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Build error
Build error
Update package versions
Browse files- app.py +3 -5
- data/text2int_results.csv +90 -0
- requirements.txt +7 -7
- tests/test_text2int.py +1 -1
app.py
CHANGED
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@@ -8,11 +8,9 @@ from fastapi.staticfiles import StaticFiles
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from fastapi.templating import Jinja2Templates
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from pydantic import BaseModel
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from modules.sentiment import sentiment
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from modules.text2int import text2int
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from modules.nlu import prepare_message_data_for_logging
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app = FastAPI()
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@@ -49,6 +47,7 @@ def text2int_ep(content: Text = None):
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content = {"message": ml_response}
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return JSONResponse(content=content)
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@app.post("/nlu")
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async def evaluate_user_message_with_nlu_api(request: Request):
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""" Calls NLU APIs on the most recent user message from Turn.io message data and logs the message data
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@@ -78,4 +77,3 @@ async def evaluate_user_message_with_nlu_api(request: Request):
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int_data_dict = {'type': 'integer', 'data': int_api_resp}
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return JSONResponse(content=int_data_dict)
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-
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from fastapi.templating import Jinja2Templates
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from pydantic import BaseModel
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from modules.nlu import prepare_message_data_for_logging
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from mathtext.sentiment import sentiment
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from mathtext.text2int import text2int
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app = FastAPI()
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content = {"message": ml_response}
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return JSONResponse(content=content)
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+
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@app.post("/nlu")
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async def evaluate_user_message_with_nlu_api(request: Request):
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""" Calls NLU APIs on the most recent user message from Turn.io message data and logs the message data
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int_data_dict = {'type': 'integer', 'data': int_api_resp}
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return JSONResponse(content=int_data_dict)
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data/text2int_results.csv
ADDED
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@@ -0,0 +1,90 @@
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input,output,text2int,score
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fourteen,14,14,True
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forteen,14,14,True
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one thousand four hundred ninety two,1492,1492,True
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one thousand ninety two,1092,1092,True
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Fourteen Hundred Ninety-Two,1492,1492,True
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Fourteen Hundred,1400,1400,True
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Ninety nine,99,99,True
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fifteen thousand five hundred-sixty,15560,15560,True
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three hundred fifty,350,350,True
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one nine eight five,1985,1985,True
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nineteen eighty-five,1985,1605,False
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oh one,1,1,True
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six oh 1,601,601,True
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sex,6,6,True
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six,6,6,True
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eight oh,80,8,False
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eighty,80,80,True
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ate,8,1,False
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double eight,88,32202,False
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eight three seven five three O nine,8375309,8375329,False
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eight three seven five three oh nine,8375309,8375309,True
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eight three seven five three zero nine,8375309,8375309,True
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eight three seven five three oh ni-ee-ine,8375309,837530619,False
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two eight,28,16,False
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seven oh eleven,7011,77,False
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seven elevens,77,77,True
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seven eleven,711,77,False
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ninety nine oh five,9905,149,False
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seven 0 seven 0 seven 0 seven,7070707,7070707,True
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123 hundred,123000,223,False
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5 o 5,505,525,False
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15 o 5,1505,22,False
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15-o 5,1505,22,False
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15 o-5,1505,22,False
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911-thousand,911000,911000,True
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twenty-two twenty-two,2222,44,False
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twenty-two twenty-twos,484,44,False
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four eighty four,484,404,False
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four eighties,320,72,False
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four eighties and nine nineties,1130,243,False
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ninety nine hundred and seventy seven,9977,276,False
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seven thousands,7000,7000,True
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2 hundreds,200,200,True
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99 thousands and one,99001,99001,True
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"forty-five thousand, seven hundred and nine",45709,1161,False
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eighty eight hundred eighty,8880,268,False
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a hundred hundred,10000,32202,False
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a hundred thousand,100000,32202,False
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a hundred million,100000000,32202,False
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nineteen ninety nine,1999,1809,False
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forteen twenty seven,1427,307,False
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seventeen-thousand and seventy two,17072,17072,True
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two hundred and nine,209,209,True
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two thousand ten,2010,2010,True
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two thousand and ten,2010,2010,True
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twelve million,12000000,12000000,True
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8 billion,8000000000,8000000000,True
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twenty ten,2010,2010,True
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thirty-two hundred,3200,3200,True
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nine,9,9,True
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forty two,42,42,True
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1 2 three,123,123,True
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fourtean,14,14,True
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one tousand four hundred ninty two,1492,1492,True
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Furteen Hundrd Ninety-Too,1492,1492,True
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forrteen,14,14,True
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sevnteen-thosand and seventy two,17072,17072,True
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ninety nine hundred ad seventy seven,9977,32202,False
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seven thusands,7000,7000,True
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2 hunreds,200,200,True
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99 tousands and one,99001,99001,True
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eighty ate hundred eighty,8880,261,False
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fourteen Hundred,1400,1400,True
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8 Bilion,8000000000,8000000,False
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one million three thousand one,1003001,1003001,True
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four million nine thousand seven,4009007,4009007,True
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two million five hundred thousand,2500000,2001500,False
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two tousand ten,2010,2010,True
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two thousand teen,2010,2007,False
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tvelve milion,12000000,12000000,True
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tventy ten,2010,2010,True
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tirty-twoo hunred,3200,3200,True
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sevn thoosands,7000,7000,True
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five,5,5,True
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ten,10,10,True
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one two three and ten,12310,51,False
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ONE MILLion three hunded and fiv,1000305,1000305,True
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"50,500 and six",50506,50506,True
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one_million_and_five,1000005,1000005,True
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requirements.txt
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fastapi==0.74.*
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requests==2.27.*
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sentencepiece==0.1.*
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torch==1.
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transformers==4.*
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uvicorn[standard]==0.17.*
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pydantic
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mathtext @ git+https://gitlab.com/tangibleai/community/mathtext@main
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spacy
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pandas
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matplotlib
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pytest
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httpx
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django-environ
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supabase
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fastapi==0.74.*
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requests==2.27.*
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sentencepiece==0.1.*
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torch==1.12.*
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transformers==4.24.*
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uvicorn[standard]==0.17.*
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pydantic
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mathtext @ git+https://gitlab.com/tangibleai/community/mathtext@main
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spacy==3.4.*
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pandas==1.5.*
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matplotlib==3.6.*
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pytest==7.2.*
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httpx==0.23.*
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django-environ
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supabase
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tests/test_text2int.py
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df.to_csv("data/text2int_results.csv", index=False)
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acc_score = df["score"].mean().__round__(2)
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self.assertGreaterEqual(acc_score, 0.
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if __name__ == '__main__':
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df.to_csv("data/text2int_results.csv", index=False)
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acc_score = df["score"].mean().__round__(2)
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self.assertGreaterEqual(acc_score, 0.5, f"Accuracy score: '{acc_score}'. Value is too low!")
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if __name__ == '__main__':
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