src/analytics.py

import json
import os
from groq import Groq
from dotenv import load_dotenv

load_dotenv()
client = Groq(api_key=os.environ.get("GROQ_API_KEY"))

def analyze_interview(session: dict) -> dict:
    messages = session.get("messages", [])
    mode = session.get("mode", "general")
    company = session.get("company", "")
    role = session.get("role", "SDE")

    # Build transcript
    transcript = ""
    for m in messages:
        if m["role"] == "user":
            transcript += f"CANDIDATE: {m['content']}\n\n"
        elif m["role"] == "assistant":
            transcript += f"INTERVIEWER: {m['content']}\n\n"

    if not transcript.strip():
        return {}

    prompt = f"""You are an expert interview coach analyzing a {mode} interview for a {role} role{' at ' + company.capitalize() if company else ''}.

Here is the full interview transcript:
---
{transcript[:6000]}
---

Analyze this interview and return a JSON object with EXACTLY this structure (no extra text, just valid JSON):
{{
  "overall_score": <number 0-100>,
  "summary": "<2-3 sentence honest overall assessment>",
  "strong_points": [
    {{"title": "<strength title>", "detail": "<specific example from interview>"}},
    {{"title": "<strength title>", "detail": "<specific example from interview>"}},
    {{"title": "<strength title>", "detail": "<specific example from interview>"}}
  ],
  "pain_points": [
    {{"title": "<weakness title>", "detail": "<specific example and why it matters>"}},
    {{"title": "<weakness title>", "detail": "<specific example and why it matters>"}},
    {{"title": "<weakness title>", "detail": "<specific example and why it matters>"}}
  ],
  "areas_of_improvement": [
    {{"title": "<area>", "action": "<specific actionable advice>"}},
    {{"title": "<area>", "action": "<specific actionable advice>"}},
    {{"title": "<area>", "action": "<specific actionable advice>"}}
  ],
  "skill_scores": {{
    "technical": <0-100>,
    "communication": <0-100>,
    "problem_solving": <0-100>,
    "confidence": <0-100>,
    "cultural_fit": <0-100>
  }},
  "hiring_verdict": "<Strong Hire | Hire | Maybe | No Hire>",
  "verdict_reason": "<one sentence explanation>"
}}"""

    response = client.chat.completions.create(
        model="llama-3.3-70b-versatile",
        messages=[{"role": "user", "content": prompt}],
        max_tokens=1500
    )

    raw = response.choices[0].message.content.strip()
    # Strip markdown if present
    if raw.startswith("```"):
        raw = raw.split("```")[1]
        if raw.startswith("json"):
            raw = raw[4:]
    raw = raw.strip()

    try:
        return json.loads(raw)
    except Exception:
        return {"error": "Could not parse analysis", "raw": raw}