Spaces:
Runtime error
Runtime error
File size: 2,654 Bytes
8ca4128 232248c 8ca4128 8124efd 6611fe9 8124efd 8ca4128 6611fe9 8124efd 8ca4128 8124efd 8ca4128 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
import gradio as gr
import nltk
nltk.download('popular')
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
import pickle
import numpy as np
from tensorflow.keras.models import load_model
model = load_model('model.h5')
import json
import random
intents = json.loads(open('data.json').read())
words = pickle.load(open('texts.pkl', 'rb'))
classes = pickle.load(open('labels.pkl', 'rb'))
def clean_up_sentence(sentence):
# tokenize the pattern - split words into array
sentence_words = nltk.word_tokenize(sentence)
# stem each word - create short form for word
sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words]
return sentence_words
# return bag of words array: 0 or 1 for each word in the bag that exists in the sentence
def bow(sentence, words, show_details=True):
# tokenize the pattern
sentence_words = clean_up_sentence(sentence)
# bag of words - matrix of N words, vocabulary matrix
bag = [0] * len(words)
for s in sentence_words:
for i, w in enumerate(words):
if w == s:
# assign 1 if current word is in the vocabulary position
bag[i] = 1
if show_details:
print("found in bag: %s" % w)
return (np.array(bag))
def predict_class(sentence, model):
# filter out predictions below a threshold
p = bow(sentence, words, show_details=False)
res = model.predict(np.array([p]))[0]
ERROR_THRESHOLD = 0.25
results = [[i, r] for i, r in enumerate(res) if r > ERROR_THRESHOLD]
# sort by strength of probability
results.sort(key=lambda x: x[1], reverse=True)
return_list = []
for r in results:
return_list.append({"intent": classes[r[0]], "probability": str(r[1])})
return return_list
def getResponse(ints, intents_json):
tag = ints[0]['intent']
list_of_intents = intents_json['intents']
for i in list_of_intents:
if (i['tag'] == tag):
result = random.choice(i['responses'])
break
return result
def chatbot_response(msg, history):
history = history or []
s = list(sum(history, ()))
s.append(msg)
inp = ' '.join(s)
ints = predict_class(msg, model)
res = getResponse(ints, intents)
history.append((msg, res))
return history, history
prompt = "Ask anything ..."
block = gr.Blocks()
with block:
chatbot = gr.Chatbot()
message = gr.Textbox(placeholder=prompt)
state = gr.State()
submit = gr.Button("SEND")
submit.click(chatbot_response, inputs=[message, state], outputs=[chatbot, state])
block.launch(debug = True) |