# ===============================================
# 🌸 BLOOM CHECK-IN QUALITY CLASSIFIER API (FastAPI)
# ===============================================

from fastapi import FastAPI, Request
from transformers import pipeline
import uvicorn

app = FastAPI(
    title="Bloom Check-in Quality Classifier",
    description="A FastAPI app that classifies check-ins as vague, neutral, or descriptive.",
    version="1.0.0"
)

# Load the Hugging Face text classification model
# This automatically downloads your public model from the Hub
classifier = pipeline("text-classification", model="user6295018/checkin-quality-classifier")

@app.get("/")
def read_root():
    """
    Root endpoint – simple health check
    """
    return {"message": "Bloom Check-in Quality Classifier is running!"}

@app.post("/api/predict")
async def predict(request: Request):
    """
    Predict endpoint – classify input text using the fine-tuned model
    """
    try:
        data = await request.json()
    except Exception:
        return {"error": "Invalid JSON body."}

    # Accept either {"text": "..."} or {"inputs": "..."}
    text = data.get("text") or data.get("inputs")

    if not text:
        return {"error": "No text provided."}

    try:
        # Run model inference
        result = classifier(text)[0]

        # Optional: Map model output labels to human-friendly names
        label_map = {
            "LABEL_0": "vague",
            "LABEL_1": "neutral",
            "LABEL_2": "descriptive"
        }
        label = label_map.get(result["label"], result["label"])
        score = round(float(result["score"]), 3)

        return {"label": label, "score": score}

    except Exception as e:
        return {"error": f"Inference failed: {str(e)}"}

# Optional: local testing entry point (ignored on Spaces)
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)