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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(share=True) | |