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#from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
"""
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
"""
import random
import json
import torch
from model import NeuralNet
from nltk_utils import bag_of_words, tokenize
device = torch.device("cpu")
with open('./intents.json', 'r') as json_data:
intents = json.load(json_data)
FILE = "./data.pth"
data = torch.load(FILE)
model_state = torch.load("chatmodel.pth", map_location=torch.device('cpu'))
input_size = data["input_size"]
hidden_size = data["hidden_size"]
output_size = data["output_size"]
all_words = data['all_words']
tags = data['tags']
#model_state = data["model_state"]
model = NeuralNet(input_size, hidden_size, output_size).to(device)
model.load_state_dict(model_state)
model.eval()
#test
def predict(sentence, history):
history = history
"""
# tokenize the new input sentence
new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
# generate a response
history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
# convert the tokens to text, and then split the responses into the right format
response = tokenizer.decode(history[0]).split("<|endoftext|>")
response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)] # convert to tuples of list
"""
sentence1 = tokenize(sentence)
X = bag_of_words(sentence1, all_words)
X = X.reshape(1, X.shape[0])
X = torch.from_numpy(X).to(device)
output = model(X)
_, predicted = torch.max(output, dim=1)
tag = tags[predicted.item()]
probs = torch.softmax(output, dim=1)
prob = probs[0][predicted.item()]
if prob.item() > 0.75:
for intent in intents['intents']:
if tag == intent["tag"]:
reply = [random.choice(intent['responses'])]
history.append((sentence, reply))
return history, history
import gradio as gr
gr.Interface(fn=predict,
theme="default",
css=".footer {display:none !important}",
inputs=["text", "state"],
outputs=["chatbot", "state"]).launch()
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