chatbot_constructor.py

import gradio as gr
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 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 = "", dropout: float = 0.5, learning_rate: float = 0.001, epochs: int = 16, emb_size: int = 128, input_len: int = 16, kernels_count: int = 8, kernel_size: int = 8, data: str = ""):
    data_hash = None
    if "→" not in data or "\n" not in data:
            if data in os.listdir("cache"):
                data_hash = data
            else:
                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
    inp_len = input_len
    if data_hash is None:
        data_hash = hash_str(data)+"_"+str(epochs)+"_"+str(learning_rate)+"_"+str(emb_size)+"_"+str(inp_len)+"_"+str(kernels_count)+"_"+str(kernel_size)+".keras"
    elif 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(0.25)(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))(noise_layer)
        conv2_layer = Conv1D(16, 4, padding='same', activation='relu', strides=1)(conv1_layer)
        conv3_layer = Conv1D(8, 2, padding='same', 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])
        dropout2_layer = Dropout(0.5)(concat1_layer)
        dense1_layer = Dense(512, activation="linear")(dropout2_layer)
        prelu1_layer = PReLU()(dense1_layer)
        dropout3_layer = Dropout(0.5)(prelu1_layer)
        dense2_layer = Dense(256, activation="tanh")(dropout3_layer)
        dropout4_layer = Dropout(0.5)(dense2_layer)
        dense3_layer = Dense(256, activation="relu")(dropout4_layer)
        dropout5_layer = Dropout(0.5)(dense3_layer)
        dense4_layer = Dense(100, activation="tanh")(dropout5_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)]

if __name__ == "__main__":
    iface = gr.Interface(fn=train, inputs=["text",
                                           gr.inputs.Slider(0, 0.5, default=0.5, step=0.00000001, label="Dropout"),
                                           gr.inputs.Slider(0.00000001, 0.1, default=0.001, step=0.00000001, label="Learning rate"),
                                           gr.inputs.Slider(1, 64, default=32, step=1, label="Epochs"),
                                           gr.inputs.Slider(1, 256, default=100, step=1, label="Embedding size"),
                                           gr.inputs.Slider(1, 128, default=16, step=1, label="Input Length"),
                                           gr.inputs.Slider(1, 128, default=64, step=1, label="Convolution kernel count"),
                                           gr.inputs.Slider(1, 16, default=8, step=1, label="Convolution kernel size"),
                                           "text"],
                         outputs="text")
    iface.launch()