Delete api.py

api.py

@@ -1,201 +0,0 @@
-# demo_phobert_api.py
-# -*- coding: utf-8 -*-
-
-from fastapi import FastAPI
-from pydantic import BaseModel
-import torch
-import re
-import json
-import emoji
-from underthesea import word_tokenize
-from transformers import AutoConfig, AutoTokenizer, AutoModelForSequenceClassification
-
-# Khởi tạo FastAPI app
-app = FastAPI(
-    title="PhoBERT Emotion Classification API",
-    description="API dự đoán cảm xúc của câu tiếng Việt sử dụng PhoBERT.",
-    version="1.0"
-)
-
-###############################################################################
-# TẢI MAPPING EMOJI - COPY Y NGUYÊN TỪ FILE TRAIN
-###############################################################################
-emoji_mapping = {
-    "😀": "[joy]", "😃": "[joy]", "😄": "[joy]", "😁": "[joy]", "😆": "[joy]", "😅": "[joy]", "😂": "[joy]", "🤣": "[joy]",
-    "🙂": "[love]", "🙃": "[love]", "😉": "[love]", "😊": "[love]", "😇": "[love]", "🥰": "[love]", "😍": "[love]",
-    "🤩": "[love]", "😘": "[love]", "😗": "[love]", "☺": "[love]", "😚": "[love]", "😙": "[love]",
-    "😋": "[satisfaction]", "😛": "[satisfaction]", "😜": "[satisfaction]", "🤪": "[satisfaction]", "😝": "[satisfaction]",
-    "🤑": "[satisfaction]",
-    "🤐": "[neutral]", "🤨": "[neutral]", "😐": "[neutral]", "😑": "[neutral]", "😶": "[neutral]",
-    "😏": "[sarcasm]",
-    "😒": "[disappointment]", "🙄": "[disappointment]", "😬": "[disappointment]",
-    "😔": "[sadness]", "😪": "[sadness]", "😢": "[sadness]", "😭": "[sadness]", "😥": "[sadness]", "😓": "[sadness]",
-    "😩": "[tiredness]", "😫": "[tiredness]", "🥱": "[tiredness]",
-    "🤤": "[discomfort]", "🤢": "[discomfort]", "🤮": "[discomfort]", "🤧": "[discomfort]", "🥵": "[discomfort]",
-    "🥶": "[discomfort]", "🥴": "[discomfort]", "😵": "[discomfort]", "🤯": "[discomfort]",
-    "😕": "[confused]", "😟": "[confused]", "🙁": "[confused]", "☹": "[confused]",
-    "😮": "[surprise]", "😯": "[surprise]", "😲": "[surprise]", "😳": "[surprise]", "🥺": "[pleading]",
-    "😦": "[fear]", "😧": "[fear]", "😨": "[fear]", "😰": "[fear]", "😱": "[fear]",
-    "😖": "[confusion]", "😣": "[confusion]", "😞": "[confusion]",
-    "😤": "[anger]", "😡": "[anger]", "😠": "[anger]", "🤬": "[anger]", "😈": "[mischievous]", "👿": "[mischievous]"
-}
-
-###############################################################################
-# HÀM XỬ LÝ (COPY TỪ FILE TRAIN)
-###############################################################################
-def replace_emojis(sentence, emoji_mapping):
-    processed_sentence = []
-    for char in sentence:
-        if char in emoji_mapping:
-            processed_sentence.append(emoji_mapping[char])
-        elif not emoji.is_emoji(char):
-            processed_sentence.append(char)
-    return ''.join(processed_sentence)
-
-def remove_profanity(sentence):
-    profane_words = ["loz", "vloz", "vl", "dm", "đm", "clgt", "dmm", "cc", "vc", "đù mé", "vãi"]
-    words = sentence.split()
-    filtered = [w for w in words if w.lower() not in profane_words]
-    return ' '.join(filtered)
-
-def remove_special_characters(sentence):
-    return re.sub(r"[\^\*@#&$%<>~{}|\\]", "", sentence)
-
-def normalize_whitespace(sentence):
-    return ' '.join(sentence.split())
-
-def remove_repeated_characters(sentence):
-    return re.sub(r"(.)\1{2,}", r"\1", sentence)
-
-def replace_numbers(sentence):
-    return re.sub(r"\d+", "[number]", sentence)
-
-def tokenize_underthesea(sentence):
-    tokens = word_tokenize(sentence)
-    return " ".join(tokens)
-
-# Nếu có abbreviations.json, load nó. Nếu không thì để rỗng.
-try:
-    with open("abbreviations.json", "r", encoding="utf-8") as f:
-        abbreviations = json.load(f)
-except Exception as e:
-    abbreviations = {}
-
-def preprocess_sentence(sentence):
-    sentence = sentence.lower()
-    sentence = replace_emojis(sentence, emoji_mapping)
-    sentence = remove_profanity(sentence)
-    sentence = remove_special_characters(sentence)
-    sentence = normalize_whitespace(sentence)
-    # Thay thế từ viết tắt nếu có trong abbreviations
-    words = sentence.split()
-    replaced = []
-    for w in words:
-        if w in abbreviations:
-            replaced.append(" ".join(abbreviations[w]))
-        else:
-            replaced.append(w)
-    sentence = " ".join(replaced)
-    sentence = remove_repeated_characters(sentence)
-    sentence = replace_numbers(sentence)
-    sentence = tokenize_underthesea(sentence)
-    return sentence
-
-###############################################################################
-# LOAD CHECKPOINT
-###############################################################################
-checkpoint_dir = "./checkpoint"  # Đường dẫn đến folder checkpoint
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-print("Loading config...")
-config = AutoConfig.from_pretrained(checkpoint_dir)
-
-# Mapping id to label theo thứ tự bạn cung cấp
-custom_id2label = {
-    0: 'Anger',
-    1: 'Disgust',
-    2: 'Enjoyment',
-    3: 'Fear',
-    4: 'Other',
-    5: 'Sadness',
-    6: 'Surprise'
-}
-
-if hasattr(config, "id2label") and config.id2label:
-    if all(label.startswith("LABEL_") for label in config.id2label.values()):
-        id2label = custom_id2label
-    else:
-        id2label = {int(k): v for k, v in config.id2label.items()}
-else:
-    id2label = custom_id2label
-
-print("id2label loaded:", id2label)
-
-print("Loading tokenizer...")
-tokenizer = AutoTokenizer.from_pretrained(checkpoint_dir)
-
-print("Loading model...")
-model = AutoModelForSequenceClassification.from_pretrained(checkpoint_dir, config=config)
-model.to(device)
-model.eval()
-
-###############################################################################
-# HÀM PREDICT
-###############################################################################
-label2message = {
-    'Anger': 'Hãy bình tĩnh và giải quyết vấn đề một cách bình thản.',
-    'Disgust': 'Hãy tránh xa những thứ khiến bạn không thích.',
-    'Enjoyment': 'Chúc mừng bạn có một ngày tuyệt vời!',
-    'Fear': 'Hãy đối mặt với nỗi sợ để vượt qua chúng.',
-    'Other': 'Cảm xúc của bạn hiện tại không được phân loại rõ ràng.',
-    'Sadness': 'Hãy tìm kiếm sự hỗ trợ khi cần thiết.',
-    'Surprise': 'Thật bất ngờ! Hãy tận hưởng khoảnh khắc này.'
-}
-
-def predict_text(text: str) -> str:
-    text_proc = preprocess_sentence(text)
-    inputs = tokenizer(
-        [text_proc],
-        padding=True,
-        truncation=True,
-        max_length=256,
-        return_tensors="pt"
-    ).to(device)
-
-    with torch.no_grad():
-        outputs = model(**inputs)
-        pred_id = outputs.logits.argmax(dim=-1).item()
-
-    if pred_id in id2label:
-        label = id2label[pred_id]
-        message = label2message.get(label, "")
-        if message:
-            return f"Dự đoán cảm xúc: {label}. {message}"
-        else:
-            return f"Dự đoán cảm xúc: {label}."
-    else:
-        return f"Nhãn không xác định (id={pred_id})"
-
-###############################################################################
-# ĐỊNH NGHĨA MODEL INPUT
-###############################################################################
-class InputText(BaseModel):
-    text: str
-
-###############################################################################
-# API ENDPOINT
-###############################################################################
-@app.post("/predict")
-def predict(input_text: InputText):
-    """
-    Nhận một câu tiếng Việt và trả về dự đoán cảm xúc.
-    """
-    result = predict_text(input_text.text)
-    return {"result": result}
-
-###############################################################################
-# CHẠY API SERVER
-###############################################################################
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)