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d3aaa0b
1
Parent(s):
a0c40d9
Update main.py
Browse files
main.py
CHANGED
@@ -30,11 +30,22 @@ import threading
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import firebase_admin
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from firebase_admin import credentials
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from firebase_admin import firestore
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# Firebase ininlaziton
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cred = credentials.Certificate(
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"text-to-emotions-firebase-adminsdk-8isbn-dffbdf01e8.json")
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firebase_admin.initialize_app(cred)
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# Model inilization
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isristemmer = ISRIStemmer()
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@@ -142,7 +153,16 @@ def original_values(num):
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return 'sympathy'
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elif num == 6:
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return 'fear'
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-
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def modelpredict(data):
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data = txt_preprocess(data)
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@@ -158,7 +178,13 @@ app = FastAPI()
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@app.get("/")
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def index():
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return "Hello World"
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-
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@app.post("/predict")
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async def read_root(request: Request):
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json_data = await request.json()
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import firebase_admin
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from firebase_admin import credentials
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from firebase_admin import firestore
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from transformers import BertTokenizer, AutoModelForSeq2SeqLM, pipeline
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from arabert.preprocess import ArabertPreprocessor
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import re
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# Firebase ininlaziton
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cred = credentials.Certificate(
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"text-to-emotions-firebase-adminsdk-8isbn-dffbdf01e8.json")
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firebase_admin.initialize_app(cred)
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# Model summury
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model_name="abdalrahmanshahrour/auto-arabic-summarization"
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preprocessor = ArabertPreprocessor(model_name="")
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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modelsummary = AutoModelForSeq2SeqLM.from_pretrained(model_name)
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pipeline1 = pipeline("text2text-generation",model=modelsummary,tokenizer=tokenizer)
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# Model inilization
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isristemmer = ISRIStemmer()
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return 'sympathy'
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elif num == 6:
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return 'fear'
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def modelsummary(data):
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result = pipeline1(text,
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pad_token_id= tokenizer.eos_token_id,
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num_beams=4,
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repetition_penalty=3.0,
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max_length=600,
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length_penalty=1.0,
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no_repeat_ngram_size = 3)[0]['generated_text']
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result = remove_punctuations(result)
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return { 'summary':result}
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def modelpredict(data):
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data = txt_preprocess(data)
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@app.get("/")
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def index():
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return "Hello World"
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@app.post("/summary")
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async def read_root(request:Request):
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json_data = await request.json()
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if 'text'in json_data:
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return modelsummary(json_data['text'])
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else:
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raise HTTPException(status_code=400, detail="Missing text value")
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@app.post("/predict")
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async def read_root(request: Request):
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json_data = await request.json()
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