AI/chatbot/app.py

import requests
from flask import Flask, request, jsonify,render_template
from flask_cors import CORS

app = Flask(__name__)



CORS(app)

# import nltk
# nltk.download('popular')
# from nltk.stem import WordNetLemmatizer
# lemmatizer = WordNetLemmatizer()
# import pickle
# import numpy as np

# from 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):
#     ints = predict_class(msg, model)
#     res = getResponse(ints, intents)
#     return res



app.static_folder = 'static'



# Define the Rasa server URL
rasa_server_url = "http://localhost:5005/webhooks/rest/webhook"

@app.route("/")
def home():
    return render_template("index.html")


@app.route('/webhook', methods=['POST','GET'])
def webhook():
    message = request.json['message']
   # message =request.args.get('msg')

    # Send the message to the Rasa server
    rasa_response = requests.post(rasa_server_url, json={"message": message}).json()
 
    # for d in rasa_response:
    #     a=d["text"]

    # Return the Rasa response as a JSON object
    print(rasa_response)
    if len(rasa_response)==0:
        return jsonify([
    {
        "recipient_id": "default",
        "text": "I'm sorry, I didn't understand that. Can you please rephrase?"
    }])
    else:
        return jsonify(rasa_response)



@app.route("/get")
def get_bot_response():
    userText = request.args.get('msg')
    return chatbot_response(userText)


if __name__ == '__main__':
    app.run(host='0.0.0.0',port=5020)