Improve code modularity and style and incorporate nlu into wormhole prototype

app.py

@@ -11,8 +11,9 @@ from mathtext.sentiment import sentiment
 from mathtext.text2int import text2int
 from pydantic import BaseModel
 
-from mathtext_fastapi.nlu import prepare_message_data_for_logging
-from mathtext_fastapi.conversation_manager import *
+from mathtext_fastapi.logging import prepare_message_data_for_logging
+from mathtext_fastapi.conversation_manager import manage_conversational_response
+from mathtext_fastapi.nlu import evaluate_message_with_nlu
 
 app = FastAPI()
 
@@ -85,7 +86,7 @@ async def programmatic_message_manager(request: Request):
 
 @app.post("/nlu")
 async def evaluate_user_message_with_nlu_api(request: Request):
-    """ Calls NLU APIs on the most recent user message from Turn.io message data and logs the message data
+    """ Calls the nlu evaluation function to run nlu functions and returns the nlu_response to Turn.io
 
     Input
     - request.body: a json object of message data for the most recent user response
@@ -96,45 +97,6 @@ async def evaluate_user_message_with_nlu_api(request: Request):
       {'type':'sentiment', 'data': 'negative'}
     """
     data_dict = await request.json()
-
     message_data = data_dict.get('message_data', '')
-    message_text = message_data['message_body']
-
-    # Handles if a student answer is already an integer or a float (ie., 8)
-    if type(message_text) == int or type(message_text) == float:
-        nlu_response = {'type': 'integer', 'data': message_text, 'confidence': ''}
-        prepare_message_data_for_logging(message_data, nlu_response)
-        return JSONResponse(content=nlu_response)
-
-    # Removes whitespace and converts str to arr to handle multiple numbers
-    message_text_arr = re.split(", |,| ", message_text.strip())
-
-    # Handle if a student answer is a string of numbers (ie., "8,9, 10")
-    if all(ele.isdigit() for ele in message_text_arr):
-        nlu_response = {'type': 'integer', 'data': ','.join(message_text_arr), 'confidence': ''}
-        prepare_message_data_for_logging(message_data, nlu_response)
-        return JSONResponse(content=nlu_response)
-
-    student_response_arr = []
-
-    for student_response in message_text_arr:
-        # Checks the student answer and returns an integer
-
-        int_api_resp = text2int(student_response.lower())
-        student_response_arr.append(int_api_resp)
-
-    # '32202' is text2int's error code for non-integer student answers (ie., "I don't know")
-    # If any part of the list is 32202, sentiment analysis will run
-    if 32202 in student_response_arr:
-        sentiment_api_resp = sentiment(message_text)
-        # [{'label': 'POSITIVE', 'score': 0.991188645362854}]
-        sent_data_dict = {'type': 'sentiment', 'data': sentiment_api_resp[0]['label']}
-        nlu_response = {'type': 'sentiment', 'data': sentiment_api_resp[0]['label'], 'confidence': sentiment_api_resp[0]['score']}
-    else:
-        if len(student_response_arr) > 1:
-            nlu_response = {'type': 'integer', 'data': ','.join(str(num) for num in student_response_arr), 'confidence': ''}
-        else:
-            nlu_response = {'type': 'integer', 'data': student_response_arr[0], 'confidence': ''}
-
-    prepare_message_data_for_logging(message_data, nlu_response)
+    nlu_response = evaluate_message_with_nlu(message_data)
     return JSONResponse(content=nlu_response)

mathtext_fastapi/conversation_manager.py

@@ -3,6 +3,7 @@ import json
 import requests
 
 from dotenv import load_dotenv
+from mathtext_fastapi.nlu import evaluate_message_with_nlu
 
 load_dotenv()
 
@@ -161,6 +162,9 @@ def manage_conversational_response(data_json):
     
     whatsapp_id = message_data['author_id']
     user_message = message_data['message_body']
+
+    # TODO: Need to incorporate nlu_response into wormhole by checking answers against database (spreadsheet?)
+    nlu_response = evaluate_message_with_nlu(message_data)
     
     message_package = return_next_conversational_state(context_data, user_message)
 

mathtext_fastapi/logging.py

@@ -0,0 +1,84 @@
+import os
+from datetime import datetime
+
+from dotenv import load_dotenv
+from supabase import create_client
+
+load_dotenv()
+
+SUPA = create_client(os.environ.get('SUPABASE_URL'), os.environ.get('SUPABASE_KEY'))
+
+
+def log_message_data_through_supabase_api(table_name, log_data):
+    return SUPA.table(table_name).insert(log_data).execute()
+
+
+def format_datetime_in_isoformat(dt):
+    return getattr(dt.now(), 'isoformat', lambda x: None)()
+
+
+def get_or_create_supabase_entry(table_name, insert_data, check_variable=None):
+    """ Checks whether a project or contact exists in the database and adds if one is not found
+
+    Input:
+    - table_name: str- the name of the table in Supabase that is being examined
+    - insert_data: json - the data to insert
+    - check_variable: str/None - the specific field to examine for existing matches
+
+    Result
+    - logged_data - an object with the Supabase data
+    
+    """
+    if table_name == 'contact':
+        resp = SUPA.table('contact').select("*").eq("original_contact_id", insert_data['original_contact_id']).eq("project", insert_data['project']).execute()
+    else:
+        resp = SUPA.table(table_name).select("*").eq(check_variable, insert_data[check_variable]).execute()
+
+    if len(resp.data) == 0:
+        logged_data = log_message_data_through_supabase_api(table_name, insert_data)
+    else:
+        logged_data = resp
+    return logged_data
+
+
+
+def prepare_message_data_for_logging(message_data, nlu_response):
+    """ Builds the message data for each table and ensures it's logged to the database
+
+    Input:
+    - message_data: an object with the full message data from Turn.io/Whatsapp
+    """
+    project_data = {
+        'name': "Rori",
+        # Autogenerated fields: id, created_at, modified_at   
+    }
+    project_data_log = get_or_create_supabase_entry('project', project_data, 'name')
+
+    contact_data = {
+        'project': project_data_log.data[0]['id'],  # FK
+        'original_contact_id': message_data['contact_uuid'],
+        'urn': "",
+        'language_code': "en",
+        'contact_inserted_at': format_datetime_in_isoformat(datetime.now())
+        # Autogenerated fields: id, created_at, modified_at
+    }
+    contact_data_log = get_or_create_supabase_entry('contact', contact_data)
+
+
+    del message_data['author_id']
+
+    message_data = {
+        'contact': contact_data_log.data[0]['id'],  # FK
+        'original_message_id': message_data['message_id'],
+        'text': message_data['message_body'],
+        'direction': message_data['message_direction'],
+        'sender_type': message_data['author_type'],
+        'channel_type': "whatsapp / turn.io",
+        'message_inserted_at': message_data['message_inserted_at'],
+        'message_modified_at': message_data['message_updated_at'],
+        'message_sent_at': format_datetime_in_isoformat(datetime.now()),
+        'nlu_response': nlu_response,
+        'request_object': message_data
+        # Autogenerated fields: created_at, modified_at
+    }
+    message_data_log = log_message_data_through_supabase_api('message', message_data)

mathtext_fastapi/nlu.py

@@ -1,84 +1,87 @@
-import os
-from datetime import datetime
-
-from dotenv import load_dotenv
-from supabase import create_client
-
-load_dotenv()
-
-SUPA = create_client(os.environ.get('SUPABASE_URL'), os.environ.get('SUPABASE_KEY'))
+from mathtext_fastapi.logging import prepare_message_data_for_logging
+from mathtext.sentiment import sentiment
+from mathtext.text2int import text2int
+import re
+
+
+def build_nlu_response_object(type, data, confidence):
+    """ Builds a json object from the result of nlu functions to send back to Turn.io
+    Inputs
+    - type: str - the type of nlu run (integer or sentiment-analysis)
+    - data: str - the student message
+    - confidence: - the nlu confidence score.  Integer is ''.  Sentiment analysis is a float
+    """
+    return {'type': type, 'data': data, 'confidence': confidence}
 
 
-def log_message_data_through_supabase_api(table_name, log_data):
-    return SUPA.table(table_name).insert(log_data).execute()
+def test_for_float_or_int(message_data, message_text):
+    nlu_response = {}
+    if type(message_text) == int or type(message_text) == float:
+        nlu_response = build_nlu_response_object('integer', message_text, '')
+        prepare_message_data_for_logging(message_data, nlu_response)
+    return nlu_response
 
 
-def format_datetime_in_isoformat(dt):
-    return getattr(dt.now(), 'isoformat', lambda x: None)()
+def test_for_number_sequence(message_text_arr, message_data, message_text):
+    nlu_response = {}
+    if all(ele.isdigit() for ele in message_text_arr):
+        nlu_response = build_nlu_response_object('integer', ','.join(message_text_arr), '')
+        prepare_message_data_for_logging(message_data, nlu_response)
+    return nlu_response
 
 
-def get_or_create_supabase_entry(table_name, insert_data, check_variable=None):
-    """ Checks whether a project or contact exists in the database and adds if one is not found
+def run_text2int_on_each_list_item(message_text_arr):
+    """ Checks each item in an array to see if it can be converted to an integer
 
-    Input:
-    - table_name: str- the name of the table in Supabase that is being examined
-    - insert_data: json - the data to insert
-    - check_variable: str/None - the specific field to examine for existing matches
+    Input
+    - message_text_arr: list - a set of text extracted from the student message
 
-    Result
-    - logged_data - an object with the Supabase data
-    
+    Output
+    - student_response_arr: list - a set of integers derived from the nlu function
     """
-    if table_name == 'contact':
-        resp = SUPA.table('contact').select("*").eq("original_contact_id", insert_data['original_contact_id']).eq("project", insert_data['project']).execute()
+    student_response_arr = []
+    for student_response in message_text_arr:
+        int_api_resp = text2int(student_response.lower())
+        student_response_arr.append(int_api_resp)
+    return student_response_arr
+
+
+def run_sentiment_analysis(message_text):
+    # TODO: Add intent labelling here
+    # TODO: Add logic to determine whether intent labeling or sentiment analysis is more appropriate (probably default to intent labeling)
+    return sentiment(message_text)
+
+
+def evaluate_message_with_nlu(message_data):
+    message_text = message_data['message_body']
+    message_text_arr = re.split(", |,| ", message_text.strip())
+
+    # TODO: Replace this with appropriate utility function (is_int, is_float, render_int_or_float)
+    nlu_response = test_for_float_or_int(message_data, message_text)
+    if len(nlu_response) > 0:
+        return nlu_response
+
+    # TODO: Replace this with appropriate utility function
+    nlu_response = test_for_number_sequence(message_text_arr, message_data, message_text)
+    if len(nlu_response) > 0:
+        return nlu_response
+
+    student_response_arr = run_text2int_on_each_list_item(message_text_arr)
+
+    # '32202' is text2int's error code for non-integer student answers (ie., "I don't know")
+    # If any part of the list is 32202, sentiment analysis will run
+    # TODO: Need to replace this with logic that recognizes multiple intents (Maybe 36 = "sentiment analysis" & "integer")
+    student_response_arr = run_text2int_on_each_list_item(message_text_arr)
+    if 32202 in student_response_arr:
+        sentiment_api_resp = sentiment(message_text)
+        nlu_response = build_nlu_response_object('sentiment', sentiment_api_resp[0]['label'], sentiment_api_resp[0]['score'])
     else:
-        resp = SUPA.table(table_name).select("*").eq(check_variable, insert_data[check_variable]).execute()
-
-    if len(resp.data) == 0:
-        logged_data = log_message_data_through_supabase_api(table_name, insert_data)
-    else:
-        logged_data = resp
-    return logged_data
+        if len(student_response_arr) > 1:
+            nlu_response = build_nlu_response_object('integer', ','.join(str(num) for num in student_response_arr), '' )
+        else:
+            nlu_response = build_nlu_response_object('integer', student_response_arr[0], '')
 
+    prepare_message_data_for_logging(message_data, nlu_response)
+    return nlu_response
 
 
-def prepare_message_data_for_logging(message_data, nlu_response):
-    """ Builds the message data for each table and ensures it's logged to the database
-
-    Input:
-    - message_data: an object with the full message data from Turn.io/Whatsapp
-    """
-    project_data = {
-        'name': "Rori",
-        # Autogenerated fields: id, created_at, modified_at   
-    }
-    project_data_log = get_or_create_supabase_entry('project', project_data, 'name')
-
-    contact_data = {
-        'project': project_data_log.data[0]['id'],  # FK
-        'original_contact_id': message_data['contact_uuid'],
-        'urn': "",
-        'language_code': "en",
-        'contact_inserted_at': format_datetime_in_isoformat(datetime.now())
-        # Autogenerated fields: id, created_at, modified_at
-    }
-    contact_data_log = get_or_create_supabase_entry('contact', contact_data)
-
-
-    del message_data['author_id']
-
-    message_data = {
-        'contact': contact_data_log.data[0]['id'],  # FK
-        'original_message_id': message_data['message_id'],
-        'text': message_data['message_body'],
-        'direction': message_data['message_direction'],
-        'sender_type': message_data['author_type'],
-        'channel_type': "whatsapp / turn.io",
-        'message_inserted_at': message_data['message_inserted_at'],
-        'message_modified_at': message_data['message_updated_at'],
-        'message_sent_at': format_datetime_in_isoformat(datetime.now()),
-        'nlu_response': nlu_response,
-        'request_object': message_data
-        # Autogenerated fields: created_at, modified_at
-    }
-    message_data_log = log_message_data_through_supabase_api('message', message_data)

scripts/make_request.py

@@ -1,144 +1,147 @@
+import json
 import requests
 
-# request = requests.post(url=
-#                         'https://tangibleai-mathtext-fastapi.hf.space/sentiment-analysis',
-#                         json={"content": "I reject it"}).json()
-
-# print(request)
-
-# request = requests.post(url=
-#                         'https://tangibleai-mathtext-fastapi.hf.space/text2int',
-#                         json={"content": "seven thousand nine hundred fifty seven"}
-#                         ).json()
-
-# print(request)
-
-
-# # json = {
-# #     'message': {
-# #         '_vnd': {
-# #             'v1': {
-# #                 'author': {
-# #                     'id': 57787919091,
-# #                     'name': 'GT',
-# #                     'type': 'OWNER'
-# #                 },
-# #                 'card_uuid': None,
-# #                 'chat': {
-# #                     'assigned_to': {
-# #                         'id': 'jhk151kl-hj42-3752-3hjk-h4jk6hjkk2',
-# #                         'name': 'Greg Thompson',
-# #                         'type': 'OPERATOR'
-# #                     },
-# #                     'contact_uuid': 'j43hk26-2hjl-43jk-hnk2-k4ljl46j0ds09',
-# #                     'inserted_at': '2022-07-05T04:00:34.033522Z',
-# #                     'owner': '+57787919091',
-# #                     'permalink': 'https://app.turn.io/c/4kl209sd0-a7b8-2hj3-8563-3hu4a89b32',
-# #                     'state': 'OPEN',
-# #                     'state_reason': 'Re-opened by inbound message.',
-# #                     'unread_count': 19,
-# #                     'updated_at': '2023-01-10T02:37:28.487319Z',
-# #                     'uuid': '4kl209sd0-a7b8-2hj3-8563-3hu4a89b32'
-# #                 },
-# #                 'direction': 'inbound',
-# #                 'faq_uuid': None,
-# #                 'in_reply_to': None,
-# #                 'inserted_at': '2023-01-10T02:37:28.477940Z',
-# #                 'labels': [{
-# #                     'confidence': 0.506479332,
-# #                     'metadata': {
-# #                         'nlu': {
-# #                             'confidence': 0.506479332,
-# #                             'intent': 'question',
-# #                             'model_name': 'nlu-general-spacy-ngrams-20191014'
-# #                         }
-# #                     },
-# #                     'uuid': 'ha7890s2k-hjk2-2476-s8d9-fh9779a8a9ds',
-# #                     'value': 'Unclassified'
-# #                 }],
-# #                 'last_status': None,
-# #                 'last_status_timestamp': None,
-# #                 'on_fallback_channel': False,
-# #                 'rendered_content': None,
-# #                 'uuid': 's8df79zhws-h89s-hj23-7s8d-thb248d9bh2qn'
-# #             }
-# #         },
-# #         'from': 57787919091,
-# #         'id': 'hsjkthzZGehkzs09sijWA3',
-# #         'text': {'body': 'eight'},
-# #         'timestamp': 1673318248,
-# #         'type': 'text'
-# #     },
-# #     'type': 'message'
-# # }
-
-# json = b'{"message_data": {"message":{"_vnd":{"v1":{"author":{"id":57787919091,"name":"GT","type":"OWNER"},"card_uuid":null,"chat":{"assigned_to":{"id":"jhk151kl-hj42-3752-3hjk-h4jk6hjkk2","name":"Greg Thompson","type":"OPERATOR"},"contact_uuid":"j43hk26-2hjl-43jk-hnk2-k4ljl46j0ds09","inserted_at":"2022-07-05T04:00:34.033522Z","owner":"+57787919091","permalink":"https://app.turn.io/c/4kl209sd0-a7b8-2hj3-8563-3hu4a89b32","state":"OPEN","state_reason":"Re-opened by inbound message.","unread_count":14,"updated_at":"2023-01-10T02:37:28.487319Z","uuid":"4kl209sd0-a7b8-2hj3-8563-3hu4a89b32"},"direction":"inbound","faq_uuid":null,"in_reply_to":null,"inserted_at":"2023-01-10T02:37:28.477940Z","labels":[{"confidence":0.506479332,"metadata":{"nlu":{"confidence":0.506479332,"intent":"question","model_name":"nlu-general-spacy-ngrams-20191014"}},"uuid":"ha7890s2k-hjk2-2476-s8d9-fh9779a8a9ds","value":"Unclassified"}],"last_status":null,"last_status_timestamp":null,"on_fallback_channel":false,"rendered_content":null,"uuid":"s8df79zhws-h89s-hj23-7s8d-thb248d9bh2qn"}},"from":57787919091,"id":"hsjkthzZGehkzs09sijWA3","text":{"body":"eight"},"timestamp":1673318248,"type":"text"},"type":"message"}}\n'
-
-# # eight > 8
-# request = requests.post(url=
-#                         'http://localhost:7860/nlu',
-#                         data=json
-#                         ).json()
-# print(request)
-
-# json2 = b'{"message_data": {"message":{"_vnd":{"v1":{"author":{"id":57787919091,"name":"GT","type":"OWNER"},"card_uuid":null,"chat":{"assigned_to":{"id":"jhk151kl-hj42-3752-3hjk-h4jk6hjkk2","name":"Greg Thompson","type":"OPERATOR"},"contact_uuid":"j43hk26-2hjl-43jk-hnk2-k4ljl46j0ds09","inserted_at":"2022-07-05T04:00:34.033522Z","owner":"+57787919091","permalink":"https://app.turn.io/c/4kl209sd0-a7b8-2hj3-8563-3hu4a89b32","state":"OPEN","state_reason":"Re-opened by inbound message.","unread_count":14,"updated_at":"2023-01-10T02:37:28.487319Z","uuid":"4kl209sd0-a7b8-2hj3-8563-3hu4a89b32"},"direction":"inbound","faq_uuid":null,"in_reply_to":null,"inserted_at":"2023-01-10T02:37:28.477940Z","labels":[{"confidence":0.506479332,"metadata":{"nlu":{"confidence":0.506479332,"intent":"question","model_name":"nlu-general-spacy-ngrams-20191014"}},"uuid":"ha7890s2k-hjk2-2476-s8d9-fh9779a8a9ds","value":"Unclassified"}],"last_status":null,"last_status_timestamp":null,"on_fallback_channel":false,"rendered_content":null,"uuid":"s8df79zhws-h89s-hj23-7s8d-thb248d9bh2qn"}},"from":57787919091,"id":"hsjkthzZGehkzs09sijWA3","text":{"body":"eight, nine, ten"},"timestamp":1673318248,"type":"text"},"type":"message"}}\n'
-
-# # "eight, nine, ten" > 8,9,10
-# request = requests.post(url=
-#                         'http://localhost:7860/nlu',
-#                         data=json2
-#                         ).json()
-
-# print(request)
-
-
-# json = b'{"message_data": {"message":{"_vnd":{"v1":{"author":{"id":57787919091,"name":"GT","type":"OWNER"},"card_uuid":null,"chat":{"assigned_to":{"id":"jhk151kl-hj42-3752-3hjk-h4jk6hjkk2","name":"Greg Thompson","type":"OPERATOR"},"contact_uuid":"j43hk26-2hjl-43jk-hnk2-k4ljl46j0ds09","inserted_at":"2022-07-05T04:00:34.033522Z","owner":"+57787919091","permalink":"https://app.turn.io/c/4kl209sd0-a7b8-2hj3-8563-3hu4a89b32","state":"OPEN","state_reason":"Re-opened by inbound message.","unread_count":14,"updated_at":"2023-01-10T02:37:28.487319Z","uuid":"4kl209sd0-a7b8-2hj3-8563-3hu4a89b32"},"direction":"inbound","faq_uuid":null,"in_reply_to":null,"inserted_at":"2023-01-10T02:37:28.477940Z","labels":[{"confidence":0.506479332,"metadata":{"nlu":{"confidence":0.506479332,"intent":"question","model_name":"nlu-general-spacy-ngrams-20191014"}},"uuid":"ha7890s2k-hjk2-2476-s8d9-fh9779a8a9ds","value":"Unclassified"}],"last_status":null,"last_status_timestamp":null,"on_fallback_channel":false,"rendered_content":null,"uuid":"s8df79zhws-h89s-hj23-7s8d-thb248d9bh2qn"}},"from":57787919091,"id":"hsjkthzZGehkzs09sijWA3","text":{"body":8},"timestamp":1673318248,"type":"text"},"type":"message"}}\n'
-
-# # 8 > 8
-# request = requests.post(url=
-#                         'http://localhost:7860/nlu',
-#                         data=json
-#                         ).json()
-
-# print(request)
-
-# json = b'{"message_data": {"message":{"_vnd":{"v1":{"author":{"id":57787919091,"name":"GT","type":"OWNER"},"card_uuid":null,"chat":{"assigned_to":{"id":"jhk151kl-hj42-3752-3hjk-h4jk6hjkk2","name":"Greg Thompson","type":"OPERATOR"},"contact_uuid":"j43hk26-2hjl-43jk-hnk2-k4ljl46j0ds09","inserted_at":"2022-07-05T04:00:34.033522Z","owner":"+57787919091","permalink":"https://app.turn.io/c/4kl209sd0-a7b8-2hj3-8563-3hu4a89b32","state":"OPEN","state_reason":"Re-opened by inbound message.","unread_count":14,"updated_at":"2023-01-10T02:37:28.487319Z","uuid":"4kl209sd0-a7b8-2hj3-8563-3hu4a89b32"},"direction":"inbound","faq_uuid":null,"in_reply_to":null,"inserted_at":"2023-01-10T02:37:28.477940Z","labels":[{"confidence":0.506479332,"metadata":{"nlu":{"confidence":0.506479332,"intent":"question","model_name":"nlu-general-spacy-ngrams-20191014"}},"uuid":"ha7890s2k-hjk2-2476-s8d9-fh9779a8a9ds","value":"Unclassified"}],"last_status":null,"last_status_timestamp":null,"on_fallback_channel":false,"rendered_content":null,"uuid":"s8df79zhws-h89s-hj23-7s8d-thb248d9bh2qn"}},"from":57787919091,"id":"hsjkthzZGehkzs09sijWA3","text":{"body":"8, 9, 10"},"timestamp":1673318248,"type":"text"},"type":"message"}}\n'
-
-# # "8, 9, 10" > "8,9,10"
-# request = requests.post(url=
-#                         'http://localhost:7860/nlu',
-#                         data=json
-#                         ).json()
-
-# print(request)
-
-# json = b'{"message_data": {"message":{"_vnd":{"v1":{"author":{"id":57787919091,"name":"GT","type":"OWNER"},"card_uuid":null,"chat":{"assigned_to":{"id":"jhk151kl-hj42-3752-3hjk-h4jk6hjkk2","name":"Greg Thompson","type":"OPERATOR"},"contact_uuid":"j43hk26-2hjl-43jk-hnk2-k4ljl46j0ds09","inserted_at":"2022-07-05T04:00:34.033522Z","owner":"+57787919091","permalink":"https://app.turn.io/c/4kl209sd0-a7b8-2hj3-8563-3hu4a89b32","state":"OPEN","state_reason":"Re-opened by inbound message.","unread_count":14,"updated_at":"2023-01-10T02:37:28.487319Z","uuid":"4kl209sd0-a7b8-2hj3-8563-3hu4a89b32"},"direction":"inbound","faq_uuid":null,"in_reply_to":null,"inserted_at":"2023-01-10T02:37:28.477940Z","labels":[{"confidence":0.506479332,"metadata":{"nlu":{"confidence":0.506479332,"intent":"question","model_name":"nlu-general-spacy-ngrams-20191014"}},"uuid":"ha7890s2k-hjk2-2476-s8d9-fh9779a8a9ds","value":"Unclassified"}],"last_status":null,"last_status_timestamp":null,"on_fallback_channel":false,"rendered_content":null,"uuid":"s8df79zhws-h89s-hj23-7s8d-thb248d9bh2qn"}},"from":57787919091,"id":"hsjkthzZGehkzs09sijWA3","text":{"body":"I dont know"},"timestamp":1673318248,"type":"text"},"type":"message"}}\n'
-
-# # "8, 9, 10" > "8,9,10"
-# request = requests.post(url=
-#                         'http://localhost:7860/nlu',
-#                         data=json
-#                         ).json()
-
-# print(request)
-
-# json = b'{"message_data": {"message":{"_vnd":{"v1":{"author":{"id":57787919091,"name":"GT","type":"OWNER"},"card_uuid":null,"chat":{"assigned_to":{"id":"jhk151kl-hj42-3752-3hjk-h4jk6hjkk2","name":"Greg Thompson","type":"OPERATOR"},"contact_uuid":"j43hk26-2hjl-43jk-hnk2-k4ljl46j0ds09","inserted_at":"2022-07-05T04:00:34.033522Z","owner":"+57787919091","permalink":"https://app.turn.io/c/4kl209sd0-a7b8-2hj3-8563-3hu4a89b32","state":"OPEN","state_reason":"Re-opened by inbound message.","unread_count":14,"updated_at":"2023-01-10T02:37:28.487319Z","uuid":"4kl209sd0-a7b8-2hj3-8563-3hu4a89b32"},"direction":"inbound","faq_uuid":null,"in_reply_to":null,"inserted_at":"2023-01-10T02:37:28.477940Z","labels":[{"confidence":0.506479332,"metadata":{"nlu":{"confidence":0.506479332,"intent":"question","model_name":"nlu-general-spacy-ngrams-20191014"}},"uuid":"ha7890s2k-hjk2-2476-s8d9-fh9779a8a9ds","value":"Unclassified"}],"last_status":null,"last_status_timestamp":null,"on_fallback_channel":false,"rendered_content":null,"uuid":"s8df79zhws-h89s-hj23-7s8d-thb248d9bh2qn"}},"from":57787919091,"id":"hsjkthzZGehkzs09sijWA3","text":{"body":"Today is a wonderful day"},"timestamp":1673318248,"type":"text"},"type":"message"}}\n'
-
-# # "8, 9, 10" > "8,9,10"
-# request = requests.post(url=
-#                         'http://localhost:7860/nlu',
-#                         data=json
-#                         ).json()
-
-# print(request)
-
-
-json = b'{"context": "hi", "message_data": {"message":{"_vnd":{"v1":{"author":{"id":57787919091,"name":"GT","type":"OWNER"},"card_uuid":null,"chat":{"assigned_to":{"id":"jhk151kl-hj42-3752-3hjk-h4jk6hjkk2","name":"Greg Thompson","type":"OPERATOR"},"contact_uuid":"j43hk26-2hjl-43jk-hnk2-k4ljl46j0ds09","inserted_at":"2022-07-05T04:00:34.033522Z","owner":"+57787919091","permalink":"https://app.turn.io/c/4kl209sd0-a7b8-2hj3-8563-3hu4a89b32","state":"OPEN","state_reason":"Re-opened by inbound message.","unread_count":14,"updated_at":"2023-01-10T02:37:28.487319Z","uuid":"4kl209sd0-a7b8-2hj3-8563-3hu4a89b32"},"direction":"inbound","faq_uuid":null,"in_reply_to":null,"inserted_at":"2023-01-10T02:37:28.477940Z","labels":[{"confidence":0.506479332,"metadata":{"nlu":{"confidence":0.506479332,"intent":"question","model_name":"nlu-general-spacy-ngrams-20191014"}},"uuid":"ha7890s2k-hjk2-2476-s8d9-fh9779a8a9ds","value":"Unclassified"}],"last_status":null,"last_status_timestamp":null,"on_fallback_channel":false,"rendered_content":null,"uuid":"s8df79zhws-h89s-hj23-7s8d-thb248d9bh2qn"}},"from":57787919091,"id":"hsjkthzZGehkzs09sijWA3","text":{"body":"Today is a wonderful day"},"timestamp":1673318248,"type":"text"},"type":"message"}}\n'
-
-
-
-request = requests.post(url=
-                        'http://localhost:7860/manager',
-                        data=json
-                        ).json()
-print(request)
+
+def add_message_text_to_sample_object(message_text):
+    """
+    Builds a sample request object using an example of a student answer
+
+    Input
+    - message_text: str - an example of user input to test
+
+    Example Input
+    "test message"
+
+    Output
+    - b_string: json b-string - an object simulating what Turn.io sends to the API endpoint using the message_text
+
+    Example Output
+    b'{"context": "hi", "message_data": {"author_id": "+57787919091", "author_type": "OWNER", "contact_uuid": "j43hk26-2hjl-43jk-hnk2-k4ljl46j0ds09", "message_body": "test message", "message_direction": "inbound", "message_id": "4kl209sd0-a7b8-2hj3-8563-3hu4a89b32", "message_inserted_at": "2023-01-10T02:37:28.477940Z", "message_updated_at": "2023-01-10T02:37:28.487319Z"}}'
+    
+    """
+    message_data = '{' + f'"author_id": "+57787919091", "author_type": "OWNER", "contact_uuid": "j43hk26-2hjl-43jk-hnk2-k4ljl46j0ds09", "message_body": "{message_text}", "message_direction": "inbound", "message_id": "4kl209sd0-a7b8-2hj3-8563-3hu4a89b32", "message_inserted_at": "2023-01-10T02:37:28.477940Z", "message_updated_at": "2023-01-10T02:37:28.487319Z"' + '}'
+    context_data = '{' + '"user":"", "state":"start-conversation", "bot_message":"", "user_message":"{message_text}"' + '}'
+
+    json_string = '{' + f'"context_data": {context_data}, "message_data": {message_data}' + '}'
+    b_string = json_string.encode("utf-8")
+
+    return b_string
+
+
+def run_simulated_request(endpoint, sample_answer, context = None):
+    print(f"Case: {sample_answer}")
+    b_string = add_message_text_to_sample_object(sample_answer)
+
+    if endpoint == 'sentiment-analysis' or endpoint == 'text2int':
+        request = requests.post(url=
+            f'http://localhost:7860/{endpoint}',
+            json={'content': sample_answer}
+        ).json()
+    else:
+        request = requests.post(url=
+            f'http://localhost:7860/{endpoint}',
+            data=b_string
+        ).json()
+
+    print(request)
+
+run_simulated_request('sentiment-analysis', 'I reject it')
+run_simulated_request('text2int', 'seven thousand nine hundred fifty seven')
+run_simulated_request('nlu', 'test message')
+run_simulated_request('nlu', 'eight')
+run_simulated_request('nlu', 'eight, nine, ten')
+run_simulated_request('nlu', '8, 9, 10')
+run_simulated_request('nlu', '8')
+run_simulated_request('nlu', "I don't know")
+run_simulated_request('nlu', 'Today is a wonderful day')
+run_simulated_request('nlu', 'IDK 5?')
+run_simulated_request('manager', '')
+run_simulated_request('manager', 'add')
+run_simulated_request('manager', 'subtract')
+run_simulated_request('manager', 'exit')
+
+
+# Example of simplified object received from Turn.io stacks
+# This is a contrived example to show the structure, not an actual state
+# NOTE: This is actually a bstring, not a dict
+simplified_json = {
+    "context": {
+        "user":"+57787919091", 
+        "state":"answer-addition-problem", 
+        "bot_message":"What is 2+2?", 
+        "user_message":"eight",
+        "type": "ask"
+    }, 
+    "message_data": {
+        "author_id": "+57787919091", 
+        "author_type": "OWNER", 
+        "contact_uuid": "j43hk26-2hjl-43jk-hnk2-k4ljl46j0ds09", 
+        "message_body": "eight", 
+        "message_direction": "inbound", 
+        "message_id": "4kl209sd0-a7b8-2hj3-8563-3hu4a89b32", 
+        "message_inserted_at": "2023-01-10T02:37:28.477940Z", 
+        "message_updated_at": "2023-01-10T02:37:28.487319Z"
+    }
+}
+
+
+
+# Full example of event data from Turn.io 
+# simplified_json is built from this in Turn.io
+# full_json = {
+#     'message': {
+#         '_vnd': {
+#             'v1': {
+#                 'author': {
+#                     'id': 57787919091,
+#                     'name': 'GT',
+#                     'type': 'OWNER'
+#                 },
+#                 'card_uuid': None,
+#                 'chat': {
+#                     'assigned_to': {
+#                         'id': 'jhk151kl-hj42-3752-3hjk-h4jk6hjkk2',
+#                         'name': 'Greg Thompson',
+#                         'type': 'OPERATOR'
+#                     },
+#                     'contact_uuid': 'j43hk26-2hjl-43jk-hnk2-k4ljl46j0ds09',
+#                     'inserted_at': '2022-07-05T04:00:34.033522Z',
+#                     'owner': '+57787919091',
+#                     'permalink': 'https://app.turn.io/c/4kl209sd0-a7b8-2hj3-8563-3hu4a89b32',
+#                     'state': 'OPEN',
+#                     'state_reason': 'Re-opened by inbound message.',
+#                     'unread_count': 19,
+#                     'updated_at': '2023-01-10T02:37:28.487319Z',
+#                     'uuid': '4kl209sd0-a7b8-2hj3-8563-3hu4a89b32'
+#                 },
+#                 'direction': 'inbound',
+#                 'faq_uuid': None,
+#                 'in_reply_to': None,
+#                 'inserted_at': '2023-01-10T02:37:28.477940Z',
+#                 'labels': [{
+#                     'confidence': 0.506479332,
+#                     'metadata': {
+#                         'nlu': {
+#                             'confidence': 0.506479332,
+#                             'intent': 'question',
+#                             'model_name': 'nlu-general-spacy-ngrams-20191014'
+#                         }
+#                     },
+#                     'uuid': 'ha7890s2k-hjk2-2476-s8d9-fh9779a8a9ds',
+#                     'value': 'Unclassified'
+#                 }],
+#                 'last_status': None,
+#                 'last_status_timestamp': None,
+#                 'on_fallback_channel': False,
+#                 'rendered_content': None,
+#                 'uuid': 's8df79zhws-h89s-hj23-7s8d-thb248d9bh2qn'
+#             }
+#         },
+#         'from': 57787919091,
+#         'id': 'hsjkthzZGehkzs09sijWA3',
+#         'text': {'body': 'eight'},
+#         'timestamp': 1673318248,
+#         'type': 'text'
+#     },
+#     'type': 'message'
+# }