Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,52 +1,47 @@
 import os
 import streamlit as st
 from transformers import pipeline
+from huggingface_hub import login
 
-# 🧩 Ensure cache is writable (for Docker or hosted env)
+# Optional if running in container: ensure HF cache is writeable
 os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"
 os.environ["HF_HOME"] = "/tmp/huggingface"
 
-from huggingface_hub import login
-login(token=os.environ.get("HF_TOKEN"))
+# ✅ 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 pipeline using Hugging Face token
+# ✅ Load model (no use_auth_token needed if logged in)
 pipe = pipeline(
     "text-classification",
-    model="t-Shr/SPAM_OR_HAM_SMS",
-    use_auth_token=os.environ.get("HF_TOKEN")  # Make sure HF_TOKEN is set
+    model="t-Shr/SPAM_OR_HAM_SMS"
 )
 
-# 🧠 Prediction function
-def predict(text, trust_score=0.5):
+# 🧠 Spam Prediction Logic
+def predict(text):
+    trust_score = 0.5  # Fixed for now
     output = pipe(text)[0]
-    label_raw = output['label']
-    model_prob = output['score'] if label_raw == 'LABEL_1' else 1 - output['score']
-
+    model_prob = output['score'] if output['label'] == 'LABEL_1' else 1 - output['score']
     alpha = 0.7
-    fused = alpha * model_prob + (1 - alpha) * (1 - trust_score)
-    risk = int(round(fused * 100))
-    label = "SPAM" if fused >= 0.5 else "NOT SPAM"
+    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
 
-    return label, round(model_prob, 4), round(fused, 4), risk
-
-# 🌐 Streamlit UI setup
+# 🎨 UI Config
 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.")
 
-st.markdown("Enter an SMS message to check if it's likely spam. The system will show:")
-st.markdown("- **📊 Model Confidence**")
-st.markdown("- **🔁 Fused Score**")
-st.markdown("- **⚠️ Risk Score out of 100**")
-
-# 📝 Text input
+# 📝 User Input
 sms_text = st.text_area("✉️ Enter SMS Text:", height=150)
 
-# 🔍 Predict button
+# 🔍 Prediction
 if st.button("🔍 Predict"):
     if sms_text.strip() == "":
         st.warning("Please enter some text.")
     else:
-        label, confidence, fused_score, risk_score = predict(sms_text)
+        label, confidence, fused, risk = predict(sms_text)
 
         if label == "SPAM":
             st.markdown("### 🟥 Prediction: **`SPAM`**")
@@ -54,5 +49,5 @@ if st.button("🔍 Predict"):
             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.metric("🔁 Fused Score", f"{fused:.2f}")
+        st.metric("⚠️ Risk Score", f"{risk}/100")