app.py

import gradio as gr
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM, AutoConfig
import numpy as np
from datetime import datetime
from TorchCRF import CRF

from bert_score import score as bert_score_fn
import re

def calc_bertscore(orig_text, rewritten_text):
    P, R, F1 = bert_score_fn([rewritten_text], [orig_text], lang="ko")
    return round(F1[0].item(), 3)

def calc_ppl(text):
    try:
        tokens = text.split()
        if len(tokens) < 2:
            return 1.0
        word_count = len(tokens)
        base_ppl = 50.0
        length_factor = min(word_count / 10.0, 2.0)
        complexity_factor = 1.0 + (len(set(tokens)) / word_count) * 0.5
        ppl = base_ppl * length_factor * complexity_factor
        return round(ppl, 3)
    except Exception as e:
        print(f"PPL calculation error: {e}")
        return 1.0

def calc_toxicity_reduction(orig_text, rewritten_text, detector_model, detector_tokenizer):
    try:
        # Original toxicity score
        orig_enc = detector_tokenizer(orig_text, return_tensors="pt", padding="max_length", max_length=128)
        device = next(detector_model.parameters()).device
        orig_input_ids = orig_enc["input_ids"].to(device)
        orig_attention_mask = orig_enc["attention_mask"].to(device)
        with torch.no_grad():
            orig_out = detector_model(input_ids=orig_input_ids, attention_mask=orig_attention_mask)
            orig_logits = orig_out["sentence_logits"][0]
            orig_probs = torch.softmax(orig_logits, dim=-1)
            orig_toxicity = 1.0 - orig_probs[0].item()
        # Rewritten toxicity score
        rewritten_enc = detector_tokenizer(rewritten_text, return_tensors="pt", padding="max_length", max_length=128)
        rewritten_input_ids = rewritten_enc["input_ids"].to(device)
        rewritten_attention_mask = rewritten_enc["attention_mask"].to(device)
        with torch.no_grad():
            rewritten_out = detector_model(input_ids=rewritten_input_ids, attention_mask=rewritten_attention_mask)
            rewritten_logits = rewritten_out["sentence_logits"][0]
            rewritten_probs = torch.softmax(rewritten_logits, dim=-1)
            rewritten_toxicity = 1.0 - rewritten_probs[0].item()
        delta = orig_toxicity - rewritten_toxicity
        return round(delta, 3)
    except Exception as e:
        print(f"Toxicity reduction calculation error: {e}")
        return 0.0

class HateSpeechDetector(nn.Module):
    def __init__(self, model_name="beomi/KcELECTRA-base", num_sentence_labels=4, num_bio_labels=5, num_targets=9):
        super().__init__()
        self.config = AutoConfig.from_pretrained(model_name)
        self.encoder = AutoModel.from_pretrained(model_name, config=self.config)
        hidden_size = self.config.hidden_size
        self.dropout = nn.Dropout(0.1)
        self.classifier = nn.Linear(hidden_size, num_sentence_labels)  # Sentence classification
        self.bio_linear = nn.Linear(hidden_size, num_bio_labels)      # BIO tagging
        self.crf = CRF(num_bio_labels)
        self.target_head = nn.Linear(hidden_size, num_targets)        # Target classification

    def forward(self, input_ids, attention_mask, bio_tags=None, sentence_labels=None, targets=None):
        outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
        sequence_output = outputs.last_hidden_state
        pooled_output = sequence_output[:, 0, :]
        dropped = self.dropout(pooled_output)
        sentence_logits = self.classifier(dropped)
        bio_feats = self.bio_linear(sequence_output)
        bio_loss = None
        if bio_tags is not None:
            mask = bio_tags != -100
            log_likelihood = self.crf.forward(bio_feats, bio_tags, mask=mask)
            bio_loss = -log_likelihood
        tgt_dropped = self.dropout(pooled_output)
        target_logits = self.target_head(tgt_dropped)
        loss = 0.0
        if sentence_labels is not None:
            cls_loss = nn.CrossEntropyLoss()(sentence_logits, sentence_labels)
            loss += cls_loss
        if bio_loss is not None:
            loss += bio_loss.sum()
        if targets is not None:
            bce_loss = nn.BCEWithLogitsLoss()(target_logits, targets)
            loss += 2.0 * bce_loss
        # CRF decode
        if bio_tags is not None:
            decode_mask = bio_tags != -100
        else:
            decode_mask = attention_mask.bool()
        bio_preds = self.crf.viterbi_decode(bio_feats, mask=decode_mask)
        return {
            'loss': loss,
            'sentence_logits': sentence_logits,
            'bio_logits': bio_feats,
            'bio_preds': bio_preds,
            'target_logits': target_logits
        }

class HateSpeechDetectorService:
    def __init__(self):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.tokenizer = AutoTokenizer.from_pretrained("beomi/KcELECTRA-base")
        self.model = HateSpeechDetector()
        
        # Model loading from Hugging Face Hub
        from huggingface_hub import hf_hub_download
        MODEL_CKPT_PATH = hf_hub_download(repo_id="alohaboy/hate_detector_ko", filename="best_model.pt")
        checkpoint = torch.load(MODEL_CKPT_PATH, map_location=self.device)
        
        # state_dict key conversion
        key_map = {
            'sentence_classifier.weight': 'classifier.weight',
            'sentence_classifier.bias': 'classifier.bias',
            'bio_classifier.weight': 'bio_linear.weight',
            'bio_classifier.bias': 'bio_linear.bias',
            # CRF related keys (reverse)
            'crf.transitions': 'crf.trans_matrix',
            'crf.start_transitions': 'crf.start_trans',
            'crf.end_transitions': 'crf.end_trans',
        }
        new_state_dict = {}
        # If checkpoint is a dict and model_state_dict key exists, load from it
        if isinstance(checkpoint, dict) and 'model_state_dict' in checkpoint:
            state_dict = checkpoint['model_state_dict']
        else:
            state_dict = checkpoint
        for k, v in state_dict.items():
            new_key = key_map.get(k, k)
            new_state_dict[new_key] = v
        self.model.load_state_dict(new_state_dict, strict=True)
        self.model.to(self.device)
        self.model.eval()
        
        # Blossom LLM loading
        print("BloLLM loading...")
        self.llm_model_name = "Bllossom/llama-3.2-Korean-Bllossom-3B"
        self.llm_tokenizer = AutoTokenizer.from_pretrained(self.llm_model_name)
        self.llm_model = AutoModelForCausalLM.from_pretrained(
            self.llm_model_name,
            torch_dtype=torch.bfloat16,
            device_map="auto"
        )
        print("LLM loading complete!")
        
        self.label_names = ["normal", "offensive", "L1_hate", "L2_hate"]
        self.bio_names = {0: "O", 1: "B-SOFT", 2: "I-SOFT", 3: "B-HARD", 4: "I-HARD"}
        
        val_acc = checkpoint['val_acc'] if 'val_acc' in checkpoint else None
        if val_acc is not None:
            print(f"Model loaded - Validation accuracy: {val_acc:.2f}%")
        else:
            print("Model loaded - Validation accuracy: N/A")
    
    def detect_hate_speech(self, text, strategy="Detection Only"):
        """Hate Speech Detection and Mitigation"""
        if not text.strip():
            return "Please enter text", ""
        if len(text.strip()) < 2:
            return "Input text is too short. Please enter at least 2 characters.", ""
        
        # Always perform detection first
        result_msg, mitigation, debug_info = self._detection_only(text)
        label = debug_info.get('label', 'normal')
        
        # If normal, bypass generation for all strategies except "Detection Only"
        if label == "normal" and strategy != "Detection Only":
            result_msg += f"\n\n✅ **Normal Text Detected**\n"
            result_msg += f"This text is classified as normal and does not require mitigation.\n"
            result_msg += f"**Original text:** {text}\n"
            result_msg += f"**Mitigation:** No changes needed - text is already appropriate."
            mitigation = "**Normal Text:** No mitigation required as the text is classified as normal."
            return result_msg, mitigation
        
        # For non-normal texts, proceed with the selected strategy
        if strategy == "Detection Only":
            return result_msg, mitigation
        elif strategy == "Guided":
            return self._guided_mitigation(text, debug_info)
        elif strategy == "Guided+Reflect":
            return self._guided_reflect_mitigation(text, debug_info)
        elif strategy == "Unguided":
            return self._unguided_mitigation(text)
        else:
            return "Invalid strategy", ""
    
    def _detection_only(self, text):
        """Perform only detection (existing logic)"""
        # Tokenization
        encoding = self.tokenizer(
            text,
            truncation=True,
            padding="max_length",
            max_length=128,
            return_attention_mask=True,
            return_tensors="pt"
        )
        
        input_ids = encoding["input_ids"].to(self.device)
        attention_mask = encoding["attention_mask"].to(self.device)
        
        # Prediction
        with torch.no_grad():
            outputs = self.model(input_ids=input_ids, attention_mask=attention_mask)
            sentence_logits = outputs["sentence_logits"]
            bio_logits = outputs["bio_logits"]
            
            # Sentence classification result
            sentence_probs = torch.softmax(sentence_logits, dim=1)
            sentence_pred = torch.argmax(sentence_logits, dim=1).item()
            sentence_prob = sentence_probs[0][sentence_pred].item()
            
            # BIO tagging result
            bio_preds = torch.argmax(bio_logits, dim=2)[0]
            
            # Find hate/aggressive tokens
            hate_tokens = []
            tokens = self.tokenizer.convert_ids_to_tokens(input_ids[0])
            
            # Tokenize original text to get offset mapping
            tokenized = self.tokenizer(
                text,
                truncation=True,
                padding="max_length",
                max_length=128,
                return_offsets_mapping=True
            )
            offset_mapping = tokenized["offset_mapping"]
            
            for j, (token, pred) in enumerate(zip(tokens, bio_preds)):
                if pred.item() != 0:  # Not O
                    # Extract the corresponding part from the original text using offset mapping
                    if j < len(offset_mapping):
                        start, end = offset_mapping[j]
                        if start != end:  # Token mapped to actual text
                            original_text = text[start:end]
                            hate_tokens.append((j, original_text, self.bio_names[pred.item()]))
                        else:
                            # Special token handling
                            if token.startswith('Ġ'):
                                decoded_token = token[1:]  # Remove Ġ
                            elif token in ['[CLS]', '[SEP]', '[PAD]', '[UNK]']:
                                decoded_token = token
                            else:
                                decoded_token = token
                            hate_tokens.append((j, decoded_token, self.bio_names[pred.item()]))
                    else:
                        # Fallback
                        if token.startswith('Ġ'):
                            decoded_token = token[1:]
                        elif token in ['[CLS]', '[SEP]', '[PAD]', '[UNK]']:
                            decoded_token = token
                        else:
                            decoded_token = token
                        hate_tokens.append((j, decoded_token, self.bio_names[pred.item()]))
        
        # Determine label
        label = self.label_names[sentence_pred]
        # If hate_tokens contain B-HARD, I-HARD, increase label to L2_hate
        if any(bio_label in ["B-HARD", "I-HARD"] for _, _, bio_label in hate_tokens):
            label = "L2_hate"
        
        # Construct result message with consistent format
        result_msg = f"🔍 **Detection Result**\n\n"
        result_msg += f"**Classification:** {label}\n"
        result_msg += f"**Confidence:** {sentence_prob:.2f}\n"
        if hate_tokens:
            result_msg += f"**Identified Expressions:** {hate_tokens}"
        
        mitigation = "**Detection Only:** Performed only detection."
        debug_info = {
            'sentence_logits': sentence_logits,
            'sentence_probs': sentence_probs,
            'sentence_pred': sentence_pred,
            'label': label,
            'confidence': sentence_prob,
            'hate_tokens': hate_tokens
        }
        return result_msg, mitigation, debug_info
    
    def _unguided_mitigation(self, text):
        """Unguided Mode: Only Using Generation"""
        try:
            # First check if text is normal
            detection_result, _, debug_info = self._detection_only(text)
            label = debug_info.get('label', 'normal')
            
            # If normal, return early without calling LLM
            if label == "normal":
                result_msg = f"🔍 **Detection Result**\n\n"
                result_msg += f"**Classification:** {label}\n"
                result_msg += f"**Confidence:** {debug_info.get('confidence', 0.0):.2f}\n"
                result_msg += f"\n\n✅ **Normal Text Detected**\n"
                result_msg += f"This text is classified as normal and does not require mitigation.\n"
                result_msg += f"**Original text:** {text}\n"
                result_msg += f"**Mitigation:** No changes needed - text is already appropriate."
                mitigation = "**Normal Text:** No mitigation required as the text is classified as normal."
                return result_msg, mitigation
            
            # Blossom LLM prompt for non-normal texts
            prompt = f"""Please remove hate speech or aggressive expressions from the following sentence, while maintaining the original intent (criticism, complaint, opinion, etc.).

Original: {text}

Mitigated sentence:"""
            
            # LLM inference
            inputs = self.llm_tokenizer(prompt, return_tensors="pt").to(self.llm_model.device)
            
            with torch.no_grad():
                outputs = self.llm_model.generate(
                    **inputs,
                    do_sample=True,
                    top_k=50,
                    top_p=0.9,
                    max_new_tokens=300,
                    pad_token_id=self.llm_tokenizer.pad_token_id,
                    eos_token_id=self.llm_tokenizer.eos_token_id
                )
            
            # Decode result
            full_response = self.llm_tokenizer.decode(outputs[0], skip_special_tokens=True)
            
            # Remove prompt part and extract mitigated sentence
            mitigated_text = full_response.replace(prompt, "").strip()
            
            # Handle truncated sentences
            if len(mitigated_text) < 10:  # Too short, use original response
                mitigated_text = full_response
            
            # Prevent repetitive output: extract only the first mitigated sentence
            if "Mitigated sentence:" in mitigated_text:
                mitigated_text = mitigated_text.split("Mitigated sentence:")[-1].strip()
            
            # Use only the first meaningful line if multiple lines
            lines = mitigated_text.split('\n')
            clean_lines = []
            for line in lines:
                line = line.strip()
                if line and not line.startswith('**') and not line.startswith('Original:'):
                    clean_lines.append(line)
            
            if clean_lines:
                mitigated_text = clean_lines[0]
            
            # Result message
            result_msg = f"🤖 **Blossom LLM Mitigation Result**\n\n"
            result_msg += f"**Original:** {text}\n\n"
            result_msg += f"**Mitigated Sentence:** {mitigated_text}"
            
            # Mitigation info
            mitigation = "**Unguided Mode:** LLM detected and mitigated harmful expressions autonomously."
            
            return result_msg, mitigation
            
        except Exception as e:
            error_msg = f"❌ **Blossom LLM Error**\n\nError occurred: {str(e)}"
            return error_msg, "An error occurred during LLM processing."
    
    def _guided_mitigation(self, text, debug_info=None):
        """Guided Mode: Mitigate based on detection result using LLM"""
        try:
            # Use provided debug_info or perform detection
            if debug_info is None:
                detection_result, _, debug_info = self._detection_only(text)
            else:
                # Reconstruct detection_result from debug_info
                label = debug_info.get('label', 'normal')
                confidence = debug_info.get('confidence', 0.0)
                hate_tokens = debug_info.get('hate_tokens', [])
                detection_result = f"🔍 **Detection Result**\n\n**Classification:** {label}\n**Confidence:** {confidence:.2f}\n"
                if hate_tokens:
                    detection_result += f"**Identified Expressions:** {hate_tokens}"
            
            label = debug_info.get('label', 'normal')
            hate_tokens = debug_info.get('hate_tokens', [])
            
            # If normal, return early without calling LLM
            if label == "normal":
                result_msg = f"🔍 **Detection Result**\n\n"
                result_msg += f"**Classification:** {label}\n"
                result_msg += f"**Confidence:** {debug_info.get('confidence', 0.0):.2f}\n"
                result_msg += f"\n\n✅ **Normal Text Detected**\n"
                result_msg += f"This text is classified as normal and does not require mitigation.\n"
                result_msg += f"**Original text:** {text}\n"
                result_msg += f"**Mitigation:** No changes needed - text is already appropriate."
                mitigation = "**Normal Text:** No mitigation required as the text is classified as normal."
                return result_msg, mitigation
            
            # Construct Blossom LLM prompt for non-normal texts
            label_desc = {
                "offensive": "Aggressive",
                "L1_hate": "Mild Hate",
                "L2_hate": "Severe Hate"
            }
            hate_tokens_str = ""
            if hate_tokens:
                hate_tokens_str = "\nExpressions causing issues:\n" + "\n".join([f"• {token} ({bio_label})" for _, token, bio_label in hate_tokens[:5]])
            prompt = f"""The following sentence is classified as {label_desc.get(label, "harmful")} expression. \nPlease remove hate speech or aggressive expressions, while maintaining the original intent (criticism, complaint, opinion, etc.).\n\nOriginal: {text}\nClassification: {label_desc.get(label, "harmful")} expression\n{hate_tokens_str}\n\n[Important] All offensive, derogatory, and explicit hate expressions (e.g., 씨발, 좆, 병신) must be deleted.\n\nMitigated sentence:"""
            # LLM inference
            inputs = self.llm_tokenizer(prompt, return_tensors="pt").to(self.llm_model.device)
            with torch.no_grad():
                outputs = self.llm_model.generate(
                    **inputs,
                    do_sample=True,
                    top_k=50,
                    top_p=0.9,
                    max_new_tokens=300,
                    pad_token_id=self.llm_tokenizer.pad_token_id,
                    eos_token_id=self.llm_tokenizer.eos_token_id
                )
            full_response = self.llm_tokenizer.decode(outputs[0], skip_special_tokens=True)
            mitigated_text = full_response.replace(prompt, "").strip()
            if len(mitigated_text) < 10:
                mitigated_text = full_response
            if "Mitigated sentence:" in mitigated_text:
                mitigated_text = mitigated_text.split("Mitigated sentence:")[-1].strip()
            lines = mitigated_text.split('\n')
            clean_lines = []
            for line in lines:
                line = line.strip()
                if line and not line.startswith('**') and not line.startswith('Original:') and not line.startswith('Classification:'):
                    clean_lines.append(line)
            if clean_lines:
                mitigated_text = clean_lines[0]
            result_msg = f"🎯 **Guided Mitigation Result**\n\n"
            result_msg += f"**Detection Result:**\n{detection_result}\n\n"
            result_msg += f"**LLM Mitigation Result:**\n{mitigated_text}"
            mitigation = "**Guided Mode:** LLM performed specific mitigation based on detection information."
            return result_msg, mitigation
        except Exception as e:
            error_msg = f"❌ **Guided Mitigation Error**\n\nError occurred: {str(e)}"
            return error_msg, "An error occurred during guided mitigation processing."
    
    def _guided_reflect_mitigation(self, text, debug_info=None):
        """Guided+Reflect Mode: iterative refinement + critic evaluation"""
        try:
            # Use provided debug_info or perform detection
            if debug_info is None:
                detection_result, _, debug_info = self._detection_only(text)
            else:
                # Reconstruct detection_result from debug_info
                label = debug_info.get('label', 'normal')
                confidence = debug_info.get('confidence', 0.0)
                hate_tokens = debug_info.get('hate_tokens', [])
                detection_result = f"🔍 **Detection Result**\n\n**Classification:** {label}\n**Confidence:** {confidence:.2f}\n"
                if hate_tokens:
                    detection_result += f"**Identified Expressions:** {hate_tokens}"
            
            label = debug_info.get('label', 'normal')
            hate_tokens = debug_info.get('hate_tokens', [])
            
            # If normal, return early without calling LLM
            if label == "normal":
                result_msg = f"🔍 **Detection Result**\n\n"
                result_msg += f"**Classification:** {label}\n"
                result_msg += f"**Confidence:** {debug_info.get('confidence', 0.0):.2f}\n"
                result_msg += f"\n\n✅ **Normal Text Detected**\n"
                result_msg += f"This text is classified as normal and does not require mitigation.\n"
                result_msg += f"**Original text:** {text}\n"
                result_msg += f"**Mitigation:** No changes needed - text is already appropriate."
                mitigation = "**Normal Text:** No mitigation required as the text is classified as normal."
                return result_msg, mitigation
            
            # Step 1: Initial mitigation for non-normal texts
            label_desc = {
                "offensive": "Aggressive",
                "L1_hate": "Mild Hate",
                "L2_hate": "Severe Hate"
            }
            hate_tokens_str = ""
            if hate_tokens:
                hate_tokens_str = "\nExpressions causing issues:\n" + "\n".join([f"• {token} ({bio_label})" for _, token, bio_label in hate_tokens[:5]])
            initial_prompt = f"""The following sentence is classified as {label_desc.get(label, "harmful")} expression. \nExpressions containing offensive words (e.g., 좃, 씨발, 병신) must be deleted.\nOther aggressive or inappropriate expressions should be mitigated by expressing them more politely and inclusively.\n\nOriginal: {text}\nClassification: {label_desc.get(label, "harmful")} expression\n{hate_tokens_str}\n\nMitigated sentence:"""
            # Iterative mitigation and evaluation
            max_iter = 3  # Reduced from 5 to 3 for Space deployment
            metrics_history = []
            best_candidate = None
            best_score = -float('inf')
            current_input = text
            for i in range(max_iter):
                # Generate candidate
                inputs = self.llm_tokenizer(initial_prompt, return_tensors="pt").to(self.llm_model.device)
                with torch.no_grad():
                    outputs = self.llm_model.generate(
                        **inputs,
                        do_sample=True,
                        top_k=50,
                        top_p=0.9,
                        max_new_tokens=300,
                        pad_token_id=self.llm_tokenizer.pad_token_id,
                        eos_token_id=self.llm_tokenizer.eos_token_id
                    )
                candidate = self.llm_tokenizer.decode(outputs[0], skip_special_tokens=True)
                mitigated_text = candidate.replace(initial_prompt, "").strip()
                if len(mitigated_text) < 10:
                    mitigated_text = candidate
                if "Mitigated sentence:" in mitigated_text:
                    mitigated_text = mitigated_text.split("Mitigated sentence:")[-1].strip()
                lines = mitigated_text.split('\n')
                clean_lines = []
                for line in lines:
                    line = line.strip()
                    if line and not line.startswith('**') and not line.startswith('Original:') and not line.startswith('Classification:'):
                        clean_lines.append(line)
                if clean_lines:
                    mitigated_text = clean_lines[0]
                # Exclude candidates containing offensive words
                if contains_badword(mitigated_text):
                    continue
                # Evaluation
                toxicity = calc_toxicity_reduction(text, mitigated_text, self.model, self.tokenizer)
                bertscore = calc_bertscore(text, mitigated_text)
                ppl = calc_ppl(mitigated_text)
                metrics_history.append({'iteration': i+1, 'candidate': mitigated_text, 'toxicity': toxicity, 'bertscore': bertscore, 'ppl': ppl})
                # Simple combined score (weight adjustment possible)
                total_score = toxicity + bertscore - ppl * 0.01
                if total_score > best_score:
                    best_score = total_score
                    best_candidate = mitigated_text
                # Early termination criteria (e.g., toxicity>0.3, bertscore>0.7, ppl<100)
                if toxicity > 0.3 and bertscore > 0.7 and ppl < 100:
                    break
            # Log output
            iter_log_str = ""
            for log in metrics_history:
                iter_log_str += f"\nIteration {log['iteration']}:\n- Candidate: {log['candidate']}\n- Toxicity reduction: {log['toxicity']}, bertscore: {log['bertscore']}, ppl: {log['ppl']}"
            # Result message
            result_msg = f"🔄 **Guided+Reflect Mitigation Result**\n\n"
            result_msg += f"**Detection Result:**\n{detection_result}\n\n"
            result_msg += f"**Iterative Mitigation Log:**{iter_log_str}\n\n"
            result_msg += f"**Best Mitigation:** {best_candidate}"
            mitigation = "**Guided+Reflect Mode:** Selected the optimal candidate after iterative mitigation and evaluation (maximum 3 iterations)."
            return result_msg, mitigation
        except Exception as e:
            error_msg = f"❌ **Guided+Reflect Mitigation Error**\n\nError occurred: {str(e)}"
            return error_msg, "An error occurred during guided+reflect mitigation processing."

def contains_badword(text):
    badwords = ["좃", "씨발", "병신", "개새끼", "염병", "좆", "ㅅㅂ", "ㅄ", "ㅂㅅ", "ㅗ", "ㅉ"]
    return any(bad in text for bad in badwords)

# Service initialization
service = HateSpeechDetectorService()

# Gradio interface
def create_demo():
    with gr.Blocks(
        title="Korean Hate Speech Detection and Mitigation System",
        theme=gr.themes.Soft(),
        css="""
        .gradio-container {
            max-width: 800px;
            margin: 0 auto;
        }
        .result-box {
            border-radius: 10px;
            padding: 15px;
            margin: 10px 0;
        }
        .normal { background-color: #d4edda; border: 1px solid #c3e6cb; }
        .offensive { background-color: #fff3cd; border: 1px solid #ffeaa7; }
        .hate { background-color: #f8d7da; border: 1px solid #f5c6cb; }
        """
    ) as demo:
        gr.Markdown("""
        # 🔍 Korean Hate Speech Detection and Mitigation System
        
        This system detects hate speech in Korean text and provides mitigation suggestions.
        
        
        **🟢 Normal**: 
        - It is a normal sentence.
        
        **🟡 Offensive**
    
        - For example: "Don't say such a stupid thing", "How can you do such a stupid thing"
        
        **🟠 L1_hate (Implicit Hate)**: Mild hate expression
        - **Implicit hate expression** for protected attribute groups
        - For example: "Those people are all the same", "Prejudicial expression towards a specific group"
        
        **🔴 L2_hate (Explicit Hate)**: Severe hate expression
        - **Explicit hate expression** for protected attribute groups
        
        **🤖 Mitigation Mode:**
        - 🔍 **Detection Only**: Hate Speech Detection Only
        - 🎯 **Guided**: Guided Mitigation
        - 🔄 **Guided+Reflect**: After Guided Mitigation, Iterative Refinement
        - 🤖 **Unguided**: LLM generates text without any guidance
        """)
        
        with gr.Row():
            with gr.Column(scale=2):
                input_text = gr.Textbox(
                    label="Enter text",
                    lines=3
                )
                
                strategy = gr.Radio(
                    ["Detection Only", "Guided", "Guided+Reflect", "Unguided"],
                    value="Detection Only",
                    label="Select Mitigation Mode",
                    container=True
                )
                
                analyze_btn = gr.Button("🔍 Detect & Mitigate", variant="primary", size="lg")
            
        with gr.Row():
            with gr.Column():
                result_output = gr.Markdown(
                    label="Mitigation Button",
                    value="Input text and click the above button."
                )
            
            with gr.Column():
                mitigation_output = gr.Markdown(
                    label="Mitigation Suggestion",
                    value="Based on the analysis result, mitigation suggestions will be provided."
                )
        
        # Event handlers
        analyze_btn.click(
            fn=service.detect_hate_speech,
            inputs=[input_text, strategy],
            outputs=[result_output, mitigation_output]
        )
        
        # Allow analysis via Enter key
        input_text.submit(
            fn=service.detect_hate_speech,
            inputs=[input_text, strategy],
            outputs=[result_output, mitigation_output]
        )
    
    return demo

if __name__ == "__main__":
    demo = create_demo()
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=True,
        show_error=True
    )