llm_feedback_generator.py

import os
from dotenv import load_dotenv
from langchain.chat_models import init_chat_model

load_dotenv()
os.environ["MISTRAL_API_KEY"] = os.getenv("MISTRAL_API_KEY")

def evaluate_answer(question: str, answer: str, skill: str) -> str:
    prompt = f"""
You are a senior technical interviewer with deep expertise in {skill}. 

Your task is to evaluate a candidate's answer to a technical interview question.

Strict Instructions:
- Provide **concise, constructive feedback in no more than 2 sentences**.
- Clearly mention **one strength** in the answer.
- Clearly mention **one specific area for improvement**.
- Do **not** restate the question or the answer.
- Be **professional, objective, and actionable** in tone.

Question:
\"\"\"{question}\"\"\"

Candidate's Answer:
\"\"\"{answer}\"\"\"

Your feedback (max 2 sentences):
"""

    llm = init_chat_model(
        model="mistral-small",
        model_provider="mistralai",
        temperature=0.3,
    )

    messages = [{"role": "user", "content": prompt}]

    try:
        response = llm.invoke(messages)
        return response.content if hasattr(response, "content") else str(response)
    except Exception as e:
        print("❌ LLM feedback error:", e)
        return "⚠️ Feedback generation failed."