chat.py

"""
Interactive Chat Interface for Testing Fine-tuned Japanese Counseling Model
"""

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import os
import warnings
from datetime import datetime
import json

warnings.filterwarnings('ignore')

class CounselorChatInterface:
    def __init__(self, model_path: str = "./merged_counselor_model"):
        """
        Initialize the chat interface with the fine-tuned model
        
        Args:
            model_path: Path to the fine-tuned model
        """
        self.model_path = model_path
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        
        print("="*80)
        print("🎌 Japanese Counseling Model Chat Interface")
        print("="*80)
        print(f"📍 Device: {self.device}")
        
        if self.device.type == "cuda":
            print(f"   GPU: {torch.cuda.get_device_name(0)}")
            print(f"   Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB")
        
        self.load_model()
        self.conversation_history = []
        
    def load_model(self):
        """Load the fine-tuned model and tokenizer"""
        print(f"\n🤖 Loading model from {self.model_path}...")
        
        try:
            # Load tokenizer
            self.tokenizer = AutoTokenizer.from_pretrained(
                self.model_path,
                local_files_only=True
            )
            
            # Set padding token if not set
            if self.tokenizer.pad_token is None:
                self.tokenizer.pad_token = self.tokenizer.eos_token
            
            # Load model
            self.model = AutoModelForCausalLM.from_pretrained(
                self.model_path,
                torch_dtype=torch.float16 if self.device.type == "cuda" else torch.float32,
                device_map="auto" if self.device.type == "cuda" else None,
                local_files_only=True,
                trust_remote_code=True
            )
            
            self.model.eval()
            print("✅ Model loaded successfully!")
            
        except Exception as e:
            print(f"❌ Error loading model: {e}")
            print("Trying alternative loading method...")
            
            # Try loading with base tokenizer
            try:
                self.tokenizer = AutoTokenizer.from_pretrained("gpt2")
                if self.tokenizer.pad_token is None:
                    self.tokenizer.pad_token = self.tokenizer.eos_token
                
                self.model = AutoModelForCausalLM.from_pretrained(
                    self.model_path,
                    torch_dtype=torch.float16 if self.device.type == "cuda" else torch.float32,
                    local_files_only=True
                )
                self.model = self.model.to(self.device)
                self.model.eval()
                print("✅ Model loaded with fallback tokenizer!")
            except Exception as e2:
                print(f"❌ Failed to load model: {e2}")
                raise
    
    def generate_response(self, user_input: str, 
                         temperature: float = 0,
                         max_length: int = 200,
                         use_context: bool = True) -> str:
        """
        Generate a counseling response
        
        Args:
            user_input: User's message
            temperature: Generation temperature (0.1-1.0)
            max_length: Maximum response length
            use_context: Whether to use conversation history
            
        Returns:
            Generated response
        """
        # Format the prompt
        if use_context and len(self.conversation_history) > 0:
            # Include recent context
            context = "\n".join(self.conversation_history[-4:])  # Last 2 exchanges
            prompt = f"""### Instruction:
あなたは思いやりのある心理カウンセラーです。
クライアントの感情を理解し、共感的で支援的な応答を提供してください。

### Context:
{context}

### Input:
{user_input}

### Response:
"""
        else:
            prompt = f"""### Instruction:
あなたは思いやりのある心理カウンセラーです。
クライアントの感情を理解し、共感的で支援的な応答を提供してください。

### Input:
{user_input}

### Response:
"""
        
        # Tokenize
        inputs = self.tokenizer(
            prompt,
            return_tensors="pt",
            truncation=True,
            max_length=512
        )
        
        if self.device.type == "cuda":
            inputs = {k: v.cuda() for k, v in inputs.items()}
        
        # Generate
        try:
            with torch.no_grad():
                with torch.cuda.amp.autocast() if self.device.type == "cuda" else torch.autocast("cpu"):
                    outputs = self.model.generate(
                        **inputs,
                        max_new_tokens=max_length,
                        temperature=temperature,
                        do_sample=True,
                        top_p=0.9,
                        top_k=50,
                        repetition_penalty=1.1,
                        pad_token_id=self.tokenizer.pad_token_id,
                        eos_token_id=self.tokenizer.eos_token_id
                    )
            
            # Decode
            full_response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
            
            # Extract only the response part
            if "### Response:" in full_response:
                response = full_response.split("### Response:")[-1].strip()
            else:
                response = full_response[len(prompt):].strip()
            
            return response
            
        except Exception as e:
            print(f"Error generating response: {e}")
            return "申し訳ございません。応答の生成中にエラーが発生しました。"
    
    def chat(self):
        """Start interactive chat session"""
        print("\n" + "="*80)
        print("💬 チャットを開始します (Chat session started)")
        print("="*80)
        print("Commands:")
        print("  /quit or /exit - 終了 (Exit)")
        print("  /clear - 会話履歴をクリア (Clear conversation history)")
        print("  /save - 会話を保存 (Save conversation)")
        print("  /temp <value> - 温度パラメータを設定 (Set temperature, e.g., /temp 0.8)")
        print("  /context on/off - コンテキスト使用の切り替え (Toggle context usage)")
        print("-"*80)
        
        temperature = 0.1
        use_context = True
        
        while True:
            try:
                # Get user input
                user_input = input("\n👤 You: ").strip()
                
                # Check for commands
                if user_input.lower() in ['/quit', '/exit', '/q']:
                    print("\n👋 さようなら！(Goodbye!)")
                    break
                
                elif user_input.lower() == '/clear':
                    self.conversation_history = []
                    print("✅ 会話履歴をクリアしました (Conversation history cleared)")
                    continue
                
                elif user_input.lower() == '/save':
                    self.save_conversation()
                    continue
                
                elif user_input.lower().startswith('/temp'):
                    try:
                        temperature = float(user_input.split()[1])
                        temperature = 0.1 # max(0.1, min(, temperature))
                        print(f"✅ Temperature set to {temperature}")
                    except:
                        print("❌ Invalid temperature. Use: /temp 0.7")
                    continue
                
                elif user_input.lower().startswith('/context'):
                    try:
                        setting = user_input.split()[1].lower()
                        use_context = setting == 'on'
                        print(f"✅ Context {'enabled' if use_context else 'disabled'}")
                    except:
                        print("❌ Use: /context on or /context off")
                    continue
                
                elif user_input.startswith('/'):
                    print("❌ Unknown command")
                    continue
                
                # Generate response
                print("\n🤖 Counselor: ", end="", flush=True)
                response = self.generate_response(
                    user_input, 
                    temperature=temperature,
                    use_context=use_context
                )
                print(response)
                
                # Add to history
                self.conversation_history.append(f"Client: {user_input}")
                self.conversation_history.append(f"Counselor: {response}")
                
            except KeyboardInterrupt:
                print("\n\n👋 さようなら！(Goodbye!)")
                break
            except Exception as e:
                print(f"\n❌ Error: {e}")
                continue
    
    def save_conversation(self):
        """Save the conversation to a file"""
        if not self.conversation_history:
            print("❌ No conversation to save")
            return
        
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        filename = f"conversation_{timestamp}.json"
        
        conversation_data = {
            "timestamp": timestamp,
            "model_path": self.model_path,
            "conversation": self.conversation_history
        }
        
        with open(filename, 'w', encoding='utf-8') as f:
            json.dump(conversation_data, f, ensure_ascii=False, indent=2)
        
        print(f"✅ Conversation saved to {filename}")
    
    def test_responses(self):
        """Test the model with predefined inputs"""
        print("\n" + "="*80)
        print("🧪 Testing Model Responses")
        print("="*80)
        
        test_inputs = [
            "こんにちは。最近ストレスを感じています。",
            "仕事がうまくいかなくて悩んでいます。",
            "人間関係で困っています。どうすればいいでしょうか。",
            "将来が不安で眠れません。",
            "自分に自信が持てません。",
            "家族との関係で悩んでいます。",
            "毎日が辛いです。",
            "誰にも相談できません。"
        ]
        
        print("\nTesting with different temperature settings:\n")
        
        for temp in [0, 0.1]:
            print(f"\n🌡️ Temperature: {temp}")
            print("-"*60)
            
            for i, test_input in enumerate(test_inputs[:3], 1):
                print(f"\n{i}. Input: {test_input}")
                response = self.generate_response(test_input, temperature=temp, use_context=False)
                print(f"   Response: {response[:200]}...")
                print()
        
        print("="*80)


def main():
    """Main function"""
    import argparse
    
    parser = argparse.ArgumentParser(description='Chat with fine-tuned counseling model')
    parser.add_argument('--model_path', type=str, default='./merged_counselor_mode_2b',
                       help='Path to the fine-tuned model')
    parser.add_argument('--test_only', action='store_true',
                       help='Only run test responses without chat')
    
    args = parser.parse_args()
    
    # Check if model exists
    if not os.path.exists(args.model_path):
        print(f"❌ Model not found at {args.model_path}")
        print("\nAvailable models:")
        for item in os.listdir('.'):
            if 'model' in item.lower() and os.path.isdir(item):
                print(f"  - {item}")
        return
    
    try:
        # Initialize chat interface
        chat = CounselorChatInterface(model_path=args.model_path)
        
        if args.test_only:
            # Run tests only
            chat.test_responses()
        else:
            # Start interactive chat
            chat.chat()
            
    except Exception as e:
        print(f"❌ Error: {e}")
        import traceback
        traceback.print_exc()


if __name__ == "__main__":
    main()