Update 'chatbot/app.py'

chatbot/app.py

@@ -1,84 +1,14 @@
 import requests
 from flask import Flask, request, jsonify,render_template
 from flask_cors import CORS
-
+import time
+import pandas as pd
 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"
 
@@ -89,14 +19,15 @@ def home():
 
 @app.route('/webhook', methods=['POST','GET'])
 def webhook():
+    user_ip = request.remote_addr 
+    print("user ip is :",user_ip)
     message = request.json['message']
-   # message =request.args.get('msg')
+   
+   
+    #recipient_id = request.json['recipient_id']
 
     # Send the message to the Rasa server
-    rasa_response = requests.post(rasa_server_url, json={"message": message}).json()
+    rasa_response = requests.post(rasa_server_url, json={"message": message,"sender":user_ip}).json()
- 
-    # for d in rasa_response:
-    #     a=d["text"]
  
     # Return the Rasa response as a JSON object
     print(rasa_response)
@@ -106,11 +37,11 @@ def webhook():
         "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')