from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import pipeline
import logging
import os

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = FastAPI()

# Set transformers cache directory to a writable location to fix permission errors
os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"

# Load models once on startup with error handling
try:
    ner_model = pipeline("ner", model="dslim/bert-base-NER", aggregation_strategy="simple")
    sentiment_model = pipeline("sentiment-analysis", model="ProsusAI/finbert")
    logger.info("Models loaded successfully.")
except Exception as e:
    logger.error(f"Model loading failed: {e}")
    ner_model = None
    sentiment_model = None

class TextRequest(BaseModel):
    text: str

@app.get("/")
def home():
    return {"message": "Crypto News API is alive!"}

@app.post("/sentiment")
def analyze_sentiment(req: TextRequest):
    if not sentiment_model:
        raise HTTPException(status_code=503, detail="Sentiment model not available")
    text = req.text.strip()
    if not text:
        raise HTTPException(status_code=400, detail="Text cannot be empty")
    try:
        result = sentiment_model(text[:512])[0]
        return {
            "label": result["label"],
            "score": round(result["score"] * 100, 2)
        }
    except Exception as e:
        logger.error(f"Sentiment analysis error: {e}")
        raise HTTPException(status_code=500, detail="Sentiment analysis failed")

@app.post("/ner")
def analyze_ner(req: TextRequest):
    if not ner_model:
        raise HTTPException(status_code=503, detail="NER model not available")
    text = req.text.strip()
    if not text:
        raise HTTPException(status_code=400, detail="Text cannot be empty")
    try:
        entities = ner_model(text[:512])
        # Filter relevant entity groups
        relevant = [e['word'] for e in entities if e.get('entity_group') in ['ORG', 'PERSON', 'MISC', 'PRODUCT', 'GPE']]
        # Remove duplicates preserving order, limit to 5 entities
        unique_entities = list(dict.fromkeys(relevant))[:5]
        return {"entities": unique_entities}
    except Exception as e:
        logger.error(f"NER analysis error: {e}")
        raise HTTPException(status_code=500, detail="NER analysis failed")