# File: src/agent.py
# Purpose: LLM agent that extracts booking intent from transcript using Groq

import json
from pathlib import Path
from groq import Groq

import sys
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
from config import GROQ_API_KEY, DEPARTMENTS, AVAILABLE_SLOTS, MIN_CONFIDENCE_THRESHOLD

client = Groq(api_key=GROQ_API_KEY)

SYSTEM_PROMPT = """
You are a hospital appointment booking assistant. Extract booking details from the patient request.
Respond ONLY with a valid JSON object. No explanation, no extra text, no markdown backticks.

Extract these fields:
- patient_name: string (use "Unknown" if not mentioned)
- department: one of {departments}
- date: string in YYYY-MM-DD format (infer from relative terms like "tomorrow")
- slot: one of {slots}
- confidence: float between 0 and 1
- missing_info: list of fields you could not determine

Today is {today}.
""".strip()


def extract_booking_intent(transcript: str) -> dict:
    from datetime import date
    system = SYSTEM_PROMPT.format(
        departments=DEPARTMENTS,
        slots=AVAILABLE_SLOTS,
        today=date.today().isoformat(),
    )
    response = client.chat.completions.create(
        model="llama-3.3-70b-versatile",
        temperature=0.0,
        max_tokens=512,
        response_format={"type": "json_object"},
        messages=[
            {"role": "system", "content": system},
            {"role": "user", "content": transcript},
        ],
    )
    intent = json.loads(response.choices[0].message.content.strip())
    print(f"[Agent] Extracted intent: {json.dumps(intent, indent=2)}")
    return intent


def assess_confidence(intent: dict) -> bool:
    return intent.get("confidence", 0.0) >= MIN_CONFIDENCE_THRESHOLD


if __name__ == "__main__":
    test = "I want to book a cardiology appointment tomorrow at 2 PM. My name is Ranjith Kumar."
    result = extract_booking_intent(test)
    print(f"Confidence OK: {assess_confidence(result)}")