app.py

import torch
import torch.nn as nn
from transformers import AutoModel, AutoTokenizer
import gradio as gr
from sklearn.preprocessing import LabelEncoder
import pandas as pd

# ===== Load Label Encoder =====
df = pd.read_csv("Dataset_new.csv",delimiter=";")
le = LabelEncoder()
le.fit(df["label"])

# ===== Define Model Class =====
class IndoBERTClassifier(nn.Module):
    def __init__(self, model_name, num_labels):
        super(IndoBERTClassifier, self).__init__()
        self.bert = AutoModel.from_pretrained(model_name)
        self.dropout = nn.Dropout(0.3)
        self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels)

    def forward(self, input_ids, attention_mask, token_type_ids=None):
        outputs = self.bert(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids
        )
        pooled = outputs.last_hidden_state[:, 0]
        pooled = self.dropout(pooled)
        logits = self.classifier(pooled)
        return logits

# ===== Load Model and Tokenizer =====
tokenizer = AutoTokenizer.from_pretrained("indobenchmark/indobert-base-p1")
model = IndoBERTClassifier("indobenchmark/indobert-base-p1", num_labels=5)
model.load_state_dict(torch.load("pytorch_model.bin", map_location=torch.device("cpu")))
model.eval()

# ===== Prediction Function =====
def predict(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
    with torch.no_grad():
        logits = model(
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            token_type_ids=inputs.get("token_type_ids")
        )
        pred = torch.argmax(logits, dim=1).item()
        label = le.inverse_transform([pred])[0]
    return f"🚨 Kategori Deteksi:\n\n📌 {label} (Label {pred})"

# ===== Gradio UI =====
with gr.Blocks() as demo:
    gr.Markdown("## 🤖 Deteksi Spam Penipuan Berbahasa Indonesia")
    gr.Markdown("Masukkan kalimat pesan yang ingin diperiksa apakah termasuk penipuan, permintaan data diri, tautan mencurigakan, atau tawaran kerja palsu.")

    with gr.Row():
        input_text = gr.Textbox(lines=3, placeholder="Contoh: Selamat! Anda mendapatkan hadiah. Klik link ini.", label="💬 Masukkan Kalimat")
    with gr.Row():
        output_text = gr.Textbox(label="📤 Hasil Klasifikasi")

    run_button = gr.Button("🔍 Deteksi")
    run_button.click(fn=predict, inputs=input_text, outputs=output_text)

demo.launch()