# app/services/sms_service.py
import pickle
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from fastapi import HTTPException
from schemas.input_schemas import CosineSimilarityResponse
from schemas.input_schemas import EmbeddingResponse


# Load the trained model and vectorizer
def load_model():
    model_path = "models/sms_classifier_model.pkl"
    vectorizer_path = "models/tfidf_vectorizer.pkl"

    try:
        with open(model_path, 'rb') as f:
            classifier = pickle.load(f)

        with open(vectorizer_path, 'rb') as f:
            vectorizer = pickle.load(f)

        return classifier, vectorizer
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error loading model: {str(e)}")

async def predict_label(message: str):
    try:
        classifier, vectorizer = load_model()
        # Vectorize the input message
        message_vec = vectorizer.transform([message])

        # Predict the label
        label = classifier.predict(message_vec)[0]
        return {"label": label}
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error predicting label: {str(e)}")

async def compute_cosine_similarity(text1: str, text2: str):
    try:
        classifier, vectorizer = load_model()

        # Vectorize the input texts
        vec1 = vectorizer.transform([text1]).toarray()
        vec2 = vectorizer.transform([text2]).toarray()

        # Compute cosine similarity
        cosine_sim = np.dot(vec1, vec2.T) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
        return CosineSimilarityResponse(cosine_similarity=cosine_sim[0][0])
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error computing similarity: {str(e)}")

async def compute_embeddings(message: str):
    try:
        classifier, vectorizer = load_model()

        # Vectorize the input message
        embedding = vectorizer.transform([message]).toarray().tolist()
        return EmbeddingResponse(embeddings=embedding)
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error computing embeddings: {str(e)}")