import os
import streamlit as st
from transformers import pipeline

# Redirect cache
cache_dir = os.path.join(os.getcwd(), "/tmp/hf_cache")
os.makedirs(cache_dir, exist_ok=True)
os.environ["TRANSFORMERS_CACHE"] = cache_dir
os.environ["HF_HOME"] = cache_dir

# Load model (ensure it's public or token is handled securely)
pipe = pipeline(
    "text-classification",
    model="t-Shr/SPAM_OR_HAM_SMS"  # 🔒 make sure it's public or token is handled
)

def predict(text):
    trust_score = 0.5
    output = pipe(text)[0]
    prob = output['score'] if output['label'] == 'LABEL_1' else 1 - output['score']
    fused = 0.7 * prob + 0.3 * (1 - trust_score)
    risk = int(round(fused * 100))
    label = "SPAM" if fused >= 0.5 else "NOT SPAM"
    return label, round(prob, 4), round(fused, 4), risk

st.set_page_config(page_title="SMS Spam Detector", layout="centered")
st.title("📩 Real-Time SMS Spam Detector")
sms = st.text_area("✉️ Enter SMS:", height=150)

if st.button("🔍 Predict"):
    if sms.strip():
        label, prob, fused, risk = predict(sms)
        st.markdown(f"### {'🟥' if label == 'SPAM' else '🟩'} Prediction: `{label}`")
        st.metric("Confidence", f"{prob:.2f}")
        st.metric("Fused Score", f"{fused:.2f}")
        st.metric("Risk Score", f"{risk}/100")
    else:
        st.warning("Please enter SMS text.")