interactive_chat.py

"""
interactive_chat.py - Fully Customizable MAP-NEO Mini Interactive Chat Interface
Features: Real-time parameter tuning, conversation memory, context management, multiple responses
"""

import torch
from transformers import AutoTokenizer
from model_neo import NeoMini, NeoMiniConfig
import os
import json
import time
from pathlib import Path
from datetime import datetime
import gc

class InteractiveChat:
    def __init__(self, checkpoint_path="checkpoints/extended_context_model.pt"):
        self.model = None
        self.tokenizer = None
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.conversation_history = []
        self.max_context_length = 16384
        
        # Generation parameters (fully customizable)
        self.params = {
            'temperature': 0.7,
            'top_k': 50,
            'top_p': 0.9,
            'repetition_penalty': 1.1,
            'max_length': 150,
            'do_sample': True,
            'num_responses': 1
        }
        
        print("🚀 MAP-NEO Mini Interactive Chat Interface")
        print("=" * 60)
        self.load_model(checkpoint_path)
        
    def clear_gpu_cache(self):
        """Clear GPU memory cache"""
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
            torch.cuda.synchronize()
        gc.collect()
        
    def get_memory_usage(self):
        """Get current GPU memory usage"""
        if not torch.cuda.is_available():
            return "CPU only"
        
        allocated = torch.cuda.memory_allocated(0) / 1024**3
        cached = torch.cuda.memory_reserved(0) / 1024**3
        total = torch.cuda.get_device_properties(0).total_memory / 1024**3
        return f"{allocated:.2f}GB/{total:.2f}GB (cached: {cached:.2f}GB)"

    def load_model(self, checkpoint_path):
        """Load model and tokenizer"""
        print(f"📂 Loading model from {checkpoint_path}...")
        
        if not os.path.exists(checkpoint_path):
            print(f"❌ Checkpoint not found: {checkpoint_path}")
            return False
            
        try:
            checkpoint = torch.load(checkpoint_path, map_location=self.device)
            
            # Get context length from config
            if 'config' in checkpoint:
                self.max_context_length = checkpoint['config'].get('max_seq_len', 16384)
            
            # Load model
            config = NeoMiniConfig()
            config.max_seq_len = self.max_context_length
            self.model = NeoMini(config)
            self.model.load_state_dict(checkpoint['model_state_dict'])
            self.model.eval()
            self.model = self.model.to(self.device)
            
            # Load tokenizer
            tokenizer_path = "data/tokenizer"
            if Path(tokenizer_path).exists():
                self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
            else:
                print("Using GPT-2 tokenizer as fallback...")
                self.tokenizer = AutoTokenizer.from_pretrained("gpt2")
                if self.tokenizer.pad_token is None:
                    self.tokenizer.pad_token = self.tokenizer.eos_token
            
            print(f"✅ Model loaded successfully!")
            print(f"🧠 Parameters: {self.model.get_num_params():,}")
            print(f"📏 Context window: {self.max_context_length:,} tokens")
            print(f"💾 Memory: {self.get_memory_usage()}")
            return True
            
        except Exception as e:
            print(f"❌ Error loading model: {e}")
            return False

    def format_conversation_context(self):
        """Format conversation history for model input"""
        if not self.conversation_history:
            return ""
            
        context = "The following is a conversation between a human and an AI assistant. The AI assistant is helpful, harmless, and honest.\n\n"
        
        for exchange in self.conversation_history:
            context += f"Human: {exchange['human']}\n"
            context += f"AI: {exchange['ai']}\n\n"
            
        return context

    def generate_response(self, user_input, num_responses=None):
        """Generate AI response(s) to user input"""
        if num_responses is None:
            num_responses = self.params['num_responses']
            
        # Build full context
        context = self.format_conversation_context()
        full_prompt = context + f"Human: {user_input}\nAI: "
        
        # Check context length
        input_ids = self.tokenizer.encode(full_prompt, return_tensors="pt").to(self.device)
        prompt_length = input_ids.size(1)
        
        print(f"📝 Context: {prompt_length:,}/{self.max_context_length:,} tokens")
        
        if prompt_length >= self.max_context_length:
            print("⚠️ Context too long, trimming conversation history...")
            self.trim_conversation_history()
            context = self.format_conversation_context()
            full_prompt = context + f"Human: {user_input}\nAI: "
            input_ids = self.tokenizer.encode(full_prompt, return_tensors="pt").to(self.device)
            prompt_length = input_ids.size(1)
        
        # Generate response(s)
        responses = []
        for i in range(num_responses):
            print(f"🤖 Generating response {i+1}/{num_responses}...")
            
            with torch.no_grad():
                generated = input_ids.clone()
                max_new_tokens = min(self.params['max_length'], self.max_context_length - prompt_length)
                
                for step in range(max_new_tokens):
                    logits = self.model(generated)
                    next_token_logits = logits[0, -1, :] / self.params['temperature']
                    
                    # Apply repetition penalty
                    if self.params['repetition_penalty'] != 1.0:
                        for token_id in set(generated[0].tolist()):
                            if next_token_logits[token_id] < 0:
                                next_token_logits[token_id] *= self.params['repetition_penalty']
                            else:
                                next_token_logits[token_id] /= self.params['repetition_penalty']
                    
                    # Top-k filtering
                    if self.params['top_k'] > 0:
                        top_k_logits, _ = torch.topk(next_token_logits, self.params['top_k'])
                        min_top_k = top_k_logits[-1]
                        next_token_logits[next_token_logits < min_top_k] = float("-inf")
                    
                    # Top-p filtering
                    if self.params['top_p'] < 1.0:
                        sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
                        cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
                        sorted_indices_to_remove = cumulative_probs > self.params['top_p']
                        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
                        sorted_indices_to_remove[..., 0] = 0
                        indices_to_remove = sorted_indices[sorted_indices_to_remove]
                        next_token_logits[indices_to_remove] = float("-inf")
                    
                    # Sample next token
                    if self.params['do_sample']:
                        probs = torch.softmax(next_token_logits, dim=-1)
                        next_token = torch.multinomial(probs, num_samples=1)
                    else:
                        next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
                    
                    generated = torch.cat([generated, next_token.unsqueeze(0)], dim=1)
                    
                    # Check stopping conditions
                    if next_token.item() == self.tokenizer.eos_token_id:
                        break
                    
                    # Check for natural stopping points
                    if step > 10:  # Only check after minimum generation
                        decoded = self.tokenizer.decode(generated[0][prompt_length:], skip_special_tokens=True)
                        if decoded.strip().endswith(('.', '!', '?', '\n\n')):
                            break
            
            # Extract just the AI response
            full_response = self.tokenizer.decode(generated[0], skip_special_tokens=True)
            ai_response = full_response[len(full_prompt):].strip()
            
            # Clean up response
            if '\nHuman:' in ai_response:
                ai_response = ai_response.split('\nHuman:')[0].strip()
            
            responses.append(ai_response)
        
        return responses

    def trim_conversation_history(self):
        """Remove oldest conversation turns to fit context"""
        while len(self.conversation_history) > 1:
            self.conversation_history.pop(0)
            context = self.format_conversation_context()
            if len(self.tokenizer.encode(context)) < self.max_context_length // 2:
                break
        print(f"🧹 Trimmed conversation history to {len(self.conversation_history)} turns")

    def update_parameters(self):
        """Interactive parameter adjustment"""
        print("\n🎛️ Current Generation Parameters:")
        for key, value in self.params.items():
            print(f"  {key}: {value}")
        
        print("\nEnter new values (press Enter to keep current):")
        
        # Temperature
        temp_input = input(f"Temperature (0.1-2.0, current: {self.params['temperature']}): ").strip()
        if temp_input:
            try:
                self.params['temperature'] = max(0.1, min(2.0, float(temp_input)))
            except ValueError:
                print("❌ Invalid temperature, keeping current value")
        
        # Top-k
        topk_input = input(f"Top-k (0-100, current: {self.params['top_k']}): ").strip()
        if topk_input:
            try:
                self.params['top_k'] = max(0, min(100, int(topk_input)))
            except ValueError:
                print("❌ Invalid top-k, keeping current value")
        
        # Top-p
        topp_input = input(f"Top-p (0.1-1.0, current: {self.params['top_p']}): ").strip()
        if topp_input:
            try:
                self.params['top_p'] = max(0.1, min(1.0, float(topp_input)))
            except ValueError:
                print("❌ Invalid top-p, keeping current value")
        
        # Max length
        maxlen_input = input(f"Max length (10-500, current: {self.params['max_length']}): ").strip()
        if maxlen_input:
            try:
                self.params['max_length'] = max(10, min(500, int(maxlen_input)))
            except ValueError:
                print("❌ Invalid max length, keeping current value")
        
        # Number of responses
        num_resp = input(f"Number of responses (1-3, current: {self.params['num_responses']}): ").strip()
        if num_resp:
            try:
                self.params['num_responses'] = max(1, min(3, int(num_resp)))
            except ValueError:
                print("❌ Invalid number, keeping current value")
        
        print("✅ Parameters updated!")

    def save_conversation(self):
        """Save conversation to 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_info': {
                'max_context': self.max_context_length,
                'parameters': self.model.get_num_params()
            },
            'generation_params': self.params,
            'conversation': self.conversation_history
        }
        
        with open(filename, 'w', encoding='utf-8') as f:
            json.dump(conversation_data, f, indent=2, ensure_ascii=False)
        
        print(f"💾 Conversation saved to {filename}")

    def load_conversation(self, filename):
        """Load conversation from file"""
        try:
            with open(filename, 'r', encoding='utf-8') as f:
                conversation_data = json.load(f)
            
            self.conversation_history = conversation_data['conversation']
            print(f"📂 Loaded conversation with {len(self.conversation_history)} turns")
            
        except Exception as e:
            print(f"❌ Error loading conversation: {e}")

    def show_help(self):
        """Show available commands"""
        print("\n🔧 Available Commands:")
        print("  /help     - Show this help message")
        print("  /params   - Adjust generation parameters")
        print("  /clear    - Clear conversation history")
        print("  /save     - Save current conversation")
        print("  /load <file> - Load conversation from file")
        print("  /memory   - Show GPU memory usage")
        print("  /context  - Show current context usage")
        print("  /multi <n> - Generate n responses to next input")
        print("  /exit     - Exit the chat")
        print("  /quit     - Exit the chat")

    def run(self):
        """Main chat loop"""
        if not self.model or not self.tokenizer:
            print("❌ Model not loaded. Exiting.")
            return
        
        print(f"\n💬 Chat started! Context window: {self.max_context_length:,} tokens")
        print("Type /help for commands, /exit to quit")
        print("-" * 60)
        
        while True:
            try:
                # Get user input
                user_input = input("\n👤 You: ").strip()
                
                if not user_input:
                    continue
                
                # Handle commands
                if user_input.startswith('/'):
                    command = user_input.lower()
                    
                    if command in ['/exit', '/quit']:
                        print("👋 Goodbye!")
                        break
                    elif command == '/help':
                        self.show_help()
                    elif command == '/params':
                        self.update_parameters()
                    elif command == '/clear':
                        self.conversation_history = []
                        self.clear_gpu_cache()
                        print("🧹 Conversation history cleared")
                    elif command == '/save':
                        self.save_conversation()
                    elif command.startswith('/load '):
                        filename = command[6:].strip()
                        self.load_conversation(filename)
                    elif command == '/memory':
                        print(f"💾 GPU Memory: {self.get_memory_usage()}")
                    elif command == '/context':
                        context_length = len(self.tokenizer.encode(self.format_conversation_context()))
                        print(f"📏 Current context: {context_length:,}/{self.max_context_length:,} tokens")
                    elif command.startswith('/multi '):
                        try:
                            num = int(command[7:].strip())
                            self.params['num_responses'] = max(1, min(3, num))
                            print(f"🎯 Next response will generate {self.params['num_responses']} options")
                        except ValueError:
                            print("❌ Invalid number format")
                    else:
                        print("❌ Unknown command. Type /help for available commands.")
                    continue
                
                # Generate response(s)
                start_time = time.time()
                responses = self.generate_response(user_input)
                generation_time = time.time() - start_time
                
                # Display response(s)
                if len(responses) == 1:
                    print(f"\n🤖 AI: {responses[0]}")
                    chosen_response = responses[0]
                else:
                    print(f"\n🤖 AI generated {len(responses)} responses:")
                    for i, response in enumerate(responses, 1):
                        print(f"\n[{i}] {response}")
                    
                    while True:
                        choice = input(f"\nChoose response (1-{len(responses)}, Enter for 1): ").strip()
                        if not choice:
                            choice = "1"
                        try:
                            choice_idx = int(choice) - 1
                            if 0 <= choice_idx < len(responses):
                                chosen_response = responses[choice_idx]
                                break
                            else:
                                print(f"❌ Invalid choice. Enter 1-{len(responses)}")
                        except ValueError:
                            print("❌ Invalid input. Enter a number.")
                
                # Add to conversation history
                self.conversation_history.append({
                    'human': user_input,
                    'ai': chosen_response,
                    'timestamp': datetime.now().isoformat(),
                    'generation_time': round(generation_time, 2)
                })
                
                # Reset num_responses if it was changed
                if self.params['num_responses'] != 1:
                    self.params['num_responses'] = 1
                
                print(f"⏱️ Generated in {generation_time:.2f}s | 💾 {self.get_memory_usage()}")
                
            except KeyboardInterrupt:
                print("\n\n👋 Chat interrupted. Goodbye!")
                break
            except Exception as e:
                print(f"\n❌ Error: {e}")
                self.clear_gpu_cache()

def main():
    # Allow custom checkpoint path
    import sys
    checkpoint_path = "checkpoints/extended_context_model.pt"
    if len(sys.argv) > 1:
        checkpoint_path = sys.argv[1]
    
    chat = InteractiveChat(checkpoint_path)
    chat.run()

if __name__ == "__main__":
    main()