Upload files to 'chatbot'

chatbot/app.py

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+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)

chatbot/config.yml

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+recipe: default.v1
+assistant_id: 20230411-174114-maximum-louver
+language: en
+pipeline: null
+# # No configuration for the NLU pipeline was provided. The following default pipeline was used to train your model.
+# # If you'd like to customize it, uncomment and adjust the pipeline.
+# # See https://rasa.com/docs/rasa/tuning-your-model for more information.
+#   - name: WhitespaceTokenizer
+#   - name: RegexFeaturizer
+#   - name: LexicalSyntacticFeaturizer
+#   - name: CountVectorsFeaturizer
+#   - name: CountVectorsFeaturizer
+#     analyzer: char_wb
+#     min_ngram: 1
+#     max_ngram: 4
+#   - name: DIETClassifier
+#     epochs: 100
+#     constrain_similarities: true
+#   - name: EntitySynonymMapper
+#   - name: ResponseSelector
+#     epochs: 100
+#     constrain_similarities: true
+#   - name: FallbackClassifier
+#     threshold: 0.3
+#     ambiguity_threshold: 0.1
+policies: null
+# # No configuration for policies was provided. The following default policies were used to train your model.
+# # If you'd like to customize them, uncomment and adjust the policies.
+# # See https://rasa.com/docs/rasa/policies for more information.
+#   - name: MemoizationPolicy
+#   - name: RulePolicy
+#   - name: UnexpecTEDIntentPolicy
+#     max_history: 5
+#     epochs: 100
+#   - name: TEDPolicy
+#     max_history: 5
+#     epochs: 100
+#     constrain_similarities: true

chatbot/credentials.yml

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+# This file contains the credentials for the voice & chat platforms
+# which your bot is using.
+# https://rasa.com/docs/rasa/messaging-and-voice-channels
+
+#rest:
+#  # you don't need to provide anything here - this channel doesn't
+#  # require any credentials
+
+
+# rest:
+#   # You can customize the webhook URL to include the user's ID as a parameter
+#   webhook_url: "http://localhost:5005/webhooks/rest/webhook?recipient_id={sender_id}"
+
+#facebook:
+#  verify: "<verify>"
+#  secret: "<your secret>"
+#  page-access-token: "<your page access token>"
+
+#slack:
+#  slack_token: "<your slack token>"
+#  slack_channel: "<the slack channel>"
+#  slack_signing_secret: "<your slack signing secret>"
+
+#socketio:
+#  user_message_evt: <event name for user message>
+#  bot_message_evt: <event name for bot messages>
+#  session_persistence: <true/false>
+
+#mattermost:
+#  url: "https://<mattermost instance>/api/v4"
+#  token: "<bot token>"
+#  webhook_url: "<callback URL>"
+
+# This entry is needed if you are using Rasa Enterprise. The entry represents credentials
+# for the Rasa Enterprise "channel", i.e. Talk to your bot and Share with guest testers.
+
+
+

chatbot/domain.yml

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+intents:
+  - greet
+  - goodbye
+  - affirm
+  - deny
+  - mood_great
+  - mood_unhappy
+  - bot_challenge
+  - question1
+  - question3
+  - question4
+  - question5
+  - question6
+  - docs
+  - video
+  - connect_support
+  - noconnect_support
+  
+  
+entities:
+  - content_type
+
+slots:
+  content_type:
+    type: text
+    mappings:
+      - type: from_entity
+        entity: content_type
+
+  
+responses:
+  utter_cheer_up:
+    - image: https://i.imgur.com/nGF1K8f.jpg
+      text: 'Here is something to cheer you up:'
+  utter_did_that_help:
+    - text: Did that help you?
+  utter_goodbye:
+    - text: Bye
+  utter_greet:
+    - text: Hey! How are you?
+  utter_happy:
+    - text: Great, carry on!
+  utter_iamabot:
+    - text: I am a bot, powered by Rasa.
+  utter_question1_1:
+    - text: We have explained the process of changing the old to the new manager for
+        customers in DMS, Follow the below procedures. 1) Open DMS--->Go to the change
+        manager module--->Click on the create option--->Fill in the details--->Save--->Confirm.
+        2) customer module--then select customer--edit the sale manager's name to
+        the required employee and save to reflect in his login
+  utter_question1_2:
+    - text: To change the manager for customers in DMS, first, open the DMS system,
+        then navigate to the "change manager" module, click on the "create" option,
+        fill in the necessary details, save the changes, and confirm the update. Next,
+        go to the "customer" module, select the relevant customer, edit the sales
+        manager's name to the desired employee, and save the changes to reflect the
+        update in their login
+  utter_question1_3:
+    - text: The process of changing the manager for customers in DMS involves several
+        steps. First, access the DMS system and open the "change manager" module.
+        From there, click on the "create" option, provide the required details, and
+        save the changes. Once the update is confirmed, navigate to the "customer"
+        module, select the appropriate customer, modify the sales manager's name to
+        the preferred employee, and save the changes to ensure that it reflects in
+        their login.
+
+  utter_question3_1:
+    - text: Customers module----> enter the customer name----> Open details----> click
+        on sales manager---->edit---->save.
+  utter_question3_2:
+    - text: If you want to change the sales manager for a particular customer in DMS,
+        go to the Customers module and enter the name of the customer. Then, open
+        the customer's details, find the "Sales Manager" section, click on it, choose
+        the "Edit" option to modify the sales manager's name, enter the new name,
+        and save the changes.
+  utter_question3_3:
+    - text: nan
+  utter_question4_1:
+    - text: Please find the below process to change the Customer Segment tag Customer
+        module--->Select Particular Customer----->click on details------>check on
+        + to change the LOB tags.
+  utter_question4_2:
+    - text: If you need to change the Customer Segment tag for a particular customer
+        in DMS, start by accessing the Customer module, selecting the relevant customer,
+        and clicking on their details. Once you are in the customer's details, locate
+        the "+" symbol to modify the LOB tags and update the Customer Segment tag.
+  utter_question4_3:
+    - text: To modify the Customer Segment tag for a specific customer in DMS, go
+        to the Customer module, find the customer whose tag you want to change, and
+        open their details. Then, click on the "+" symbol to add or remove LOB tags,
+        and make the necessary changes to the Customer Segment tag.
+  utter_question5_1:
+    - text: We have explained the process of checking the EB-invoice report through
+        remote session & follow the below procedure. Open DMS-----> click on reports----->open
+        EB- invoice report---->check.
+  utter_question5_2:
+    - text: To verify the EB-invoice report through a remote session, follow these
+        steps first, access the DMS platform, then navigate to the "reports" section
+        and click on the "EB-invoice report." Finally, carefully review the report
+        to ensure all necessary information is present and accurate.
+  utter_question5_3:
+    - text: The process of remotely checking the EB-invoice report can be accomplished
+        in a few simple steps. Begin by logging into the DMS platform and selecting
+        the "reports" option. From there, click on the "EB-invoice report" and review
+        the contents to confirm its accuracy.
+  utter_question6_1:
+    - text: Please find the below process to add branchto employee Employee module------>Search
+        the employee name ---->Open employee----->click on official Widget------>click
+        on branches +check on------->click on plus symbol----->select the branch from
+        the dropdown ------->save.
+  utter_question6_2:
+    - text: To assign a branch to an employee, follow these steps first, access the
+        Employee module and search for the employee's name. Next, open the employee's
+        profile and click on the Official Widget. From there, click on the "branches"
+        option and check the box to enable it. Then, click on the plus symbol and
+        select the appropriate branch from the dropdown menu. Finally, click on "save"
+        to confirm the changes.
+  utter_question6_3:
+    - text: Adding a branch to an employee is a simple process that involves accessing
+        the Employee module and searching for the employee in question. Once you've
+        found their profile, open it and click on the Official Widget, followed by
+        the "branches" option. Check the box to enable branches, then click the plus
+        symbol and select the appropriate branch from the dropdown list. Be sure to
+        save your changes before exiting the page.
+
+  utter_blogs:
+  - text: "great carry on" 
+
+  utter_video:
+  - text: "do you want me to connect you to support team" 
+
+
+  utter_connect_support:
+  - text: "Im conneting you to support team" 
+
+  utter_noconnect_support:
+  - text: "great carry on" 
+
+actions:
+  - action_service
+  - action_suport_service
+  # - action_save_conersation
+  # - action_clear_conversation
+ 
+
+
+session_config:
+  carry_over_slots_to_new_session: true
+  session_expiration_time: 60
+version: '3.1'

chatbot/endpoints.yml

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+# This file contains the different endpoints your bot can use.
+
+# Server where the models are pulled from.
+# https://rasa.com/docs/rasa/model-storage#fetching-models-from-a-server
+
+#models:
+#  url: http://my-server.com/models/default_core@latest
+#  wait_time_between_pulls:  10   # [optional](default: 100)
+
+# Server which runs your custom actions.
+# https://rasa.com/docs/rasa/custom-actions
+
+action_endpoint:
+ url: "http://localhost:5055/webhook?recipient_id={sender_id}"
+
+# Tracker store which is used to store the conversations.
+# By default the conversations are stored in memory.
+# https://rasa.com/docs/rasa/tracker-stores
+
+#tracker_store:
+#    type: redis
+#    url: <host of the redis instance, e.g. localhost>
+#    port: <port of your redis instance, usually 6379>
+#    db: <number of your database within redis, e.g. 0>
+#    password: <password used for authentication>
+#    use_ssl: <whether or not the communication is encrypted, default false>
+
+#tracker_store:
+#    type: mongod
+#    url: <url to your mongo instance, e.g. mongodb://localhost:27017>
+#    db: <name of the db within your mongo instance, e.g. rasa>
+#    username: <username used for authentication>
+#    password: <password used for authentication>
+
+# Event broker which all conversation events should be streamed to.
+# https://rasa.com/docs/rasa/event-brokers
+
+#event_broker:
+#  url: localhost
+#  username: username
+#  password: password
+#  queue: queue
+
+
+
+