Upload examples/smartphone_deployment.py with huggingface_hub

examples/smartphone_deployment.py

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+#!/usr/bin/env python3
+"""
+AuraMind Smartphone Deployment Example
+Complete implementation for mobile applications
+"""
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import time
+import psutil
+import os
+from typing import Dict, List, Optional
+import json
+
+class SmartphoneAuraMind:
+    """
+    Smartphone-optimized AuraMind implementation
+    Designed for efficient mobile deployment with memory and battery optimization
+    """
+    
+    def __init__(self, model_variant: str = "270m", device: str = "auto"):
+        """
+        Initialize AuraMind for smartphone deployment
+        
+        Args:
+            model_variant: "270m", "180m", or "90m" 
+            device: "auto", "cpu", or "cuda"
+        """
+        self.model_variant = model_variant
+        self.model_name = f"zail-ai/Auramind"
+        
+        print(f"Loading AuraMind {model_variant} for smartphone deployment...")
+        
+        # Smartphone-optimized loading configuration
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.model_name,
+            use_fast=True,  # Fast tokenizer for mobile
+            trust_remote_code=False
+        )
+        
+        # Memory-efficient model loading
+        self.model = AutoModelForCausalLM.from_pretrained(
+            self.model_name,
+            torch_dtype=torch.float16,  # Half precision essential for mobile
+            device_map=device,
+            low_cpu_mem_usage=True,     # Optimize CPU memory usage
+            use_cache=True,             # Enable KV caching
+            trust_remote_code=False
+        )
+        
+        # Mobile-specific optimizations
+        if hasattr(self.model, 'half'):
+            self.model = self.model.half()
+        
+        # Set to evaluation mode for inference
+        self.model.eval()
+        
+        print(f"✅ AuraMind {model_variant} loaded successfully")
+        self._print_system_info()
+    
+    def _print_system_info(self):
+        """Print system information for mobile deployment"""
+        process = psutil.Process(os.getpid())
+        memory_mb = process.memory_info().rss / 1024 / 1024
+        
+        print(f"📱 System Information:")
+        print(f"   Memory Usage: {memory_mb:.1f} MB")
+        
+        if torch.cuda.is_available():
+            gpu_memory = torch.cuda.memory_allocated() / 1024 / 1024
+            gpu_name = torch.cuda.get_device_name(0)
+            print(f"   GPU: {gpu_name}")
+            print(f"   GPU Memory: {gpu_memory:.1f} MB")
+        else:
+            print("   Device: CPU")
+    
+    def chat(self, message: str, mode: str = "Assistant", 
+             max_tokens: int = 200, temperature: float = 0.7) -> Dict:
+        """
+        Generate response with performance monitoring
+        
+        Args:
+            message: User input message
+            mode: "Therapist" or "Assistant"
+            max_tokens: Maximum response length
+            temperature: Response creativity (0.1-1.0)
+            
+        Returns:
+            Dict containing response, metrics, and metadata
+        """
+        start_time = time.time()
+        
+        # Format prompt for dual-mode architecture
+        prompt = f"<|start_of_turn|>user\n[{mode} Mode] {message}<|end_of_turn|>\n<|start_of_turn|>model\n"
+        
+        # Tokenize with mobile optimization
+        inputs = self.tokenizer(
+            prompt, 
+            return_tensors="pt", 
+            truncation=True, 
+            max_length=512,  # Optimized for mobile memory
+            padding=False
+        )
+        
+        # Mobile-optimized generation configuration
+        generation_config = {
+            "max_new_tokens": max_tokens,
+            "temperature": temperature,
+            "do_sample": True,
+            "top_p": 0.9,
+            "repetition_penalty": 1.1,
+            "pad_token_id": self.tokenizer.eos_token_id,
+            "eos_token_id": self.tokenizer.eos_token_id,
+            "use_cache": True,  # Essential for mobile performance
+        }
+        
+        # Generate response with memory optimization
+        with torch.no_grad():
+            outputs = self.model.generate(
+                **inputs,
+                **generation_config
+            )
+        
+        # Decode response
+        full_response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        response = full_response.split("<|start_of_turn|>model\n")[-1].strip()
+        
+        # Calculate performance metrics
+        end_time = time.time()
+        inference_time = (end_time - start_time) * 1000  # Convert to milliseconds
+        
+        # Memory usage
+        process = psutil.Process(os.getpid())
+        memory_mb = process.memory_info().rss / 1024 / 1024
+        
+        return {
+            "response": response,
+            "mode": mode,
+            "inference_time_ms": round(inference_time, 2),
+            "memory_usage_mb": round(memory_mb, 1),
+            "input_tokens": len(inputs["input_ids"][0]),
+            "output_tokens": len(outputs[0]) - len(inputs["input_ids"][0]),
+            "timestamp": datetime.now().isoformat()
+        }
+    
+    def batch_chat(self, messages: List[Dict], batch_size: int = 4) -> List[Dict]:
+        """
+        Process multiple messages efficiently for mobile deployment
+        
+        Args:
+            messages: List of {"message": str, "mode": str} dictionaries
+            batch_size: Batch size for processing (mobile-optimized)
+            
+        Returns:
+            List of response dictionaries
+        """
+        results = []
+        
+        for i in range(0, len(messages), batch_size):
+            batch = messages[i:i + batch_size]
+            
+            for msg_dict in batch:
+                result = self.chat(
+                    message=msg_dict["message"],
+                    mode=msg_dict.get("mode", "Assistant")
+                )
+                results.append(result)
+                
+                # Brief pause to prevent overheating on mobile
+                time.sleep(0.1)
+        
+        return results
+    
+    def get_model_info(self) -> Dict:
+        """Get comprehensive model information for mobile deployment"""
+        return {
+            "model_name": self.model_name,
+            "variant": self.model_variant,
+            "config": {
+                "vocab_size": self.tokenizer.vocab_size,
+                "max_position_embeddings": getattr(self.model.config, 'max_position_embeddings', 'Unknown'),
+                "hidden_size": getattr(self.model.config, 'hidden_size', 'Unknown'),
+                "num_attention_heads": getattr(self.model.config, 'num_attention_heads', 'Unknown'),
+                "num_hidden_layers": getattr(self.model.config, 'num_hidden_layers', 'Unknown')
+            },
+            "memory_requirements": {
+                "minimum_ram": self.model_variants.get(f"auramind-{self.model_variant}", {}).get("memory_usage", "Unknown"),
+                "recommended_storage": "1-2GB free space",
+                "os_requirements": "Android 8+ or iOS 12+"
+            },
+            "performance": {
+                "expected_inference_speed": self.model_variants.get(f"auramind-{self.model_variant}", {}).get("inference_speed", "Unknown"),
+                "quantization": self.model_variants.get(f"auramind-{self.model_variant}", {}).get("quantization", "Unknown")
+            }
+        }
+
+# Demo usage for smartphone deployment
+def demonstrate_mobile_deployment():
+    """Demonstrate AuraMind smartphone deployment"""
+    
+    print("🚀 AuraMind Mobile Demo")
+    print("=" * 50)
+    
+    # Initialize for smartphone (using lighter variant for demo)
+    auramind = SmartphoneAuraMind(model_variant="270m", device="cpu")
+    
+    # Sample conversations demonstrating dual-mode capability
+    sample_conversations = [
+        {
+            "message": "I'm feeling overwhelmed with my workload and having trouble sleeping",
+            "mode": "Therapist"
+        },
+        {
+            "message": "Help me organize my daily tasks more efficiently", 
+            "mode": "Assistant"
+        },
+        {
+            "message": "I'm having anxiety about an upcoming presentation",
+            "mode": "Therapist"  
+        },
+        {
+            "message": "What's the best way to track my productivity goals?",
+            "mode": "Assistant"
+        }
+    ]
+    
+    print("\n🧠 Testing Dual-Mode Responses:")
+    print("-" * 40)
+    
+    for i, conversation in enumerate(sample_conversations, 1):
+        print(f"\n[Test {i}] {conversation['mode']} Mode")
+        print(f"User: {conversation['message']}")
+        
+        result = auramind.chat(
+            message=conversation["message"],
+            mode=conversation["mode"],
+            max_tokens=150,
+            temperature=0.7
+        )
+        
+        print(f"AuraMind: {result['response']}")
+        print(f"⏱️  Inference: {result['inference_time_ms']}ms | 💾 Memory: {result['memory_usage_mb']}MB")
+        
+        # Small delay for demonstration
+        time.sleep(1)
+    
+    print("\n📊 Model Information:")
+    print("-" * 40)
+    model_info = auramind.get_model_info()
+    print(json.dumps(model_info, indent=2))
+    
+    print("\n✅ Mobile deployment demonstration completed!")
+    print("Ready for smartphone integration with Android/iOS apps.")
+
+if __name__ == "__main__":
+    demonstrate_mobile_deployment()