Update src/streamlit_app.py

src/streamlit_app.py

@@ -2,47 +2,37 @@ import os
 import streamlit as st
 from transformers import pipeline
 
-# ✅ Set HF cache directory to a local writable path
+# Redirect cache
 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
 
-# ✅ Load pipeline (no token needed if logged in)
+# Load model (ensure it's public or token is handled securely)
 pipe = pipeline(
     "text-classification",
-    model="t-Shr/SPAM_OR_HAM_SMS"
+    model="t-Shr/SPAM_OR_HAM_SMS"  # 🔒 make sure it's public or token is handled
 )
 
-# 🧠 Prediction function
 def predict(text):
     trust_score = 0.5
     output = pipe(text)[0]
-    model_prob = output['score'] if output['label'] == 'LABEL_1' else 1 - output['score']
-    alpha = 0.7
-    fused_score = alpha * model_prob + (1 - alpha) * (1 - trust_score)
-    risk_score = int(round(fused_score * 100))
-    label = "SPAM" if fused_score >= 0.5 else "NOT SPAM"
-    return label, round(model_prob, 4), round(fused_score, 4), risk_score
+    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
 
-# 🌐 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.")
-
-sms_text = st.text_area("✉️ Enter SMS Text:", height=150)
+sms = st.text_area("✉️ Enter SMS:", height=150)
 
 if st.button("🔍 Predict"):
-    if sms_text.strip() == "":
-        st.warning("Please enter some text.")
+    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:
-        label, confidence, fused_score, risk_score = predict(sms_text)
-
-        if label == "SPAM":
-            st.markdown("### 🟥 Prediction: **`SPAM`**")
-        else:
-            st.markdown("### 🟩 Prediction: **`NOT SPAM`**")
-
-        st.metric("📊 Model Confidence", f"{confidence:.2f}")
-        st.metric("🔁 Fused Score", f"{fused_score:.2f}")
-        st.metric("⚠️ Risk Score", f"{risk_score}/100")
+        st.warning("Please enter SMS text.")