app/services/llm.py

import os
import json
import re
from openai import OpenAI
from dotenv import load_dotenv

load_dotenv()

class LLMService:
    def __init__(self):
        self.api_key = os.getenv("OPENROUTER_API_KEY")
        self.client = OpenAI(
            base_url="https://openrouter.ai/api/v1",
            api_key=self.api_key,
        )
        
        self.model = "deepseek/deepseek-chat-v3.1"

    def _clean_json_response(self, content: str):
        """Helper to strip Markdown formatting """
        try:
            return json.loads(content)
        except json.JSONDecodeError:
            # Look for markdown code blocks
            match = re.search(r"```json\s*(.*)\s*```", content, re.DOTALL)
            if match:
                return json.loads(match.group(1))
            # Fallback: look for the first { and last }
            match = re.search(r"(\{.*\})", content, re.DOTALL)
            if match:
                return json.loads(match.group(1))
            raise ValueError("Could not parse JSON from LLM")

    def analyze_match(self, resume_text: str, jd_text: str) -> dict:
        """
        Uses LLM to Extract AND Match skills semantically in one pass.
        """
        if not self.api_key:
            return {
                "score": 0,
                "common_skills": [],
                "missing_skills": ["API_KEY_MISSING"],
                "reasoning": "Please check server secrets."
            }

        prompt = f"""
        You are an expert AI Recruiter. 
        
        TASK:
        Compare the Candidate's Resume against the Job Description (JD).
        1. Extract the required Technical Skills & Soft Skills from the JD.
        2. Check if the Candidate possesses these skills based on the Resume.
        3. Calculate a Match Score (0-100).

        JOB DESCRIPTION:
        {jd_text[:]}

        CANDIDATE RESUME:
        {resume_text[:]}

        OUTPUT FORMAT:
        Return STRICT JSON with these keys:
        - "score": (Integer 0-100)
        - "common_skills": (List of strings)
        - "missing_skills": (List of strings)
        - "reasoning": (Short explanation)
        """

        try:
            response = self.client.chat.completions.create(
                model=self.model,
                messages=[
                    {"role": "system", "content": "Output strictly valid JSON only."},
                    {"role": "user", "content": prompt}
                ]
            )
            content = response.choices[0].message.content
            return self._clean_json_response(content)

        except Exception as e:
            print(f"LLM MATCH ERROR: {str(e)}")
            return {
                "score": 0,
                "common_skills": [],
                "missing_skills": ["Error calculating match"],
                "reasoning": "AI Service unavailable."
            }

    def generate_suggestions(self, resume_text: str, jd_text: str, missing_skills: list) -> dict:
        prompt = f"""
        You are a Career Coach.
        
        CONTEXT:
        Candidate is applying for this job
        {jd_text[:1000]}

        and this is their resume
        {resume_text[:]}

        They are missing: {", ".join(missing_skills)}
        
        TASK:
        1. Suggest 3 specific technical side projects to bridge these gaps.
        2. Write a short, professional cover letter.
        
        CRITICAL FORMATTING RULES:
        - Project format: Give project title first then description.
        - Do not use markdown headers in the project list.
        
        OUTPUT JSON keys:
        - "suggested_projects" (List of strings)
        - "cover_letter" (String)
        """

        try:
            response = self.client.chat.completions.create(
                model='openrouter/free',
                messages=[
                    {"role": "system", "content": "Output strictly valid JSON only."},
                    {"role": "user", "content": prompt}
                ]
            )
            
            content = response.choices[0].message.content
            return self._clean_json_response(content)

        except Exception as e:
            print(f"LLM SUGGESTION ERROR: {str(e)}")
            return {
                "suggested_projects": ["Error generating projects. Please try again."],
                "cover_letter": "Error generating cover letter."
            }

llm_service = LLMService()