backend/src/services/clustering_service.py

import os
import numpy as np
from sklearn.cluster import KMeans
from typing import List, Dict, Any
from google import genai
import google.genai.types as types
from supabase import create_client, Client
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

class ClusteringService:
    def __init__(self):
        url = os.environ.get("SUPABASE_URL")
        key = os.environ.get("SUPABASE_SERVICE_ROLE_KEY") or os.environ.get("SUPABASE_KEY")
        self.client: Client = create_client(url, key)
        self.gemini_client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))

    def fetch_all_embeddings(self) -> List[Dict[str, Any]]:
        """Fetch IDs and concatenated embeddings for all profiles."""
        print("🔍 Fetching profile embeddings...")
        # We'll use 'technical_skills' or 'headline' as a representative embedding for clustering
        # Or concatenate multiple if available. For simplicity, we use 'technical_skills'
        resp = self.client.table("profile_embeddings").select("id, technical_skills").execute()
        return resp.data

    def perform_clustering(self, data: List[Dict[str, Any]], n_clusters: int = 5):
        """Perform K-Means clustering on the fetched embeddings."""
        if not data:
            print("⚠️ No data to cluster.")
            return []

        # Extract vectors
        X = []
        ids = []
        import json
        for item in data:
            raw_vec = item.get("technical_skills")
            if raw_vec:
                try:
                    # If it's a string, parse it
                    if isinstance(raw_vec, str):
                        # Some versions of postgrest return vectors as strings like '[0.1, 0.2]'
                        vec = json.loads(raw_vec)
                    else:
                        vec = raw_vec
                    
                    X.append(vec)
                    ids.append(item["id"])
                except Exception as e:
                    print(f"⚠️ Failed to parse embedding for {item['id']}: {e}")

        if len(X) < n_clusters:
            n_clusters = max(1, len(X))

        print(f"🤖 Performing K-Means clustering (K={n_clusters})...")
        kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
        labels = kmeans.fit_predict(X)

        return [{"id": ids[i], "cluster": int(labels[i])} for i in range(len(ids))]

    def generate_labels_for_clusters(self, clustered_data: List[Dict[str, Any]]) -> Dict[int, str]:
        """Generate human-readable labels for each cluster using Gemini."""
        cluster_groups = {}
        for item in clustered_data:
            c = item["cluster"]
            if c not in cluster_groups:
                cluster_groups[c] = []
            cluster_groups[c].append(item["id"])

        labels = {}
        for cluster_id, user_ids in cluster_groups.items():
            # Fetch sample details for these users to describe the cluster
            sample_ids = user_ids[:5]
            profiles_resp = self.client.table("profiles").select("headline, technical_skills").in_("id", sample_ids).execute()
            
            sample_text = "\n".join([
                f"- {p.get('headline')} (Skills: {p.get('technical_skills')})"
                for p in profiles_resp.data
            ])

            prompt = f"""
            You are an expert HR Talent Acquisition Specialist. 
            Analyze the following representative professional profiles from a talent pool and provide a perfect, professional job title that best encapsulates the entire group.

            CRITERIA:
            - Concise: Exactly 2-4 words.
            - Professional: Use industry-standard terminology (e.g., "Full Stack Engineer", "DevOps Architect").
            - Accurate: Reflect the common denominator in seniority and technical domain.
            - Formatting: Return ONLY the title string, no quotes, no extra text.

            REPRESENTATIVE PROFILES:
            {sample_text}

            PERFECT JOB TITLE:
            """
            
            import time
            max_retries = 3
            label = "Unknown Group"
            
            for attempt in range(max_retries):
                try:
                    response = self.gemini_client.models.generate_content(
                        model="gemini-2.5-flash-lite",
                        contents=prompt,
                        config=types.GenerateContentConfig(temperature=0)
                    )
                    label = response.text.strip().replace('"', '')
                    break
                except Exception as e:
                    if attempt < max_retries - 1:
                        wait = 2 ** (attempt + 1)
                        print(f"⚠️ Labeling failed for Cluster {cluster_id}. Retrying in {wait}s... ({e})")
                        time.sleep(wait)
                    else:
                        print(f"❌ Labeling failed for Cluster {cluster_id} after {max_retries} attempts.")

            labels[cluster_id] = label
            print(f"✅ Cluster {cluster_id} Label: {label}")
            time.sleep(1) # Small pause between clusters

        return labels

    def update_database_with_labels(self, clustered_data: List[Dict[str, Any]], cluster_labels: Dict[int, str]):
        """Update the profiles table with the new cluster labels."""
        print("💾 Updating database with cluster labels...")
        for item in clustered_data:
            user_id = item["id"]
            label = cluster_labels[item["cluster"]]
            
            self.client.table("profiles").update({"cluster_label": label}).eq("id", user_id).execute()
        print("✨ Database successfully updated.")

    def run_clustering_pipeline(self, n_clusters: int = 5):
        """Orchestrate the full clustering pipeline."""
        data = self.fetch_all_embeddings()
        clustered_results = self.perform_clustering(data, n_clusters)
        if not clustered_results:
            return
        
        labels = self.generate_labels_for_clusters(clustered_results)
        self.update_database_with_labels(clustered_results, labels)

if __name__ == "__main__":
    service = ClusteringService()
    service.run_clustering_pipeline(n_clusters=5)