))(conv1_layer) conv3_layer = Conv1D(8, 2, padding='same', activation='relu', strides=1, kernel_regularizer=L1(regularization))(conv2_layer) flatten_layer = Flatten()(conv3_layer) attn_flatten_layer = Flatten()(attn_layer) conv1_flatten_layer = Flatten()(conv1_layer) conv2_flatten_layer = Flatten()(conv2_layer) conv3_flatten_layer = Flatten()(conv3_layer) concat1_layer = Concatenate()([flatten_layer, attn_flatten_layer, conv1_flatten_layer, conv2_flatten_layer, conv3_flatten_layer]) dropout2_layer = Dropout(dropout)(concat1_layer) dense1_layer = Dense(1024, activation="linear", kernel_regularizer=L1(regularization))(dropout2_layer) prelu1_layer = PReLU()(dense1_layer) dropout3_layer = Dropout(dropout)(prelu1_layer) dense2_layer = Dense(512, activation="relu", kernel_regularizer=L1(regularization))(dropout3_layer) dropout4_layer = Dropout(dropout)(dense2_layer) dense3_layer = Dense(512, activation="relu", kernel_regularizer=L1(regularization))(dropout4_layer) dropout5_layer = Dropout(dropout)(dense3_layer) dense4_layer = Dense(256, activation="relu", kernel_regularizer=L1(regularization))(dropout5_layer) concat2_layer = Concatenate()([dense4_layer, prelu1_layer, attn_flatten_layer, conv1_flatten_layer]) if end_activation is not None: dense4_layer = Dense(resps_len, activation=end_activation, kernel_regularizer=L1(regularization))(concat2_layer) else: dense4_layer = Dense(resps_len, activation="softmax", kernel_regularizer=L1(regularization))(concat2_layer) model = Model(inputs=input_layer, outputs=dense4_layer) X = [] y = [] if left_padding: for key in dset: tokens = tokenizer.texts_to_sequences([key,])[0] X.append(np.array(([0,]*inp_len+list(tokens))[-inp_len:])) y.append(dset[key]) else: for key in dset: tokens = tokenizer.texts_to_sequences([key,])[0] X.append(np.array((list(tokens)+[0,]*inp_len)[:inp_len])) y.append(dset[key]) X = np.array(X) y = np.array(y) model.compile(optimizer=RMSprop(learning_rate=learning_rate), loss="sparse_categorical_crossentropy", metrics=["accuracy",]) model.fit(X, y, epochs=epochs, batch_size=8, workers=4, use_multiprocessing=True) model.save(f"cache/{data_hash}") tokens = tokenizer.texts_to_sequences([message,])[0] prediction = model.predict(np.array([(list(tokens)+[0,]*inp_len)[:inp_len],]))[0] K.clear_session() return responses[np.argmax(prediction)] if __name__ == "__main__": iface = gr.Interface(fn=train, inputs=["text", gr.components.Slider(0, 0.01, value=0.0001, step=1e-8, label="Regularization L1"), gr.components.Slider(0, 0.5, value=0.1, step=1e-8, label="Dropout"), gr.components.Slider(1e-8, 0.01, value=0.001, step=1e-8, label="Learning rate"), gr.components.Slider(1, 128, value=16, step=1, label="Epochs"), gr.components.Slider(1, 256, value=88, step=1, label="Embedding size"), gr.components.Slider(1, 128, value=16, step=1, label="Input Length"), gr.components.Slider(1, 128, value=64, step=1, label="Convolution kernel count"), gr.components.Slider(1, 16, value=2, step=1, label="Convolution kernel size"), gr.components.Checkbox(False, label="Use left padding"), gr.components.Radio(['softmax', 'sigmoid', 'linear', 'softplus', 'exponential', 'log_softmax'], label="Output activation function"), "text"], outputs="text") iface.launch()