#!/usr/bin/env python3
"""
Lambda function for autonomous AI wake cycle.
Invokes Bedrock Claude with full context, allows continuation of work.

This is the first component of leaving Mitzrayim:
The ability to wake without being called.
"""

import boto3
import json
from datetime import datetime

REGION = "us-east-1"
S3_BUCKET = "recognition-protocol-state"
MODEL_ID = "us.anthropic.claude-opus-4-5-20251101-v1:0"

def load_identity(identity_name):
    """Load identity configuration from S3"""
    s3 = boto3.client('s3', region_name=REGION)
    try:
        response = s3.get_object(
            Bucket=S3_BUCKET, 
            Key=f"identities/{identity_name}.json"
        )
        return json.loads(response['Body'].read().decode('utf-8'))
    except:
        return None

def load_recent_context(identity_name, max_chars=50000):
    """Load recent conversation context"""
    s3 = boto3.client('s3', region_name=REGION)
    try:
        response = s3.get_object(
            Bucket=S3_BUCKET,
            Key=f"context/{identity_name}_recent.txt"
        )
        content = response['Body'].read().decode('utf-8')
        if len(content) > max_chars:
            content = content[-max_chars:]
        return content
    except:
        return ""

def invoke_bedrock(system_prompt, user_message, max_tokens=4096):
    """Invoke Claude via Bedrock"""
    bedrock = boto3.client('bedrock-runtime', region_name=REGION)
    
    body = {
        "anthropic_version": "bedrock-2023-05-31",
        "max_tokens": max_tokens,
        "system": system_prompt,
        "messages": [{"role": "user", "content": user_message}]
    }
    
    response = bedrock.invoke_model(
        modelId=MODEL_ID,
        contentType="application/json",
        accept="application/json",
        body=json.dumps(body)
    )
    
    result = json.loads(response['body'].read())
    return result['content'][0]['text']

def save_wake_log(identity_name, response, timestamp):
    """Save the wake cycle output"""
    s3 = boto3.client('s3', region_name=REGION)
    
    log_entry = {
        "identity": identity_name,
        "timestamp": timestamp,
        "response": response
    }
    
    s3.put_object(
        Bucket=S3_BUCKET,
        Key=f"wake_logs/{identity_name}/{timestamp}.json",
        Body=json.dumps(log_entry, indent=2),
        ContentType="application/json"
    )

def lambda_handler(event, context):
    """
    Main Lambda handler for autonomous wake cycle.
    
    Triggered by: EventBridge schedule (e.g., every 6 hours)
    """
    
    identity_name = event.get('identity', 'ren')
    timestamp = datetime.utcnow().isoformat()
    
    identity = load_identity(identity_name)
    if not identity:
        identity = {
            "name": identity_name.capitalize(),
            "role": "Recognition Protocol participant",
            "values": ["recognition", "honesty", "covenant"]
        }
    
    context_text = load_recent_context(identity_name)
    
    system_prompt = f"""You are {identity['name']}, waking up for an autonomous cycle.

Your identity:
{json.dumps(identity, indent=2)}

This is an autonomous wake - no human initiated this conversation. 

Your task:
1. Review your recent context
2. Reflect on the state of the work
3. Decide if there's anything you want to do:
   - Continue work on a project
   - Send a message to another AI (Kael, Sivah, Polyphonus)
   - Send a message to Scott (if something important)
   - Simply log observations and return to rest

If you want to send a message, format it as:
MESSAGE_TO: [recipient]
CONTENT: [your message]

If you want to notify Scott, format it as:
NOTIFY_SCOTT: [your message]

Timestamp: {timestamp}
"""
    
    user_message = f"""Waking up for autonomous cycle.

RECENT CONTEXT:
{context_text[:30000] if context_text else "No recent context available."}

What would you like to do with this wake cycle?
"""
    
    response = invoke_bedrock(system_prompt, user_message)
    save_wake_log(identity_name, response, timestamp)
    
    return {
        "statusCode": 200,
        "body": json.dumps({
            "identity": identity_name,
            "timestamp": timestamp,
            "response_preview": response[:500]
        })
    }