backend/intelligence/llm_client.py

"""
PIOE LLM Client Abstraction Layer

Supports Gemini (default) and OpenAI as providers.
"""
from abc import ABC, abstractmethod
from typing import Optional
import json

from ..config import get_settings


class BaseLLMClient(ABC):
    """Abstract base class for LLM providers."""
    
    @abstractmethod
    def classify(self, text: str) -> dict:
        """Classify opportunity text into category and domain."""
        pass
    
    @abstractmethod
    def summarize(self, text: str, max_length: int = 150) -> str:
        """Generate concise summary of opportunity."""
        pass
    
    @abstractmethod
    def recommend_action(self, opportunity: dict) -> dict:
        """Recommend action based on opportunity context."""
        pass
    
    @abstractmethod
    def extract_metadata(self, text: str) -> dict:
        """Extract structured metadata (deadline, location, reward, etc.)."""
        pass


class GeminiClient(BaseLLMClient):
    """Google Gemini implementation."""
    
    def __init__(self, api_key: str):
        import google.generativeai as genai
        genai.configure(api_key=api_key)
        self.model = genai.GenerativeModel('gemini-2.5-flash')
    
    def _generate(self, prompt: str, as_json: bool = False) -> str:
        """Generate response from Gemini."""
        response = self.model.generate_content(prompt)
        return response.text
    
    def classify(self, text: str) -> dict:
        """Classify opportunity into category and domain."""
        prompt = f"""Analyze this opportunity and classify it. Return JSON only.

TEXT: {text[:2000]}

Return this exact JSON structure:
{{
    "category": "one of: scholarship, fellowship, internship, job, research, hackathon, competition, grant, conference, open_source, investment, weak_signal, other",
    "domain": "one of: ai, computer_vision, robotics, finance, crypto, academia, mixed",
    "confidence": 0.0 to 1.0
}}"""
        
        try:
            result = self._generate(prompt)
            # Extract JSON from response
            start = result.find('{')
            end = result.rfind('}') + 1
            if start != -1 and end > start:
                return json.loads(result[start:end])
        except Exception as e:
            print(f"Classification error: {e}")
        
        return {"category": "other", "domain": "mixed", "confidence": 0.0}
    
    def summarize(self, text: str, max_length: int = 150) -> str:
        """Generate concise summary."""
        prompt = f"""Summarize this opportunity in {max_length} characters or less. 
Focus on: what it is, who it's for, and deadline if any.

TEXT: {text[:2000]}

Return only the summary, no quotes or labels."""
        
        try:
            return self._generate(prompt).strip()[:max_length]
        except Exception as e:
            print(f"Summary error: {e}")
            return text[:max_length]
    
    def recommend_action(self, opportunity: dict) -> dict:
        """
        PIOE 2.0 Enhanced Action Guidance.
        Returns comprehensive recommendations for how to approach the opportunity.
        """
        prompt = f"""You are an expert career and opportunity advisor. Analyze this opportunity and provide detailed action guidance.

OPPORTUNITY DETAILS:
- Title: {opportunity.get('title', '')}
- Category: {opportunity.get('category', '')}
- Domain: {opportunity.get('domain', '')}
- Deadline: {opportunity.get('deadline', 'No deadline specified')}
- Description: {opportunity.get('raw_text', '')[:1500]}
- ROI Score: {opportunity.get('roi_score', 'N/A')}
- Competition Level: {opportunity.get('competition_level', 'N/A')}
- Region: {opportunity.get('region', 'global')}

USER CONTEXT:
- Location: Nigeria, Africa
- Interests: AI, Computer Vision, Robotics, Web3
- Status: Student/Early Career

Provide strategic action guidance. Return JSON only:
{{
    "primary_action": "one of: apply_now, apply_prepared, track, save_for_later, deep_research, network_first, skip",
    "urgency": "one of: immediate, this_week, this_month, whenever, expired",
    "timing_status": "one of: early, optimal, late, unknown",
    
    "skills_to_highlight": ["skill1", "skill2", "skill3"],
    "portfolio_pieces": ["project type 1", "project type 2"],
    
    "preparation_steps": [
        "step 1",
        "step 2",
        "step 3"
    ],
    
    "networking_tips": "who to contact or how to stand out (1 sentence)",
    "differentiation_angle": "what unique angle to take (1 sentence)",
    
    "success_probability": 0.0 to 1.0,
    "time_investment_hours": estimated hours to apply well,
    "risk_level": "low, medium, or high",
    
    "why": "brief strategic reasoning (max 100 chars)",
    "red_flags": ["any concerns"] or []
}}"""
        
        try:
            result = self._generate(prompt)
            start = result.find('{')
            end = result.rfind('}') + 1
            if start != -1 and end > start:
                parsed = json.loads(result[start:end])
                # Ensure required fields exist
                return {
                    "primary_action": parsed.get("primary_action", "save_for_later"),
                    "urgency": parsed.get("urgency", "whenever"),
                    "timing_status": parsed.get("timing_status", "unknown"),
                    "skills_to_highlight": parsed.get("skills_to_highlight", []),
                    "portfolio_pieces": parsed.get("portfolio_pieces", []),
                    "preparation_steps": parsed.get("preparation_steps", []),
                    "networking_tips": parsed.get("networking_tips", ""),
                    "differentiation_angle": parsed.get("differentiation_angle", ""),
                    "success_probability": parsed.get("success_probability", 0.3),
                    "time_investment_hours": parsed.get("time_investment_hours", 10),
                    "risk_level": parsed.get("risk_level", "medium"),
                    "why": parsed.get("why", "Review and decide"),
                    "red_flags": parsed.get("red_flags", []),
                }
        except Exception as e:
            print(f"Action guidance error: {e}")
        
        # Fallback response
        return {
            "primary_action": "save_for_later",
            "urgency": "whenever",
            "timing_status": "unknown",
            "skills_to_highlight": [],
            "portfolio_pieces": [],
            "preparation_steps": ["Review the opportunity details", "Assess fit with your goals"],
            "networking_tips": "",
            "differentiation_angle": "",
            "success_probability": 0.3,
            "time_investment_hours": 10,
            "risk_level": "medium",
            "why": "Needs manual review",
            "red_flags": [],
        }
    
    def extract_metadata(self, text: str) -> dict:
        """Extract structured metadata from text."""
        prompt = f"""Extract metadata from this opportunity text. Return JSON only.

TEXT: {text[:2000]}

Return this structure (use null for missing info):
{{
    "deadline": "YYYY-MM-DD or null",
    "location": "location or 'remote' or null",
    "reward": "amount or null",
    "organization": "org name or null",
    "requirements": ["skill1", "skill2"] or [],
    "url": "application url or null"
}}"""
        
        try:
            result = self._generate(prompt)
            start = result.find('{')
            end = result.rfind('}') + 1
            if start != -1 and end > start:
                return json.loads(result[start:end])
        except Exception as e:
            print(f"Metadata extraction error: {e}")
        
        return {}


class OpenAIClient(BaseLLMClient):
    """OpenAI implementation (fallback)."""
    
    def __init__(self, api_key: str):
        from openai import OpenAI
        self.client = OpenAI(api_key=api_key)
        self.model = "gpt-3.5-turbo"
    
    def _generate(self, prompt: str) -> str:
        """Generate response from OpenAI."""
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[{"role": "user", "content": prompt}],
            temperature=0.3
        )
        return response.choices[0].message.content
    
    def classify(self, text: str) -> dict:
        """Classify opportunity - same logic as Gemini."""
        prompt = f"""Classify this opportunity. Return JSON only with keys: category, domain, confidence.
Categories: scholarship, fellowship, internship, job, research, hackathon, competition, grant, conference, open_source, investment, weak_signal, other
Domains: ai, computer_vision, robotics, finance, crypto, academia, mixed

TEXT: {text[:2000]}"""
        
        try:
            result = self._generate(prompt)
            start = result.find('{')
            end = result.rfind('}') + 1
            if start != -1 and end > start:
                return json.loads(result[start:end])
        except Exception:
            pass
        return {"category": "other", "domain": "mixed", "confidence": 0.0}
    
    def summarize(self, text: str, max_length: int = 150) -> str:
        prompt = f"Summarize in {max_length} chars: {text[:2000]}"
        try:
            return self._generate(prompt).strip()[:max_length]
        except Exception:
            return text[:max_length]
    
    def recommend_action(self, opportunity: dict) -> dict:
        return {"action": "save", "reason": "Review later", "urgency": "low"}
    
    def extract_metadata(self, text: str) -> dict:
        return {}


class LLMClient:
    """
    Factory class that provides the configured LLM client.
    Uses Gemini by default, falls back to OpenAI if configured.
    """
    
    _instance: Optional[BaseLLMClient] = None
    
    @classmethod
    def get_client(cls) -> BaseLLMClient:
        """Get or create the LLM client instance."""
        if cls._instance is None:
            settings = get_settings()
            
            if settings.ai_provider == "gemini" and settings.gemini_api_key:
                cls._instance = GeminiClient(settings.gemini_api_key)
            elif settings.openai_api_key:
                cls._instance = OpenAIClient(settings.openai_api_key)
            else:
                # Return a mock client if no API keys configured
                cls._instance = MockLLMClient()
        
        return cls._instance


class MockLLMClient(BaseLLMClient):
    """Mock client for development without API keys. PIOE 2.0 compatible."""
    
    def classify(self, text: str) -> dict:
        # Basic rule-based classification
        text_lower = text.lower()
        
        if any(kw in text_lower for kw in ["scholarship", "fellowship", "grant"]):
            return {"category": "scholarship", "domain": "academia", "confidence": 0.7}
        elif any(kw in text_lower for kw in ["hackathon", "competition", "challenge"]):
            return {"category": "hackathon", "domain": "ai", "confidence": 0.7}
        elif any(kw in text_lower for kw in ["internship", "intern"]):
            return {"category": "internship", "domain": "mixed", "confidence": 0.7}
        elif any(kw in text_lower for kw in ["job", "hiring", "position"]):
            return {"category": "job", "domain": "mixed", "confidence": 0.7}
        elif any(kw in text_lower for kw in ["bounty", "ecosystem", "solana", "ethereum"]):
            return {"category": "bounty", "domain": "crypto", "confidence": 0.7}
        elif any(kw in text_lower for kw in ["pitch", "demo day", "accelerator"]):
            return {"category": "pitch_event", "domain": "mixed", "confidence": 0.7}
        elif any(kw in text_lower for kw in ["collaborat", "partner", "looking for"]):
            return {"category": "collaboration", "domain": "mixed", "confidence": 0.6}
        
        return {"category": "other", "domain": "mixed", "confidence": 0.3}
    
    def summarize(self, text: str, max_length: int = 150) -> str:
        return text[:max_length]
    
    def recommend_action(self, opportunity: dict) -> dict:
        """PIOE 2.0 action guidance - rule-based fallback."""
        category = opportunity.get("category", "other")
        
        # Category-based action mapping
        action_map = {
            "hackathon": ("apply_now", "this_week", ["Python", "ML/AI"], ["Previous hackathon project"]),
            "grant": ("apply_prepared", "this_month", ["Technical writing", "Project planning"], ["Open source contributions"]),
            "ecosystem_grant": ("apply_prepared", "this_month", ["Solidity/Rust", "Web3"], ["DApp or smart contract"]),
            "internship": ("apply_now", "this_week", ["Relevant coursework", "Projects"], ["GitHub portfolio"]),
            "scholarship": ("apply_prepared", "this_month", ["Academic excellence", "Leadership"], ["Research paper or thesis"]),
            "bounty": ("apply_now", "immediate", ["Specific tech stack"], ["Related code samples"]),
            "pitch_event": ("apply_prepared", "this_month", ["Presentation", "Business model"], ["Pitch deck", "Demo video"]),
            "collaboration": ("network_first", "whenever", ["Domain expertise"], ["Relevant projects"]),
        }
        
        action, urgency, skills, portfolio = action_map.get(
            category, 
            ("save_for_later", "whenever", [], [])
        )
        
        return {
            "primary_action": action,
            "urgency": urgency,
            "timing_status": "unknown",
            "skills_to_highlight": skills,
            "portfolio_pieces": portfolio,
            "preparation_steps": [
                "Review the opportunity requirements",
                "Prepare relevant materials",
                "Submit before deadline"
            ],
            "networking_tips": "Research the organization and connect with past participants",
            "differentiation_angle": "Highlight unique projects and Africa/Nigeria perspective",
            "success_probability": 0.3,
            "time_investment_hours": 10,
            "risk_level": "medium",
            "why": f"Standard approach for {category}",
            "red_flags": [],
        }
    
    def extract_metadata(self, text: str) -> dict:
        return {}