Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,25 +1,22 @@
 import os
 import streamlit as st
 from transformers import pipeline
-from huggingface_hub import login
 
-# Optional if running in container: ensure HF cache is writeable
-os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"
-os.environ["HF_HOME"] = "/tmp/huggingface"
+# ✅ Set HF cache directory to a local writable path
+cache_dir = os.path.join(os.getcwd(), "hf_cache")
+os.makedirs(cache_dir, exist_ok=True)
+os.environ["TRANSFORMERS_CACHE"] = cache_dir
+os.environ["HF_HOME"] = cache_dir
 
-# ✅ Login once; not needed if you're already logged in via CLI
-# Remove this line if using HF Spaces or if already logged in
-# login(token=os.environ.get("HF_TOKEN"))
-
-# ✅ Load model (no use_auth_token needed if logged in)
+# ✅ Load pipeline (no token needed if logged in)
 pipe = pipeline(
     "text-classification",
     model="t-Shr/SPAM_OR_HAM_SMS"
 )
 
-# 🧠 Spam Prediction Logic
+# 🧠 Prediction function
 def predict(text):
-    trust_score = 0.5  # Fixed for now
+    trust_score = 0.5
     output = pipe(text)[0]
     model_prob = output['score'] if output['label'] == 'LABEL_1' else 1 - output['score']
     alpha = 0.7
@@ -28,20 +25,18 @@ def predict(text):
     label = "SPAM" if fused_score >= 0.5 else "NOT SPAM"
     return label, round(model_prob, 4), round(fused_score, 4), risk_score
 
-# 🎨 UI Config
+# 🌐 Streamlit UI
 st.set_page_config(page_title="SMS Spam Detector", layout="centered")
 st.title("📩 Real-Time SMS Spam Detector")
 st.markdown("Enter an SMS message to check if it's likely spam.")
 
-# 📝 User Input
 sms_text = st.text_area("✉️ Enter SMS Text:", height=150)
 
-# 🔍 Prediction
 if st.button("🔍 Predict"):
     if sms_text.strip() == "":
         st.warning("Please enter some text.")
     else:
-        label, confidence, fused, risk = predict(sms_text)
+        label, confidence, fused_score, risk_score = predict(sms_text)
 
         if label == "SPAM":
             st.markdown("### 🟥 Prediction: **`SPAM`**")
@@ -49,5 +44,5 @@ if st.button("🔍 Predict"):
             st.markdown("### 🟩 Prediction: **`NOT SPAM`**")
 
         st.metric("📊 Model Confidence", f"{confidence:.2f}")
-        st.metric("🔁 Fused Score", f"{fused:.2f}")
-        st.metric("⚠️ Risk Score", f"{risk}/100")
+        st.metric("🔁 Fused Score", f"{fused_score:.2f}")
+        st.metric("⚠️ Risk Score", f"{risk_score}/100")