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"""FastAPI endpoint | |
To run locally use 'uvicorn app:app --host localhost --port 7860' | |
""" | |
import ast | |
import re | |
from fastapi import FastAPI, Request | |
from fastapi.responses import JSONResponse | |
from fastapi.staticfiles import StaticFiles | |
from fastapi.templating import Jinja2Templates | |
from mathtext.sentiment import sentiment | |
from mathtext.text2int import text2int | |
from pydantic import BaseModel | |
from mathtext_fastapi.logging import prepare_message_data_for_logging | |
from mathtext_fastapi.conversation_manager import manage_conversation_response | |
from mathtext_fastapi.nlu import evaluate_message_with_nlu | |
from scripts.quiz.generators import start_interactive_math | |
from scripts.quiz.hints import generate_hint | |
app = FastAPI() | |
app.mount("/static", StaticFiles(directory="static"), name="static") | |
templates = Jinja2Templates(directory="templates") | |
class Text(BaseModel): | |
content: str = "" | |
def home(request: Request): | |
return templates.TemplateResponse("home.html", {"request": request}) | |
def hello(content: Text = None): | |
content = {"message": f"Hello {content.content}!"} | |
return JSONResponse(content=content) | |
def sentiment_analysis_ep(content: Text = None): | |
ml_response = sentiment(content.content) | |
content = {"message": ml_response} | |
return JSONResponse(content=content) | |
def text2int_ep(content: Text = None): | |
ml_response = text2int(content.content) | |
content = {"message": ml_response} | |
return JSONResponse(content=content) | |
async def programmatic_message_manager(request: Request): | |
""" | |
Calls conversation management function to determine the next state | |
Input | |
request.body: dict - message data for the most recent user response | |
{ | |
"author_id": "+47897891", | |
"contact_uuid": "j43hk26-2hjl-43jk-hnk2-k4ljl46j0ds09", | |
"author_type": "OWNER", | |
"message_body": "a test message", | |
"message_direction": "inbound", | |
"message_id": "ABJAK64jlk3-agjkl2QHFAFH", | |
"message_inserted_at": "2022-07-05T04:00:34.03352Z", | |
"message_updated_at": "2023-02-14T03:54:19.342950Z", | |
} | |
Output | |
context: dict - the information for the current state | |
{ | |
"user": "47897891", | |
"state": "welcome-message-state", | |
"bot_message": "Welcome to Rori!", | |
"user_message": "", | |
"type": "ask" | |
} | |
""" | |
data_dict = await request.json() | |
context = manage_conversation_response(data_dict) | |
return JSONResponse(context) | |
async def evaluate_user_message_with_nlu_api(request: Request): | |
""" Calls nlu evaluation and returns the nlu_response | |
Input | |
- request.body: json - message data for the most recent user response | |
Output | |
- int_data_dict or sent_data_dict: dict - the type of NLU run and result | |
{'type':'integer', 'data': '8'} | |
{'type':'sentiment', 'data': 'negative'} | |
""" | |
data_dict = await request.json() | |
message_data = data_dict.get('message_data', '') | |
nlu_response = evaluate_message_with_nlu(message_data) | |
return JSONResponse(content=nlu_response) | |
async def ask_math_question(request: Request): | |
"""Generates a question and returns it as response along with question data | |
Input | |
request.body: json - amount of correct and incorrect answers in the account | |
{ | |
'number_correct': 0, | |
'number_incorrect': 0 | |
} | |
Output | |
context: dict - the information for the current state | |
{ | |
'text': 'What is 1+2?', | |
'question_numbers': [1,2,3,4], #3 or 4 numbers | |
'right_answer': 3, | |
'number_correct': 0, | |
'number_incorrect': 0, | |
'hints_used': 0 | |
} | |
""" | |
data_dict = await request.json() | |
message_data = ast.literal_eval(data_dict.get('message_data', '').get('message_body', '')) | |
number_correct = message_data['number_correct'] | |
number_incorrect = message_data['number_incorrect'] | |
return JSONResponse(start_interactive_math(number_correct, number_incorrect)) | |
async def get_hint(request: Request): | |
"""Generates a hint and returns it as response along with hint data | |
Input | |
request.body: json - amount of correct and incorrect answers in the account | |
{ | |
'question_numbers': [1,2,3,4], # 3 or 4 numbers | |
'right_answer': 3, | |
'user_answer': 10, | |
'number_correct': 0, | |
'number_incorrect': 0, | |
'hints_used': 0 | |
} | |
Output | |
context: dict - the information for the current state | |
{ | |
'text': 'What is 1+2?', | |
'question_numbers': [1,2,3], #3 or 4 numbers | |
'right_answer': 3, | |
'number_correct': 0, | |
'number_incorrect': 0, | |
'hints_used': 0 | |
} | |
""" | |
data_dict = await request.json() | |
message_data = ast.literal_eval(data_dict.get('message_data', '').get('message_body', '')) | |
question_numbers = message_data['number_correct'] | |
question_numbers = message_data['number_correct'] | |
number_correct = message_data['number_correct'] | |
number_incorrect = message_data['number_incorrect'] | |
hints_used = message_data['hints_used'] | |
return JSONResponse(generate_hint(question_numbers, number_correct, number_incorrect, hints_used)) | |