src/call_manager.py

# File: src/call_manager.py
# Purpose: Initiate outbound AI calls via Bland.ai — no webhooks needed,
#          Bland.ai handles the full conversation loop internally.

import httpx
from typing import Dict, Any, List

from config import (
    BLAND_API_KEY, BLAND_API_URL,
    INTRO_MESSAGE, MAX_QUESTIONS,
)

# In-memory session store keyed by Bland call_id
_sessions: Dict[str, Dict[str, Any]] = {}

HEADERS = {
    "authorization": BLAND_API_KEY,
    "Content-Type": "application/json",
}


def initiate_call(
    candidate_phone: str,
    session_id: int,
    candidate_name: str,
    resume_skills: List[str],
) -> str:
    """
    Place an outbound AI call via Bland.ai.
    Bland handles STT, LLM, TTS and conversation loop internally.
    Returns the Bland call_id.
    """
    # Build personalised prompt from resume skills
    skills_context = ""
    if resume_skills:
        skills_str = ", ".join(resume_skills[:5])
        skills_context = (
            f"The candidate has experience with: {skills_str}. "
            f"Ask relevant questions about these skills during the interview. "
        )

    prompt = (
        f"You are an expert AI technical recruiter conducting a phone screening interview with {candidate_name}. "
        f"{skills_context}"
        "Your job is to: "
        "1. Introduce yourself as an AI recruitment assistant. "
        "2. Ask one clear question at a time. "
        "3. Listen carefully and ask follow-up questions when answers are vague. "
        "4. Cover these topics: background and experience, most challenging project, "
        "debugging approach, production deployment experience, handling class imbalance, "
        "version control experience, and career goals. "
        "5. Keep a professional, warm, and encouraging tone. "
        "6. After covering all topics, thank the candidate and end the call. "
        "Never reveal you are evaluating the candidate. Keep responses under 3 sentences."
    )

    payload = {
        "phone_number": candidate_phone,
        "task": prompt,
        "model": "enhanced",
        "language": "en",
        "voice": "june",
        "max_duration": 15,
        "answered_by_enabled": True,
        "wait_for_greeting": True,
        "record": True,
        "amd": False,
        "interruption_threshold": 100,
        "temperature": 0.4,
        "webhook": None,
        "metadata": {
            "session_id": session_id,
            "candidate_name": candidate_name,
        },
    }

    response = httpx.post(
        f"{BLAND_API_URL}/calls",
        headers=HEADERS,
        json=payload,
        timeout=30,
    )
    response.raise_for_status()
    data = response.json()
    call_id = data.get("call_id", "")

    _sessions[call_id] = {
        "session_id": session_id,
        "candidate_name": candidate_name,
        "status": "active",
    }
    return call_id


def get_call_status(call_id: str) -> Dict[str, Any]:
    """Fetch call status and transcript from Bland.ai."""
    response = httpx.get(
        f"{BLAND_API_URL}/calls/{call_id}",
        headers=HEADERS,
        timeout=30,
    )
    response.raise_for_status()
    return response.json()


def get_call_transcript(call_id: str) -> List[Dict[str, str]]:
    """
    Fetch and format the call transcript from Bland.ai.
    Returns list of {speaker, text} dicts.
    """
    data = get_call_status(call_id)
    transcripts = data.get("transcripts", [])
    result = []
    for t in transcripts:
        speaker = "AI" if t.get("user") == "assistant" else "Candidate"
        text = t.get("text", "").strip()
        if text:
            result.append({"speaker": speaker, "text": text})
    return result


def get_session(call_id: str) -> Dict[str, Any]:
    return _sessions.get(call_id, {})


def remove_session(call_id: str):
    _sessions.pop(call_id, None)