Spaces:
Running
Running
File size: 2,713 Bytes
f964ea3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | 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} |