Update app.py

app.py

@@ -1,33 +1,45 @@
 import gradio as gr
 from transformers import pipeline
 
-hate = pipeline("text-classification", model="hossam87/bert-base-arabic-hate-speech", tokenizer="hossam87/bert-base-arabic-hate-speech", return_all_scores=False)
-dialect = pipeline("text-classification", model="IbrahimAmin/marbertv2-arabic-written-dialect-classifier", tokenizer="IbrahimAmin/marbertv2-arabic-written-dialect-classifier", return_all_scores=False)
-
-def analyze(text):
-    # Hate speech detection
-    hate_res = hate(text)[0]
-    hate_label = hate_res['label']
-    hate_conf = hate_res['score']
-
-    # Dialect detection
-    dial_res = dialect(text)[0]
-    dial_label = dial_res['label']
-    dial_conf = dial_res['score']
-
-    # Threat score
-    weight = {"Neutral":0, "Offensive":0.5, "Sexism":1, "Racism":1, "Religious Discrimination":1}
-    score = hate_conf * weight.get(hate_label, 0)
-
-    # Recommended action (modified logic)
-    if hate_label != "Neutral":
-        action = "🚨 Immediate Review Required — This content contains severe hate speech or threats and should be escalated to moderators immediately."
+# ---------- Pipelines ----------
+hate = pipeline(
+    "text-classification",
+    model="hossam87/bert-base-arabic-hate-speech",
+    tokenizer="hossam87/bert-base-arabic-hate-speech",
+    return_all_scores=False
+)
+
+dialect = pipeline(
+    "text-classification",
+    model="IbrahimAmin/marbertv2-arabic-written-dialect-classifier",
+    tokenizer="IbrahimAmin/marbertv2-arabic-written-dialect-classifier",
+    return_all_scores=False
+)
+
+# ---------- Inference ----------
+def analyze(text: str):
+    if not text or not text.strip():
+        return ("", "", "", "", "", "Please enter some Arabic text.")
+
+    h = hate(text)[0]
+    d = dialect(text)[0]
+
+    hate_label, hate_conf = h.get("label",""), float(h.get("score",0))
+    dial_label, dial_conf = d.get("label",""), float(d.get("score",0))
+
+    weights = {"Neutral":0.0, "Offensive":0.5, "Sexism":1.0, "Racism":1.0, "Religious Discrimination":1.0}
+    score = hate_conf * weights.get(hate_label, 0.0)
+
+    if hate_label != "Neutral" and weights.get(hate_label,0.0) >= 1.0:
+        action = "🚨 Immediate Review — Severe content detected. Escalate to moderators."
+    elif hate_label != "Neutral":
+        action = "⚠️ Potentially Harmful — Contains offensive content. Please review."
     elif score >= 0.49:
-        action = "⚠️ Potentially Harmful — The content may contain offensive or harmful language. Please review before taking further action."
+        action = "⚠️ Borderline — Review recommended."
     else:
-        action = "✅ Safe Content — No harmful language detected. No moderation needed."
+        action = "✅ Safe — No action needed."
 
-    return hate_label, f"{hate_conf:.2f}", dial_label, f"{dial_conf:.2f}", f"{score:.2f}", action
+    return (hate_label, f"{hate_conf:.2f}", dial_label, f"{dial_conf:.2f}", f"{score:.2f}", action)
 
 # ---------- CSS ----------
 CSS = """
@@ -73,10 +85,10 @@ with gr.Blocks(css=CSS, theme="default") as demo:
                 - **Threat Severity Score** (0–1 based on label + confidence)
                 - **Recommended Action** (rule-based suggestion)
 
-                **How to Use:**
-                1. Enter Arabic text in the box.
-                2. Click **Analyze**.
-                3. Review the results and suggested action.
+                **How to Use**
+                1) Enter Arabic text in the box.  
+                2) Click **Analyze**.  
+                3) Review the results and suggested action.
                 """)
 
         with gr.Column(scale=3):