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from fastapi import FastAPI |
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from huggingface_hub import hf_hub_download |
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import os |
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from pydantic import BaseModel |
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from fastapi.responses import JSONResponse |
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print("Version ---- 2") |
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app = FastAPI() |
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def download_file_from_hf(repo_id, filename): |
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target_dir = os.path.expanduser("~/.sinatools") |
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os.makedirs(target_dir, exist_ok=True) |
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file_path = hf_hub_download( |
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repo_id=repo_id, |
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filename=filename, |
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local_dir=target_dir, |
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local_dir_use_symlinks=False |
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) |
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return file_path |
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download_file_from_hf("SinaLab/ALMA","lemmas_dic.pickle") |
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download_file_from_hf("SinaLab/ArabGlossBERT","two_grams.pickle") |
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download_file_from_hf("SinaLab/ArabGlossBERT","three_grams.pickle") |
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download_file_from_hf("SinaLab/ArabGlossBERT","four_grams.pickle") |
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download_file_from_hf("SinaLab/ArabGlossBERT","five_grams.pickle") |
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from sinatools.morphology.morph_analyzer import analyze |
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from sinatools.utils.tokenizer import sentence_tokenizer |
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class ALMARequest(BaseModel): |
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text: str |
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language: str |
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task: str |
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flag: str |
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@app.post("/predict") |
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def predict(request: ALMARequest): |
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text = request.text |
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language = request.language |
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task = request.task |
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flag = request.flag |
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sentences = sentence_tokenizer(text, dot=True, new_line=True, question_mark=True, exclamation_mark=True) |
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results_with_sentences = [] |
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for i, sentence in enumerate(sentences): |
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sentence_id = i + 1 |
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result = analyze(sentence, language, task, str(flag)) |
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results_with_sentences.append({ |
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"sentence_id": sentence_id, |
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"sentence": sentence, |
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"lemmatizer_results": result |
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}) |
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content = {"resp": results_with_sentences, "statusText": "OK", "statusCode": 0} |
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return JSONResponse( |
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content=content, |
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media_type="application/json", |
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status_code=200, |
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) |
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