# -*- coding: utf-8 -*-
"""SentimentAnalysis

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/#fileId=https%3A//storage.googleapis.com/kaggle-colab-exported-notebooks/fatihramadhan/sentimentanalysis.74f160cb-74cc-4609-ba85-0081c3654a18.ipynb%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com/20260326/auto/storage/goog4_request%26X-Goog-Date%3D20260326T141800Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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
"""

# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES,
# THEN FEEL FREE TO DELETE THIS CELL.
# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON
# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR
# NOTEBOOK.
import kagglehub
fatihramadhan_sentimentdataset_path = kagglehub.dataset_download('fatihramadhan/sentimentdataset')

print('Data source import complete.')

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

import re
import html
import torch
import evaluate
import os
import transformers
import inspect
import joblib

from pathlib import Path
from torch.utils.data import Dataset, DataLoader
from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments, EarlyStoppingCallback, pipeline

from sklearn.model_selection import train_test_split
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.metrics import accuracy_score, f1_score
from sklearn.utils import resample

# ----------------------------
# Konfigurasi
# ----------------------------
INPUT_PATH = "/kaggle/input/sentimentdataset/dataset_gabungan.csv"

# Jika kamu pakai model cased (mis. indobenchmark/indobert-base-p2), set ke False
APPLY_LOWERCASE = True

# Batasi huruf berulang (contoh: "baguuuusss" -> "baguus")
LIMIT_REPEAT_CHARS = True
MAX_REPEAT = 2

# Nama kolom (biarkan None agar ditebak otomatis)
TEXT_COL = None
LABEL_COL = None

# Label yang didukung (akan dinormalisasi ke bentuk ini)
CANON_LABELS = {"positif": "positif", "positive": "positif", "pos": "positif", 'positi': 'positif',
                "negatif": "negatif", "negative": "negatif", "neg": "negatif", 'negartif': 'negatif',
                "netral": "netral", "neutral": "netral", "neu": "netral", 'netr' : 'netral'}

# ----------------------------
# Utilitas
# ----------------------------
def guess_column(df: pd.DataFrame, candidates):
    for c in candidates:
        if c in df.columns:
            return c
    # fallback: pilih kolom bertipe object terpanjang
    obj_cols = [c for c in df.columns if df[c].dtype == "object"]
    return obj_cols[0] if obj_cols else df.columns[0]

url_pattern = re.compile(r"(https?://\S+|www\.\S+)")
mention_pattern = re.compile(r"@\w+")
hashtag_pattern = re.compile(r"#(\w+)")
multi_space_pattern = re.compile(r"\s+")
rt_fw_pattern = re.compile(r"\b(rt|fw|fwd)\b[:]?", flags=re.IGNORECASE)

# Optional: pola khusus yang sering ada di data komentar (hapus segmen "author ... comment")
author_comment_pattern = re.compile(r"author\b.*?\bcomment", flags=re.IGNORECASE|re.DOTALL)

def limit_repeated_chars(text: str, max_repeat: int = 2) -> str:
    return re.sub(r"(.)\1{%d,}" % (max_repeat), r"\1" * max_repeat, text)

class TextPreprocessor(BaseEstimator, TransformerMixin):
    def __init__(self,
                 apply_lowercase=True,
                 limit_repeat=True,
                 max_repeat=2,
                 canon_labels=None):
        self.apply_lowercase = apply_lowercase
        self.limit_repeat = limit_repeat
        self.max_repeat = max_repeat
        self.canon_labels = canon_labels or {}

    def fit(self, X, y=None):
        return self

    def transform(self, X, y=None):
        # pastikan Series + atasi NaN di sini, JANGAN di _clean_text
        texts = pd.Series(X).fillna("").astype(str)
        return texts.apply(self._clean_text)

    def transform_labels(self, y):
        if y is None:
            return None
        labels = pd.Series(y).astype(str)
        return labels.apply(self._normalize_label)

    def _normalize_label(self, x):
        if pd.isna(x):
            return None
        s = str(x).strip().lower()
        return self.canon_labels.get(s, None)

    def _clean_text(self, t: str) -> str:
        if not isinstance(t, str):
            return ""

        # Hapus pola "author ... comment"
        t = author_comment_pattern.sub("", t)

        # Hapus tag HTML / atribut
        t = remove_html_elements(t)

        # Unescape HTML entities
        t = html.unescape(t)

        # Ganti URL dan mention
        t = url_pattern.sub(" <url> ", t)
        t = mention_pattern.sub(" <user> ", t)

        # Hashtag "#kata" -> "kata"
        t = hashtag_pattern.sub(lambda m: f"{m.group(1)}", t)

        # Hapus token RT/FW
        t = rt_fw_pattern.sub(" ", t)

        # Hanya simpan huruf, angka, dan spasi
        t = re.sub(r"[^a-zA-Z0-9\s]", " ", t)

        # Normalisasi whitespace
        t = multi_space_pattern.sub(" ", t).strip()

        # Lowercase jika diinginkan
        if self.apply_lowercase:
            t = t.lower()

        # Batasi huruf berulang
        if self.limit_repeat:
            t = limit_repeated_chars(t, self.max_repeat)

        return t


def remove_html_elements(text: str) -> str:
    if not isinstance(text, str):
        return ""

    # Unescape HTML entities (&amp; -> &, dll)
    text = html.unescape(text)

    # Hapus semua <tag> lengkap
    text = TAG_RE.sub(" ", text)

    # Hapus atribut HTML yang nyangkut sebagai plain text
    text = ATTR_RE.sub(" ", text)

    # Hapus simbol "<" atau ">" sisa
    text = re.sub(r"[<>]", " ", text)

    # Normalkan spasi
    text = re.sub(r"\s+", " ", text).strip()

    return text

# regex: hapus <tag> beserta isinya
TAG_RE = re.compile(r"<[^>]+>")

# regex: hapus atribut-atribut html yang sering nyangkut
ATTR_RE = re.compile(r"\b(class|id|style|role|tabindex|href|src|alt)=[^\s>]+", flags=re.IGNORECASE)

# ----------------------------
# Load
# ----------------------------
path = Path(INPUT_PATH)
if not path.exists():
    raise FileNotFoundError(f"File tidak ditemukan: {path.resolve()}")

df = pd.read_csv(path)

# ----------------------------
# Tentukan kolom teks & label
# ----------------------------
if TEXT_COL is None:
    TEXT_COL = guess_column(df, ["text", "tweet", "content", "sentence", "caption", "judul", "deskripsi"])
if LABEL_COL is None:
    LABEL_COL = guess_column(df, ["label", "sentiment", "polarity", "target", "kelas"])

print(f"Kolom teks terdeteksi : {TEXT_COL}")
print(f"Kolom label terdeteksi: {LABEL_COL}")

# ----------------------------
# Load Preproc
# ----------------------------

preproc = TextPreprocessor(
    apply_lowercase=APPLY_LOWERCASE,
    limit_repeat=LIMIT_REPEAT_CHARS,
    max_repeat=MAX_REPEAT,
    canon_labels=CANON_LABELS
)

# ----------------------------
# Penggunaan Preproc
# ----------------------------
# fit_transform teks
df["text"] = preproc.fit_transform(df[TEXT_COL])
df["sentiment"] = preproc.transform_labels(df[LABEL_COL])

# ----------------------------
# Drop Data jika Text Kosong
# ----------------------------
df = df[df["text"].str.strip().ne("")]

# ----------------------------
# Tampilkan contoh label tak dikenal
# ----------------------------
unknown = df[df["sentiment"].isna()]
print("\nContoh label tak dikenal yang akan dibuang:")
print(unknown[[LABEL_COL]].value_counts())  # tampilkan 10 teratas

# Buang label tak dikenal
before = len(df)
df = df[df["sentiment"].notna()]
dropped_unknown = before - len(df)

# ----------------------------
# Hapus duplikasi (berdasarkan teks bersih)
# ----------------------------
df = df.drop_duplicates(subset=["text"]).reset_index(drop=True)

# ----------------------------
# Ringkasan
# ----------------------------
print("\nRingkasan setelah preprocessing:")
print(f" - Baris total        : {len(df)}")
print(f" - Dibuang label tak dikenal: {dropped_unknown}")
print(" - Distribusi label:")
print(df["sentiment"].value_counts(dropna=False))

# Contoh pratinjau
print("\nContoh 5 baris:")
print(df[[TEXT_COL, "text", LABEL_COL, "sentiment"]].head(5))

# df.to_csv('/content/drive/MyDrive/Machine Learning/Latih Model/bersihhh.csv')

# ----------------------------
# Save Preproc
# ----------------------------

joblib.dump(preproc, "preprocessor.joblib")

# ============================
# PERBAIKAN LABEL BERDASARKAN KATA KUNCI
# ============================

# Definisikan kamus kata kunci untuk tiap label
NEGATIVE_KEYWORDS = {
    # Kata kasar / slang
    "bego", "bodoh", "jelek", "goblok", "bangsat", "kampungan", "tolol",
    "kontol", "kirik", "koplok", "anjing", "babi", "monyet", "belegug",
    "kik", "goblog", "kntl",

    # Kata resmi / formal
    "buruk", "lemah", "rendah", "gagal", "hancur", "rusak", "cacat",
    "jahat", "dusta", "bohong", "fitnah", "korup", "curang", "palsu",
    "salah", "sesat", "kejam", "dendam", "malas", "lambat", "menyakitkan",
    "tercela", "merugikan", "menghina", "melecehkan", "menyesatkan"
}

POSITIVE_KEYWORDS = {
    # Kata umum positif
    "bagus", "hebat", "mantap", "luar biasa", "keren", "canggih",
    "cerdas", "pintar", "senang", "bahagia", "memuaskan", "unggul",
    "sempurna", "berhasil", "luas", "indah"
}

NEUTRAL_KEYWORDS = {
    # Kata netral / umum
    "ok", "oke", "biasa", "lumayan", "standar", "normal", "cukup", "agak"
}

def correct_label(row):
    text = row["text"]
    label = row["sentiment"]

    # cek kata negatif
    if any(word in text for word in NEGATIVE_KEYWORDS):
        return "negatif"
    # cek kata positif
    if any(word in text for word in POSITIVE_KEYWORDS):
        return "positif"
    # cek kata netral
    if any(word in text for word in NEUTRAL_KEYWORDS):
        return "netral"

    # kalau tidak ada aturan yang kena, pakai label asli
    return label

# Terapkan perbaikan
df["sentiment"] = df.apply(correct_label, axis=1)

# Ringkasan distribusi setelah perbaikan
print("\nDistribusi label setelah perbaikan:")
print(df["sentiment"].value_counts())

# Pisahkan tiap kelas
df_negatif = df[df["sentiment"] == "negatif"]
df_positif = df[df["sentiment"] == "positif"]
df_netral  = df[df["sentiment"] == "netral"]

# Tentukan target jumlah (misal samakan dengan kelas netral)
target_count = df_netral.shape[0]

# Oversampling positif & negatif
df_negatif_over = resample(df_negatif,
                           replace=True,
                           n_samples=target_count,
                           random_state=42)

df_positif_over = resample(df_positif,
                           replace=True,
                           n_samples=target_count,
                           random_state=42)

# Gabungkan kembali
df_balanced = pd.concat([df_netral, df_negatif_over, df_positif_over])

print("Distribusi setelah balancing:")
print(df_balanced["sentiment"].value_counts())

# ============================
# VISUALISASI DISTRIBUSI LABEL
# ============================

# ambil distribusi label_clean
label_counts = df_balanced["sentiment"].value_counts()

# -------- Diagram Batang --------
plt.figure(figsize=(6,4))
label_counts.plot(kind="bar", color=["red","green","blue"])
plt.title("Distribusi Sentimen")
plt.xlabel("Label")
plt.ylabel("Jumlah")
plt.xticks(rotation=0)
plt.show()

print('\n')

# -------- Diagram Lingkaran (Pie) --------
plt.figure(figsize=(5,5))
label_counts.plot(kind="pie", autopct='%1.1f%%', startangle=90, colors=["red","green","blue"])
plt.title("Persentase Sentimen")
plt.ylabel("")  # hilangkan label Y
plt.show()

# ============================
# SPLIT DATASET (train/val/test)
# ============================

# ambil teks & label hasil bersih
X = df_balanced["text"].values
y = df_balanced["sentiment"].values

# 1. Bagi train + temp (80%) dan test (20%)
X_train, X_temp, y_train, y_temp = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

# 2. Dari temp (20%), bagi lagi jadi val (10%) + test (10%)
X_val, X_test, y_val, y_test = train_test_split(
    X_temp, y_temp, test_size=0.5, random_state=42, stratify=y_temp
)

# Cek ukuran hasil split
print("Ukuran dataset:")
print(f"Train: {len(X_train)}")
print(f"Validation: {len(X_val)}")
print(f"Test: {len(X_test)}")

# ============================
# FINE-TUNING IndoBERT
# ============================

# pastikan pakai GPU kalau tersedia
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Device:", device)

os.environ["WANDB_API_KEY"] = "009f08e71506e55bdfd282b691a4abee4ac85ff9"
os.environ["WANDB_DISABLED"] = "false"

# ----------------------------
# 1. Tokenizer & Label Encoding
# ----------------------------
MODEL_NAME = "indobenchmark/indobert-base-p1"  # model IndoBERT pre-trained

tokenizer = BertTokenizer.from_pretrained(MODEL_NAME)

# mapping label ke angka
label2id = {"negatif": 0, "netral": 1, "positif": 2}
id2label = {v: k for k, v in label2id.items()}

def encode_labels(labels):
    return [label2id[l] for l in labels]

y_train_enc = encode_labels(y_train)
y_val_enc   = encode_labels(y_val)
y_test_enc  = encode_labels(y_test)

# ----------------------------
# 2. Dataset class
# ----------------------------
class SentimentDataset(Dataset):
    def __init__(self, texts, labels, tokenizer, max_len=128):
        self.texts = texts
        self.labels = labels
        self.tokenizer = tokenizer
        self.max_len = max_len

    def __len__(self):
        return len(self.texts)

    def __getitem__(self, idx):
        text = str(self.texts[idx])
        label = self.labels[idx]

        enc = self.tokenizer(
            text,
            truncation=True,
            padding="max_length",
            max_length=self.max_len,
            return_tensors="pt"
        )

        return {
            "input_ids": enc["input_ids"].squeeze(),
            "attention_mask": enc["attention_mask"].squeeze(),
            "labels": torch.tensor(label, dtype=torch.long)
        }

train_dataset = SentimentDataset(X_train, y_train_enc, tokenizer)
val_dataset   = SentimentDataset(X_val, y_val_enc, tokenizer)
test_dataset  = SentimentDataset(X_test, y_test_enc, tokenizer)

# ----------------------------
# 3. Model
# ----------------------------
model = BertForSequenceClassification.from_pretrained(
    MODEL_NAME,
    num_labels=3,
    id2label=id2label,
    label2id=label2id
).to(device)

# ----------------------------
# 4. Training Arguments
# ----------------------------
training_args = TrainingArguments(
    output_dir="./results",
    per_device_train_batch_size=32,
    per_device_eval_batch_size=32,
    num_train_epochs=5,                   # cukup 10–15, early stopping yang handle
    learning_rate=2e-5,                    # lebih kecil → stabil
    weight_decay=0.05,                     # lebih besar → regularisasi
    warmup_ratio=0.1,                      # 10% step awal dipakai warmup
    logging_dir="./logs",
    logging_steps=500,
    save_total_limit=2,
    eval_strategy="epoch",         # evaluasi setiap epoch
    save_strategy="epoch",         # simpan juga setiap epoch
    load_best_model_at_end=True,
    metric_for_best_model="f1",
    greater_is_better=True
)


# ----------------------------
# 5. Metrics
# ----------------------------

metric_acc = evaluate.load("accuracy")
metric_f1  = evaluate.load("f1")

def compute_metrics(eval_pred):
    logits, labels = eval_pred
    preds = np.argmax(logits, axis=-1)
    acc = metric_acc.compute(predictions=preds, references=labels)
    f1  = metric_f1.compute(predictions=preds, references=labels, average="weighted")
    return {"accuracy": acc["accuracy"], "f1": f1["f1"]}

# ----------------------------
# 6. Trainer
# ----------------------------
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,
    compute_metrics=compute_metrics,
    callbacks=[EarlyStoppingCallback(early_stopping_patience=2)]  # stop kalau 2 epoch tidak membaik
)

# ----------------------------
# 7. Mulai Training
# ----------------------------
trainer.train()

# =============================
# 8. Evaluasi & Simpan Prediksi
# =============================

# hasil prediksi di test set
pred_results = trainer.predict(test_dataset)

# ambil logits → konversi ke label prediksi
pred_logits = pred_results.predictions
pred_labels = np.argmax(pred_logits, axis=1)

# konversi angka ke label teks
pred_text_labels = [id2label[i] for i in pred_labels]
true_text_labels = [id2label[i] for i in y_test_enc]

# gabungkan dengan teks asli
df_test_results = pd.DataFrame({
    "text": X_test,
    "true_label": true_text_labels,
    "predicted_label": pred_text_labels
})

# simpan ke CSV
df_test_results.to_csv("test_predictions.csv", index=False)
print("✅ Hasil prediksi test set sudah disimpan ke test_predictions.csv")

# ============================
# EVALUASI & SIMPAN MODEL
# ============================

# 1. Evaluasi di test set
print("\nEvaluasi di Test Set:")
test_result = trainer.evaluate(test_dataset)
print(test_result)

# 2. Prediksi label test set (opsional, untuk analisis lebih lanjut)
predictions = trainer.predict(test_dataset)
pred_labels = np.argmax(predictions.predictions, axis=-1)

# contoh lihat 10 prediksi pertama
for i in range(10):
    print(f"Teks: {X_test[i]}")
    print(f"Label Asli: {id2label[y_test_enc[i]]} | Prediksi: {id2label[pred_labels[i]]}")
    print("---")

# 3. Simpan model + tokenizer
SAVE_DIR = "./indoBERT-sentiment"

trainer.save_model(SAVE_DIR)
tokenizer.save_pretrained(SAVE_DIR)

print(f"\nModel & tokenizer sudah disimpan ke: {SAVE_DIR}")

# ==========================
# LOAD MODEL & TOKENIZER
# ==========================
MODEL_DIR = "./indoBERT-sentiment"

tokenizer = BertTokenizer.from_pretrained(MODEL_DIR)
model = BertForSequenceClassification.from_pretrained(MODEL_DIR)

device = 0 if torch.cuda.is_available() else -1
sentiment_pipeline = pipeline(
    "text-classification",
    model=model,
    tokenizer=tokenizer,
    device=device
)

# load preprocessor yang sudah disimpan
preproc = joblib.load("preprocessor.joblib")

# ==========================
# FUNGSI PREDIKSI
# ==========================
def predict_text(text):
    if not isinstance(text, str) or text.strip() == "":
        return "EMPTY"
    result = sentiment_pipeline(text, truncation=True, max_length=512)[0]
    return result["label"]

# ==========================
# PREDIKSI FILE 1 (MEDIA SOSIAL)
# ==========================
file1 = pd.read_csv("/kaggle/input/sentimentdataset/gabungan (1).csv")

# Preprocessing caption
file1["caption"] = preproc.transform(file1["caption"])

# Preprocessing comment
file1["comments"] = preproc.transform(file1["comments"])

# drop NaN biar aman
file1 = file1.dropna(subset=["caption", "comments"])

outputs1 = []

for idx, row in file1.iterrows():
    print(f"[File1] Proses baris {idx+1}/{len(file1)}")

    # caption
    caption_text = str(row["caption"]).strip()
    caption_pred = predict_text(caption_text)

    # comments
    comments_text = str(row["comments"]).strip()
    comments_pred_label = predict_text(comments_text)

    outputs1.append({
        "link": row.get("link", ""),             # simpan link medsos
        "caption": caption_text,
        "caption_pred": caption_pred,
        "comments_pred": comments_text,          # simpan teks asli komentar
        "comments_summary": comments_pred_label  # hasil prediksi sentimen komentar
    })

df_out1 = pd.DataFrame(outputs1)
df_out1.to_csv("medsos.csv", index=False, encoding="utf-8-sig")
print("✅ Hasil prediksi file1 sudah disimpan ke medsos.csv")

# ==========================
# PREDIKSI FILE 2 (BERITA)
# ==========================
file2 = pd.read_csv("/kaggle/input/sentimentdataset/berita2 (1).csv")

# Preprocessing judul
file2["judul"] = preproc.transform(file2["judul"])

# Preprocessing tag (✅ perbaikan: tidak menimpa judul)
file2["tag"] = preproc.transform(file2["tag"])

# Preprocessing isi_berita
file2["isi_berita"] = preproc.transform(file2["isi_berita"])

# drop NaN biar aman
file2 = file2.dropna(subset=["judul", "tag", "isi_berita"])

outputs2 = []

for idx, row in file2.iterrows():
    print(f"[File2] Proses baris {idx+1}/{len(file2)}")

    combined_text = f"{row['judul']} {row['tag']} {row['isi_berita']}"
    pred = predict_text(combined_text)

    outputs2.append({
        "link": row.get("link", ""),    # simpan link berita
        "judul": row["judul"],
        "tag": row["tag"],
        "isi_berita": row["isi_berita"],
        "prediction": pred
    })

df_out2 = pd.DataFrame(outputs2)
df_out2.to_csv("berita.csv", index=False, encoding="utf-8-sig")
print("✅ Hasil prediksi file2 sudah disimpan ke berita.csv")