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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) | |
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() | |
def predict(message, history): | |
history = history or [] | |
sentence = tokenize(message) | |
X = bag_of_words(sentence, 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'])] | |
else: | |
reply = ["Sorry I do not understand :-("] | |
history.append((message, 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() | |