app.py

from fastapi import FastAPI
from pydantic import BaseModel
import joblib
import logging
from huggingface_hub import hf_hub_download
from transformers import pipeline

logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")

app = FastAPI()
models = None

def load_models():
    global models
    if models is not None:
        return
    logging.info("Loading models...")

    en_vectorizer = joblib.load(hf_hub_download("E-motionAssistant/Englsih_Trained_Model_LR", "tfidf_vectorizer.joblib"))
    en_classifier = joblib.load(hf_hub_download("E-motionAssistant/Englsih_Trained_Model_LR", "logreg_model.joblib"))
    en_label_encoder = joblib.load(hf_hub_download("E-motionAssistant/Englsih_Trained_Model_LR", "label_encoder.joblib"))

    si_vectorizer = joblib.load(hf_hub_download("E-motionAssistant/Sinhala_Text_Emotion_Model_LR", "tfidf_vectorizer.joblib"))
    si_classifier = joblib.load(hf_hub_download("E-motionAssistant/Sinhala_Text_Emotion_Model_LR", "logreg_model.joblib"))
    si_label_encoder = joblib.load(hf_hub_download("E-motionAssistant/Sinhala_Text_Emotion_Model_LR", "label_encoder.joblib"))

    tamil_pipe = pipeline("text-classification", model="E-motionAssistant/Tamil_Emotion_Recognition_Model", device=-1)

    models = (en_vectorizer, en_classifier, en_label_encoder,
              si_vectorizer, si_classifier, si_label_encoder, tamil_pipe)
    logging.info("✅ All models loaded.")

@app.on_event("startup")
def startup_event():
    load_models()

class PredictRequest(BaseModel):
    text: str
    language: str  # "English", "Sinhala", or "Tamil"

@app.get("/")
def root():
    return {"status": "ok", "message": "Emotion Detector API is running"}

@app.post("/predict")
def predict(req: PredictRequest):
    if not req.text.strip():
        return {"error": "Text cannot be empty"}

    en_vec, en_clf, en_le, si_vec, si_clf, si_le, tamil_pipe = models

    try:
        if req.language == "English":
            X = en_vec.transform([req.text])
            pred = en_clf.predict(X)[0]
            emotion = en_le.inverse_transform([pred])[0]
            return {"emotion": emotion, "language": "English"}

        elif req.language == "Sinhala":
            X = si_vec.transform([req.text])
            pred = si_clf.predict(X)[0]
            emotion = si_le.inverse_transform([pred])[0]
            return {"emotion": emotion, "language": "Sinhala"}

        elif req.language == "Tamil":
            res = tamil_pipe(req.text)[0]
            return {"emotion": res["label"], "confidence": round(res["score"], 3), "language": "Tamil"}

        else:
            return {"error": f"Unsupported language: {req.language}"}

    except Exception as e:
        logging.error(f"Prediction error: {e}")
        return {"error": str(e)}