| import gradio as gr | |
| from todset import todset | |
| import numpy as np | |
| from keras.models import Model | |
| from keras.saving import load_model | |
| from keras.layers import * | |
| from tensorflow.keras.optimizers import RMSprop | |
| from keras.preprocessing.text import Tokenizer | |
| import os | |
| import hashlib | |
| import keras | |
| os.mkdir("cache") | |
| def hash_str(data: str): | |
| return hashlib.md5(data.encode('utf-8')).hexdigest() | |
| def train(message: str, epochs: int, learning_rate: float, emb_size: int, inp_len: int, data: str): | |
| if "→" not in data or "\n" not in data: | |
| return "Dataset 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 | |
| data_hash = hash_str(data)+".keras" | |
| 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) | |
| attn_layer = MultiHeadAttention(num_heads=4, key_dim=128)(emb_layer, emb_layer, emb_layer) | |
| noise_layer = GaussianNoise(0.1)(attn_layer) | |
| conv1_layer = Conv1D(64, 8, padding='same', activation='relu', strides=1, input_shape=(64, 128))(noise_layer) | |
| conv2_layer = Conv1D(16, 4, padding='valid', activation='relu', strides=1)(conv1_layer) | |
| conv3_layer = Conv1D(8, 2, padding='valid', activation='relu', strides=1)(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]) | |
| dense1_layer = Dense(512, activation="linear")(concat1_layer) | |
| prelu1_layer = PReLU()(dense1_layer) | |
| dropout_layer = Dropout(0.3)(prelu1_layer) | |
| dense2_layer = Dense(256, activation="tanh")(dropout_layer) | |
| dense3_layer = Dense(256, activation="relu")(dense2_layer) | |
| dense4_layer = Dense(100, activation="tanh")(dense3_layer) | |
| concat2_layer = Concatenate()([dense4_layer, prelu1_layer, attn_flatten_layer, conv1_flatten_layer]) | |
| dense4_layer = Dense(resps_len, activation="softmax")(concat2_layer) | |
| model = Model(inputs=input_layer, outputs=dense4_layer) | |
| X = [] | |
| y = [] | |
| 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=16, 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] | |
| keras.backend.clear_session() | |
| return responses[np.argmax(prediction)] | |
| iface = gr.Interface(fn=train, inputs=["text", | |
| gr.inputs.Slider(1, 64, default=32, step=1, label="Epochs"), | |
| gr.inputs.Slider(0.00000001, 0.1, default=0.001, step=0.00000001, label="Learning rate"), | |
| gr.inputs.Slider(1, 256, default=100, step=1, label="Embedding size"), | |
| gr.inputs.Slider(1, 128, default=16, step=1, label="Input Length"), | |
| "text"], | |
| outputs="text") | |
| iface.launch() |