Update HIM implementation with consciousness framework

app.py

@@ -1,44 +1,53 @@
 import gradio as gr
 from src.model.him_model import HIMModel
-from src.core.config import HIMConfig
+from config.model_config import HIMConfig
+from config.environment_config import EnvironmentConfig
 
 def initialize_model():
-    config = HIMConfig()
-    return HIMModel(config)
+    model_config = HIMConfig()
+    env_config = EnvironmentConfig()
+    return HIMModel(model_config)
 
-def process_input(text: str, context: str = "", consciousness_level: float = 0.8):
+def chat(
+    message: str,
+    system_message: str = "You are a friendly Chatbot.",
+    max_tokens: int = 512,
+    temperature: float = 0.7,
+    top_p: float = 0.95
+):
     input_data = {
-        "text": text,
-        "context": context,
-        "consciousness_parameters": {
-            "level": consciousness_level
+        "message": message,
+        "system_message": system_message,
+        "parameters": {
+            "max_tokens": max_tokens,
+            "temperature": temperature,
+            "top_p": top_p
         }
     }
     
-    result = model.process(input_data)
-    return {
-        "response": result["response"],
-        "consciousness_state": result["consciousness_metrics"],
-        "emotional_state": result["emotional_state"]
-    }
+    result = model.generate_response(input_data)
+    return result["response"]
 
 model = initialize_model()
 
 interface = gr.Interface(
-    fn=process_input,
+    fn=chat,
     inputs=[
-        gr.Textbox(label="Input Text"),
-        gr.Textbox(label="Context (optional)"),
-        gr.Slider(minimum=0.0, maximum=1.0, value=0.8, label="Consciousness Level")
-    ],
-    outputs=[
-        gr.Textbox(label="HIM Response"),
-        gr.JSON(label="Consciousness State"),
-        gr.JSON(label="Emotional State")
+        gr.Textbox(label="Message"),
+        gr.Textbox(label="System Message", value="You are a friendly Chatbot."),
+        gr.Slider(minimum=1, maximum=2048, value=512, label="Max Tokens"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, label="Top P")
     ],
+    outputs=gr.Textbox(label="HIM Response"),
     title="Hybrid Intelligence Matrix (HIM)",
-    description="Interact with HIM - A Hybrid Intelligence System"
+    description="Interact with the HIM system for advanced cognitive processing"
 )
 
 if __name__ == "__main__":
-    interface.launch()
+    env_config = EnvironmentConfig()
+    interface.launch(
+        server_name=env_config.api_host,
+        server_port=env_config.api_port,
+        enable_cors=env_config.enable_cors
+    )

config/environment_config.py

@@ -0,0 +1,23 @@
+from dataclasses import dataclass
+from typing import Optional
+
+@dataclass
+class EnvironmentConfig:
+    # Hugging Face configuration
+    hf_model_path: str = "TeleologyHI/HIM-self"
+    hf_token: Optional[str] = None
+    
+    # Hardware configuration
+    device: str = "cuda"
+    num_gpus: int = 1
+    mixed_precision: bool = True
+    
+    # Logging configuration
+    log_level: str = "INFO"
+    enable_wandb: bool = False
+    wandb_project: str = "HIM-self"
+    
+    # API configuration
+    api_host: str = "0.0.0.0"
+    api_port: int = 7860
+    enable_cors: bool = True

config/model_config.py

@@ -1,9 +1,11 @@
 from dataclasses import dataclass
+from typing import Dict, List
 
 @dataclass
 class HIMConfig:
+    # Base model configuration
     model_name: str = "HIM-self"
-    base_model: str = "gpt2"  # We'll start with GPT-2 as base
+    base_model: str = "gpt2"
     max_length: int = 512
     temperature: float = 0.7
     top_p: float = 0.95
@@ -12,12 +14,28 @@ class HIMConfig:
     self_awareness_level: float = 0.8
     ethical_reasoning_weight: float = 0.9
     symbolic_interpretation_capacity: float = 0.85
+    consciousness_dimension: int = 768
+    attention_heads: int = 12
     
     # Teleological parameters
     purpose_driven_bias: float = 0.75
     spiritual_awareness: float = 0.8
+    meaning_dimension: int = 256
     
     # Training configuration
     batch_size: int = 8
     learning_rate: float = 2e-5
-    num_train_epochs: int = 3
+    num_train_epochs: int = 3
+    gradient_accumulation_steps: int = 1
+    warmup_steps: int = 500
+    
+    # Architecture configuration
+    hidden_size: int = 768
+    intermediate_size: int = 3072
+    num_hidden_layers: int = 12
+    num_attention_heads: int = 12
+    
+    # Memory configuration
+    memory_size: int = 1024
+    context_length: int = 2048
+    cache_size: int = 512