from fastapi import APIRouter, HTTPException, UploadFile, File
import pypdf
import io
from pydantic import BaseModel
from app.services.ml_pipeline import ml_pipeline
from app.services.db_service import db_service
import random
import requests

router = APIRouter()

class ArticleRequest(BaseModel):
    text: str
    title: str = "Uploaded Article"

@router.post("/analyze/article")
def analyze_article(request: ArticleRequest):
    if not request.text:
         raise HTTPException(status_code=400, detail="Text is required")
         
    try:
        # Run ML analysis
        analysis = ml_pipeline.analyze_article(request.text)
        
        article_data = {
            "title": request.title,
            "text_snippet": request.text[:100] + "...",
            **analysis
        }
        
        # Save to db
        db_service.save_analysis(article_data)
        
        # PyMongo mutates the dict and adds an ObjectId under '_id'. Pop it before returning!
        article_data.pop('_id', None)
        
        return article_data
    except Exception as e:
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")

@router.post("/analyze/pdf")
async def analyze_pdf(file: UploadFile = File(...)):
    if not file.filename.lower().endswith(".pdf"):
        raise HTTPException(status_code=400, detail="Only PDF files are supported")
        
    try:
        content = await file.read()
        import fitz
        import numpy as np
        
        doc = fitz.open(stream=content, filetype="pdf")
        
        extracted_text = ""
        for page in doc:
            extracted_text += page.get_text() + "\n"
            
        if len(extracted_text.strip()) < 50:
            import easyocr
            from PIL import Image
            
            img_texts = []
            reader = easyocr.Reader(['en'], gpu=False)
            for i in range(min(3, len(doc))):
                page = doc[i]
                pix = page.get_pixmap()
                img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
                result = reader.readtext(np.array(img))
                img_texts.extend([res[1] for res in result])
                
            extracted_text = " ".join(img_texts)
            
            if len(extracted_text.strip()) < 50:
                raise HTTPException(
                    status_code=400, 
                    detail="Could not extract enough text from the PDF even with OCR. Please ensure the document is clear."
                )
            
        # Limit text for analysis
        text_to_analyze = extracted_text[:5000] # Process first 5k chars for speed
        
        analysis = ml_pipeline.analyze_article(text_to_analyze)
        
        article_data = {
            "title": file.filename,
            "text_snippet": text_to_analyze[:100] + "...",
            **analysis
        }
        
        db_service.save_analysis(article_data)
        
        # Pop PyMongo ObjectId before JSON serialization
        article_data.pop('_id', None)
        
        return article_data
        
    except Exception as e:
        import traceback
        traceback.print_exc()
        if hasattr(e, 'status_code'):
            raise e
        raise HTTPException(status_code=500, detail=f"PDF extraction failed: {str(e)}")

@router.get("/graph/narratives")
def get_narratives_graph():
    return db_service.get_graph_data()

import time
_trending_cache = {"time": 0, "data": None}

@router.get("/news/trending")
def get_trending_news():
    global _trending_cache
    if time.time() - _trending_cache["time"] < 600 and _trending_cache["data"]:
        return _trending_cache["data"]

    api_key = "6065e35156724b269f88dcaa2dab2777"
    url = f"https://newsapi.org/v2/top-headlines?country=us&apiKey={api_key}"
    
    try:
        response = requests.get(url, timeout=5)
        data = response.json()
        articles = data.get("articles", [])[:3]  # Process max 3 articles to avoid CPU hang
        
        trending_topics = []
        cluster_counts = {}
        highest_fake_prob = 0
        most_emotional_topic = "General News"
        
        for art in articles:
            text = str(art.get('title', '')) + " " + str(art.get('description', ''))
            if len(text.strip()) < 10:
                continue
                
            analysis = ml_pipeline.analyze_article(text)
            
            topic = analysis.get("topic", "General")
            fake_prob = analysis.get("fake_prob", 0.1)
            emotion = analysis.get("emotion", "neutral")
            
            cluster = analysis.get("cluster", "General")
            cluster_counts[cluster] = cluster_counts.get(cluster, 0) + 1
            
            if fake_prob > highest_fake_prob:
                highest_fake_prob = fake_prob
                most_emotional_topic = topic
                
            # Avoid massive duplicate topics
            if topic not in [t['name'] for t in trending_topics]:
                trending_topics.append({
                    "name": topic[:30] + ("..." if len(topic) > 30 else ""),
                    "fake_prob": fake_prob,
                    "emotion": emotion
                })

        # Sort clusters by frequency
        growing_clusters = sorted(cluster_counts.keys(), key=lambda k: cluster_counts[k], reverse=True)[:3]
        
        insights = f"Real-time scan resolved {len(trending_topics)} top entities. The cluster '{growing_clusters[0] if growing_clusters else 'General'}' is currently expanding heavily. Maximum deception probability detected in '{most_emotional_topic}'."
        
        # fallback if API returned empty
        if not trending_topics:
            raise Exception("No topics extracted")
            
        result = {
            "trending_topics": trending_topics[:5],
            "growing_clusters": growing_clusters,
            "insights": insights
        }
        _trending_cache["time"] = time.time()
        _trending_cache["data"] = result
        return result

    except Exception as e:
        import traceback
        traceback.print_exc()
        print(f"News API Error details: {e}")
        # Default fallback
        return {
            "trending_topics": [
                {"name": "API Connection Disrupted", "fake_prob": 0.5, "emotion": "sadness"}
            ],
            "growing_clusters": ["System"],
            "insights": f"News stream offline or analyzing failed: {e}. Reverting to static analytics."
        }