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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"],
title="Coffee Shop Bot").launch()
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