Spaces:
Runtime error
Runtime error
File size: 3,254 Bytes
7ff1fe0 4120946 7ff1fe0 e63aa20 7ff1fe0 6b07ee4 b31816e 48c823d 7ff1fe0 b7f929e 6b07ee4 007ec3d 25fedb7 d7c0eb6 25fedb7 007ec3d f549aa3 007ec3d 1054a05 b7f929e 48c823d b7f929e 28a310a 48c823d 7b3a151 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 |
"""FastAPI endpoint
To run locally use 'uvicorn app:app --host localhost --port 7860'
"""
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_conversational_response
from mathtext_fastapi.nlu import evaluate_message_with_nlu
app = FastAPI()
app.mount("/static", StaticFiles(directory="static"), name="static")
templates = Jinja2Templates(directory="templates")
class Text(BaseModel):
content: str = ""
@app.get("/")
def home(request: Request):
return templates.TemplateResponse("home.html", {"request": request})
@app.post("/hello")
def hello(content: Text = None):
content = {"message": f"Hello {content.content}!"}
return JSONResponse(content=content)
@app.post("/sentiment-analysis")
def sentiment_analysis_ep(content: Text = None):
ml_response = sentiment(content.content)
content = {"message": ml_response}
return JSONResponse(content=content)
@app.post("/text2int")
def text2int_ep(content: Text = None):
ml_response = text2int(content.content)
content = {"message": ml_response}
return JSONResponse(content=content)
@app.post("/manager")
async def programmatic_message_manager(request: Request):
"""
Calls the conversation management function to determine what to send to the user based on the current state and user response
Input
request.body: dict - a json object of 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 - a json object that holds 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_conversational_response(data_dict)
return JSONResponse(context)
@app.post("/nlu")
async def evaluate_user_message_with_nlu_api(request: Request):
""" 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
Output
- int_data_dict or sent_data_dict: A dictionary telling the type of NLU run and the resulting data
{'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)
|