chatbot

chatbot.py

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+import random
+import json
+import pickle
+import numpy as np
+
+import nltk
+nltk.download('wordnet')
+nltk.download('omw-1.4')
+from nltk.tokenize import wordpunct_tokenize
+from nltk.stem import WordNetLemmatizer
+from keras.models import load_model
+
+
+
+lemmatizer=WordNetLemmatizer()
+intents = json.loads(open('D:\AI and ML\Projects\Chat-Bot\intent.json').read())
+
+words=pickle.load(open('D:\AI and ML\Projects\Chat-Bot\words.pkl','rb'))
+classes=pickle.load(open('D:\AI and ML\Projects\Chat-Bot\classes.pkl','rb'))
+model=load_model('D:\AI and ML\Projects\Chat-Bot\chatbotmodel.h5')
+
+def clean_up_sentence(sentence):
+    sentence_words=wordpunct_tokenize(sentence)
+    sentence_words=[lemmatizer.lemmatize(word) for word in sentence_words]
+    return sentence_words
+
+def bag_of_words(sentence):
+    sentence_words=clean_up_sentence(sentence)
+    bag=[0]*len(words)
+    for w in sentence_words:
+        for i,word in enumerate(words):
+            if word==w:
+                bag[i]=1
+    return np.array(bag)
+
+
+def predict_class(sentence):
+    bow=bag_of_words(sentence)
+    res=model.predict(np.array([bow]))[0]
+    ERROR_THRESHOLD=0.1
+    results = [[i,r] for i,r in enumerate(res) if r> ERROR_THRESHOLD]
+
+    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 get_response(intents_list,intents_json):
+    result= None
+    tag=intents_list[0]['intent']
+    list_of_intents=intents_json['intents']
+    for i in list_of_intents:
+        if i['tag']==tag:
+            result=random.choice(i['response'])
+            break
+    return result 
+
+
+print("Go! Bot is running")
+
+while True:
+    message=input("")
+    ints=predict_class(message)
+    res=get_response(ints,intents)
+    print(res)