Spaces:
Sleeping
Sleeping
| 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() | |