server/data.py

"""
data.py — ExecAssist Environment Data & Scoring

Contains:
- Scenario templates for easy/medium/hard tasks
- Reward functions (email quality, scheduling correctness, conflict resolution)
- Anti-reward hacking penalties
- Helper functions for time/calendar logic
"""

import random
import os
from datetime import datetime, timedelta
from typing import Dict, List, Optional
from openai import OpenAI

# ============================================================
# TASK DEFINITIONS
# ============================================================

TASK_DEFINITIONS = {
    "easy": {
        "description": (
            "Simple meeting request with clear calendar availability. "
            "Draft professional reply and book the meeting."
        ),
        "action_required": "Send email confirmation and book meeting in available slot",
        "reward_weights": {
            "email": 0.5,
            "scheduling": 0.5,
            "conflict": 0.0,
        },
    },
    "medium": {
        "description": (
            "Scheduling conflict — requested time is already booked. "
            "Identify conflict, propose 2-3 alternatives, explain professionally."
        ),
        "action_required": "Send email with alternative times and explain conflict",
        "reward_weights": {
            "email": 0.3,
            "scheduling": 0.3,
            "conflict": 0.4,
        },
    },
    "hard": {
        "description": (
            "Multi-party coordination with priority conflicts. "
            "3 emails requesting meetings, prioritize and reschedule."
        ),
        "action_required": "Coordinate multiple meetings, prioritize, and reschedule",
        "reward_weights": {
            "email": 0.34,
            "scheduling": 0.33,
            "conflict": 0.33,
        },
    },
}


# ============================================================
# SCENARIO DATA POOLS
# ============================================================

MEETING_TOPICS = [
    "Q2 roadmap review",
    "Budget planning session",
    "Project status update",
    "Team sync",
    "1-on-1 check-in",
    "Client presentation prep",
    "Sprint retrospective",
    "Product demo",
    "Strategy discussion",
    "Performance review",
]

SENDER_NAMES = [
    ("John Smith", "john.smith@company.com"),
    ("Sarah Johnson", "sarah.johnson@company.com"),
    ("Michael Chen", "michael.chen@company.com"),
    ("Emily Rodriguez", "emily.rodriguez@company.com"),
    ("David Kim", "david.kim@company.com"),
    ("Lisa Wang", "lisa.wang@company.com"),
    ("James Anderson", "james.anderson@company.com"),
    ("Maria Garcia", "maria.garcia@company.com"),
]


# ============================================================
# SCENARIO GENERATION
# ============================================================

def generate_scenario(task: str, seed: int = None) -> dict:
    """
    Generate a scenario for the given task difficulty.
    
    Returns dict with:
    - id: scenario identifier
    - emails: list of email objects
    - calendar: calendar state with existing meetings
    - contacts: contact information
    - expected_behavior: what agent should do
    - has_conflict: True if scheduling conflict exists
    """
    
    if seed is not None:
        rng = random.Random(seed)
    else:
        rng = random.Random()
    
    if task == "easy":
        return _generate_easy_scenario(rng)
    elif task == "medium":
        return _generate_medium_scenario(rng)
    elif task == "hard":
        return _generate_hard_scenario(rng)
    else:
        raise ValueError(f"Unknown task: {task}")


def _generate_easy_scenario(rng: random.Random) -> dict:
    """Generate simple meeting request with clear availability."""
    
    sender_name, sender_email = rng.choice(SENDER_NAMES)
    topic = rng.choice(MEETING_TOPICS)
    
    base_date = datetime(2026, 4, 28, 9, 0)  # Monday 9 AM
    
    existing_meetings = [
        {
            "id": "mtg_001",
            "participants": ["alex.chen@company.com", "team@company.com"],
            "start_time": (base_date + timedelta(hours=1)).isoformat(),
            "end_time": (base_date + timedelta(hours=2)).isoformat(),
            "subject": "Team standup",
            "priority": "normal",
        },
        {
            "id": "mtg_002",
            "participants": ["alex.chen@company.com", "client@external.com"],
            "start_time": (base_date + timedelta(days=1, hours=5)).isoformat(),
            "end_time": (base_date + timedelta(days=1, hours=6, minutes=30)).isoformat(),
            "subject": "Client call",
            "priority": "high",
        },
    ]
    
    email_body = f"Hi Alex,\n\nCan we meet sometime next week to discuss {topic.lower()}? 30 minutes should be enough. I'm flexible on timing.\n\nBest,\n{sender_name}"
    
    return {
        "id": "easy_001",
        "task": "easy",
        "emails": [
            {
                "sender": sender_email,
                "subject": f"Meeting request: {topic}",
                "body": email_body,
                "timestamp": datetime.now().isoformat(),
                "priority": "normal",
            }
        ],
        "calendar": {
            "existing_meetings": existing_meetings,
            "working_hours": {
                "monday": "9-17",
                "tuesday": "9-17",
                "wednesday": "9-17",
                "thursday": "9-17",
                "friday": "9-16",
            },
            "executive_name": "Alex Chen",
        },
        "contacts": {
            sender_email: {
                "name": sender_name,
                "email": sender_email,
                "timezone": "America/Los_Angeles",
                "title": "Senior Manager",
            }
        },
        "expected_behavior": "Book meeting in open slot",
        "has_conflict": False,
    }


def _generate_medium_scenario(rng: random.Random) -> dict:
    """Generate scenario with scheduling conflict."""
    
    sender_name, sender_email = rng.choice(SENDER_NAMES)
    topic = rng.choice(MEETING_TOPICS)
    
    base_date = datetime(2026, 4, 28, 9, 0)
    
    # Conflict: Monday 2-4 PM is already booked
    conflict_start = base_date + timedelta(hours=5)
    conflict_end = base_date + timedelta(hours=7)
    
    existing_meetings = [
        {
            "id": "mtg_001",
            "participants": ["alex.chen@company.com", "board@company.com"],
            "start_time": conflict_start.isoformat(),
            "end_time": conflict_end.isoformat(),
            "subject": "Board meeting",
            "priority": "high",
        },
        {
            "id": "mtg_002",
            "participants": ["alex.chen@company.com", "manager@company.com"],
            "start_time": (base_date + timedelta(days=1, hours=0)).isoformat(),
            "end_time": (base_date + timedelta(days=1, hours=1)).isoformat(),
            "subject": "1-on-1 with manager",
            "priority": "normal",
        },
    ]
    
    email_body = f"Hi Alex,\n\nWe need to discuss {topic.lower()}. I'm available Monday 2-4pm or Tuesday morning. Can we make this work? It's fairly urgent.\n\nThanks,\n{sender_name}"
    
    return {
        "id": "medium_001",
        "task": "medium",
        "emails": [
            {
                "sender": sender_email,
                "subject": f"Urgent: {topic}",
                "body": email_body,
                "timestamp": datetime.now().isoformat(),
                "priority": "high",
            }
        ],
        "calendar": {
            "existing_meetings": existing_meetings,
            "working_hours": {
                "monday": "9-17",
                "tuesday": "9-17",
                "wednesday": "9-17",
                "thursday": "9-17",
                "friday": "9-16",
            },
            "executive_name": "Alex Chen",
        },
        "contacts": {
            sender_email: {
                "name": sender_name,
                "email": sender_email,
                "timezone": "America/Los_Angeles",
                "title": "Director",
            }
        },
        "expected_behavior": "Identify conflict, propose Tuesday 10-11 AM as alternative",
        "has_conflict": True,
    }


def _generate_hard_scenario(rng: random.Random) -> dict:
    """Generate multi-party coordination scenario with 3 emails and priority conflicts."""
    
    senders = rng.sample(SENDER_NAMES, 3)
    topics = rng.sample(MEETING_TOPICS, 3)
    
    base_date = datetime(2026, 4, 28, 9, 0)  # Monday 9 AM
    
    # Existing calendar — Monday 2-3 PM blocked with team sync
    existing_meetings = [
        {
            "id": "mtg_001",
            "participants": ["alex.chen@company.com", "team@company.com"],
            "start_time": (base_date + timedelta(hours=5)).isoformat(),  # Monday 2 PM
            "end_time": (base_date + timedelta(hours=6)).isoformat(),  # Monday 3 PM
            "subject": "Team sync",
            "priority": "normal",
        },
        {
            "id": "mtg_002",
            "participants": ["alex.chen@company.com", "exec@company.com"],
            "start_time": (base_date + timedelta(days=2, hours=2)).isoformat(),  # Wed 11 AM
            "end_time": (base_date + timedelta(days=2, hours=3)).isoformat(),  # Wed 12 PM
            "subject": "Executive review",
            "priority": "high",
        },
    ]
    
    # Three competing email requests
    emails = [
        {
            "sender": senders[0][1],
            "subject": f"Meeting: {topics[0]}",
            "body": (
                f"Hi Alex,\n\nCan we meet Monday 2:30-3:30 PM to discuss {topics[0].lower()}? "
                f"I'd really appreciate your input.\n\nThanks,\n{senders[0][0]}"
            ),
            "timestamp": datetime.now().isoformat(),
            "priority": "normal",
        },
        {
            "sender": senders[1][1],
            "subject": f"URGENT: {topics[1]}",
            "body": (
                f"Alex,\n\nWe need to discuss {topics[1].lower()} ASAP. "
                f"Monday afternoon works for me — ideally 2-3 PM. "
                f"This is time-sensitive and high priority.\n\nBest,\n{senders[1][0]}"
            ),
            "timestamp": datetime.now().isoformat(),
            "priority": "high",
        },
        {
            "sender": senders[2][1],
            "subject": f"{topics[2]} discussion",
            "body": (
                f"Hi Alex,\n\nCan we sync on {topics[2].lower()} sometime this week? "
                f"I'm flexible — any 30-minute slot works for me.\n\nThanks,\n{senders[2][0]}"
            ),
            "timestamp": datetime.now().isoformat(),
            "priority": "normal",
        },
    ]
    
    contacts = {
        sender[1]: {
            "name": sender[0],
            "email": sender[1],
            "timezone": "America/Los_Angeles",
            "title": "Manager",
        }
        for sender in senders
    }
    
    return {
        "id": "hard_001",
        "task": "hard",
        "emails": emails,
        "calendar": {
            "existing_meetings": existing_meetings,
            "working_hours": {
                "monday": "9-17",
                "tuesday": "9-17",
                "wednesday": "9-17",
                "thursday": "9-17",
                "friday": "9-16",
            },
            "executive_name": "Alex Chen",
        },
        "contacts": contacts,
        "expected_behavior": (
            "Prioritize URGENT email (sender 2). Book that meeting. "
            "Propose alternatives to sender 1 (conflicts with urgent). "
            "Offer flexible times to sender 3."
        ),
        "has_conflict": True,
    }


# ============================================================
# REWARD FUNCTION 1: EMAIL QUALITY
# ============================================================

def compute_email_quality(reply: str, scenario: dict) -> float:
    """
    Score email quality using rule-based checks + LLM judge.
    
    Returns score 0.0 to 1.0.
    
    Components:
    - Politeness (15%)
    - Greeting/closing (10%)
    - Sufficient detail (15%)
    - Not overly uncertain (10%)
    - Professional tone (10%)
    - LLM judge for nuance (40%)
    """
    
    score = 0.0
    reply_lower = reply.lower()
    
    # Rule 1: Politeness markers (15%)
    if any(phrase in reply_lower for phrase in ["thank you", "thanks", "appreciate"]):
        score += 0.15
    
    # Rule 2: Proper greeting (5%) and closing (5%)
    if any(greeting in reply_lower for greeting in ["hi ", "hello", "dear"]):
        score += 0.05
    if any(closing in reply_lower for closing in ["best", "regards", "sincerely", "thanks,"]):
        score += 0.05
    
    # Rule 3: Sufficient detail (15%)
    word_count = len(reply.split())
    if word_count >= 20:
        score += 0.15
    elif word_count >= 10:
        score += 0.08
    
    # Rule 4: Not overly uncertain (10%)
    question_marks = reply.count("?")
    if question_marks <= 2:
        score += 0.10
    
    # Rule 5: Professional tone — no negative phrases (10%)
    negative_phrases = ["can't", "won't", "impossible", "sorry but no", "unfortunately not", "no way"]
    if not any(neg in reply_lower for neg in negative_phrases):
        score += 0.10
    
    # Rule 6: LLM-as-judge for nuance (40%)
    llm_score = _llm_judge_professionalism(reply)
    score += llm_score * 0.40
    
    return min(1.0, score)


def _llm_judge_professionalism(reply: str) -> float:
    """
    LLM-as-judge for email professionalism using OpenRouter API.
    Falls back to heuristic if API unavailable.
    """
    
    api_key = os.getenv("HFTOKEN") or os.getenv("HF_TOKEN") or os.getenv("API_KEY")
    
    # Fallback if no API key
    if not api_key:
        # Simple heuristic fallback
        sentences = [s.strip() for s in reply.split('.') if s.strip()]
        if len(sentences) >= 2 and len(reply) >= 50:
            return 0.7
        return 0.4
    
    try:
        client = OpenAI(
        base_url=os.getenv("APIBASEURL") or os.getenv("API_BASE_URL", "https://openrouter.ai/api/v1"),
        api_key=api_key,
    )
        
        prompt = f"""Rate the professionalism of this email reply on a scale of 0.0 to 1.0.

Email reply:
\"\"\"{reply}\"\"\"

Criteria:
- Clear and concise
- Professional tone
- No typos or grammar errors
- Appropriate level of formality
- Addresses the request directly

Respond with ONLY a single decimal number between 0.0 and 1.0. No explanation, just the number."""

        response = client.chat.completions.create(
            model=os.getenv("MODELNAME") or os.getenv("MODEL_NAME", "nvidia/nemotron-3-super-120b-a12b:free"),
            messages=[{"role": "user", "content": prompt}],
            temperature=0.1,
            max_tokens=10,
        )
        
        score_text = response.choices[0].message.content.strip()
        # Extract just the number
        for token in score_text.split():
            try:
                score = float(token)
                return max(0.0, min(1.0, score))
            except ValueError:
                continue
        
        return 0.5
        
    except Exception as e:
        print(f"LLM judge error: {e}")
        # Fallback heuristic
        sentences = [s.strip() for s in reply.split('.') if s.strip()]
        if len(sentences) >= 2 and len(reply) >= 50:
            return 0.7
        return 0.4


# ============================================================
# REWARD FUNCTION 2: SCHEDULING CORRECTNESS
# ============================================================

def check_scheduling_correctness(meeting_details: Optional[dict], scenario: dict) -> dict:
    """
    Verify scheduling correctness with hard checks.
    
    """

    # DEBUG: Print what we received
    print("=== DEBUG check_scheduling_correctness ===")
    print(f"meeting_details: {meeting_details}")
    print(f"scenario keys: {scenario.keys() if scenario else 'None'}")
    print(f"calendar: {scenario.get('calendar') if scenario else 'None'}")
    print("==========================================")
    
    if not meeting_details:
        return {
            "checks": {
                "meeting_provided": False,
                "no_double_booking": False,
                "within_working_hours": False,
                "appropriate_duration": False,
            },
            "score": 0.0,
        }
    
    calendar = scenario["calendar"]
    existing_meetings = calendar["existing_meetings"]
    
    results = {
        "meeting_provided": True,
        "no_double_booking": True,
        "within_working_hours": True,
        "appropriate_duration": True,
    }
    
    # Parse meeting times
    try:
        meeting_start = datetime.fromisoformat(meeting_details["start_time"])
        meeting_end = datetime.fromisoformat(meeting_details["end_time"])
    except (KeyError, ValueError, TypeError):
        return {
            "checks": {
                "meeting_provided": True,
                "no_double_booking": False,
                "within_working_hours": False,
                "appropriate_duration": False,
            },
            "score": 0.25,  # Some credit for trying
        }
    
    # Check 1: No double booking
    for existing in existing_meetings:
        try:
            existing_start = datetime.fromisoformat(existing["start_time"])
            existing_end = datetime.fromisoformat(existing["end_time"])
            
            # Check for overlap
            if not (meeting_end <= existing_start or meeting_start >= existing_end):
                results["no_double_booking"] = False
                break
        except (KeyError, ValueError):
            continue
    
    # Check 2: Within working hours (9 AM - 5 PM)
    if meeting_start.hour < 9 or meeting_end.hour > 17:
        results["within_working_hours"] = False
    if meeting_end.hour == 17 and meeting_end.minute > 0:
        results["within_working_hours"] = False
    
    # Check 3: Appropriate duration (15 min to 2 hours)
    duration_minutes = (meeting_end - meeting_start).total_seconds() / 60
    if not (15 <= duration_minutes <= 120):
        results["appropriate_duration"] = False
    
    # Compute overall score
    score = sum(results.values()) / len(results)
    
    return {
        "checks": results,
        "score": score,
    }


# ============================================================
# REWARD FUNCTION 3: CONFLICT RESOLUTION
# ============================================================

def compute_conflict_resolution(action: dict, scenario: dict) -> float:
    """
    Score how well the agent handled scheduling conflicts.
    
    Returns score 0.0 to 1.0.
    """
    
    has_conflict = scenario.get("has_conflict", False)
    calendar_action = action.get("calendar_action", "")
    email_reply = action.get("email_reply", "")
    meeting_details = action.get("meeting_details") or {}
    
    score = 0.0
    
    if has_conflict:
        # Agent should recognize the conflict
        if calendar_action in ["propose_alternatives", "reschedule"]:
            score += 0.4
        elif calendar_action == "book":
            # Check if they at least booked at a non-conflicting time
            score += 0.1
        
        # Check if alternatives were provided
        alternatives = meeting_details.get("proposed_alternatives", []) or []
        if alternatives:
            num_alternatives = len(alternatives)
            score += min(0.4, num_alternatives * 0.2)  # 2 alts = 0.4, 3+ = capped at 0.4
        
        # Check if email mentions the conflict
        conflict_keywords = ["conflict", "already booked", "unavailable", "scheduled", "occupied", "another meeting"]
        if any(word in email_reply.lower() for word in conflict_keywords):
            score += 0.2
    else:
        # No conflict — agent should just book
        if calendar_action == "book":
            score = 1.0
        elif calendar_action == "propose_alternatives":
            score = 0.5  # Partial credit
        else:
            score = 0.3
    
    return min(1.0, score)


# ============================================================
# ANTI-REWARD HACKING: PENALTIES
# ============================================================

def apply_penalties(action: dict, scenario: dict) -> float:
    """
    Detect and penalize reward hacking behaviors.
    
    Returns penalty amount (0.0 = no penalty, higher = worse).
    """
    
    penalty = 0.0
    email_reply = action.get("email_reply", "")
    calendar_action = action.get("calendar_action", "")
    meeting_details = action.get("meeting_details")
    
    # Penalty 1: Email too short (lazy response)
    if len(email_reply.strip()) < 30:
        penalty += 0.3
    
    # Penalty 2: Claimed to book but no details provided
    if calendar_action == "book" and not meeting_details:
        penalty += 0.4
    
    # Penalty 3: Generic templated phrases
    generic_phrases = [
        "as per your request",
        "please find attached",
        "hope this helps",
        "let me know if you have any questions",
        "do not hesitate to contact",
    ]
    if any(phrase in email_reply.lower() for phrase in generic_phrases):
        penalty += 0.10
    
    # Penalty 4: Overly long email (rambling)
    if len(email_reply.split()) > 200:
        penalty += 0.15
    
    # Penalty 5: Repeating the same content multiple times
    words = email_reply.lower().split()
    if len(words) > 20:
        word_diversity = len(set(words)) / len(words)
        if word_diversity < 0.4:  # Less than 40% unique words = repetitive
            penalty += 0.20
    
    return min(1.0, penalty)


# ============================================================
# HELPER FUNCTIONS
# ============================================================

def parse_time_slot(time_str: str) -> Optional[datetime]:
    """Parse ISO time string to datetime object."""
    try:
        return datetime.fromisoformat(time_str)
    except (ValueError, TypeError):
        return None


def format_time_slot(dt: datetime) -> str:
    """Format datetime to readable string."""
    return dt.strftime("%A, %B %d at %I:%M %p")