low-margin uncertainty detection

src/streamlit_app.py

@@ -1,6 +1,6 @@
 import os
 
-# ✅ Set writable directories for cache and metrics
+# ✅ Fix: Use writable directories for cache and metrics
 os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf"
 os.environ["HF_HOME"] = "/tmp/hf"
 os.environ["STREAMLIT_HOME"] = "/tmp/.streamlit"
@@ -10,14 +10,15 @@ os.makedirs("/tmp/.streamlit", exist_ok=True)
 import streamlit as st
 from transformers import pipeline
 
-# 🔐 Load your Hugging Face token from Space secrets
+# 🔐 Load Hugging Face token from Space secrets
 HF_TOKEN = os.getenv("HF_TOKEN")
 
 # 🔁 Load the private model using the token
 classifier = pipeline(
     "text-classification",
     model="azratuni/isl-classifier",
-    token=HF_TOKEN
+    token=HF_TOKEN,
+    return_all_scores=True  # ✅ Return all class scores
 )
 
 # 🧠 Streamlit UI
@@ -30,11 +31,31 @@ text_input = st.text_area("Input text", placeholder="e.g. Muslims are terrorists
 if st.button("Classify"):
     if text_input.strip():
         with st.spinner("Classifying..."):
-            result = classifier(text_input)[0]
-            label = result["label"]  # Now already in friendly form
-            score = result["score"]
+            scores = classifier(text_input)[0]
+            scores = sorted(scores, key=lambda x: x["score"], reverse=True)
+
+            top_label = scores[0]["label"]
+            top_score = scores[0]["score"]
+            second_score = scores[1]["score"]
+            margin = top_score - second_score
+
+            label_map = {
+                "LABEL_0": "Not Islamophobic",
+                "LABEL_1": "Islamophobic"
+            }
+
+            label = label_map.get(top_label, top_label)
 
             st.success(f"**Prediction:** {label}")
-            st.write(f"**Confidence:** {score:.2%}")
+            st.write(f"**Confidence:** {top_score:.2%}")
+
+            # ⚠️ Uncertainty warning if margin is low
+            if margin < 0.15:
+                st.warning("⚠️ The model is uncertain. Both classes received similar confidence scores.")
+
+            # 🧾 Optional: show both scores
+            st.markdown("**Score breakdown:**")
+            for s in scores:
+                st.write(f"- {label_map.get(s['label'], s['label'])}: {s['score']:.2%}")
     else:
         st.warning("Please enter some text.")