From 074f6cdbfb8955116e945599c63a3b9d1a820450 Mon Sep 17 00:00:00 2001
From: SadhulaSaiKumar <sagar96767@gmail.com>
Date: Wed, 30 Aug 2023 04:41:00 +0000
Subject: [PATCH] Update 'chatbot/app.py'

---
 chatbot/app.py | 171 +++++++++++++++----------------------------------
 1 file changed, 51 insertions(+), 120 deletions(-)

diff --git a/chatbot/app.py b/chatbot/app.py
index ec71523..5a3acd0 100644
--- a/chatbot/app.py
+++ b/chatbot/app.py
@@ -1,121 +1,52 @@
-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__':
+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)
+
+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():
+    user_ip = request.remote_addr 
+    print("user ip is :",user_ip)
+    message = request.json['message']
+   
+   
+    #recipient_id = request.json['recipient_id']
+
+    # Send the message to the Rasa server
+    rasa_response = requests.post(rasa_server_url, json={"message": message,"sender":user_ip}).json()
+ 
+    # 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)
\ No newline at end of file