Update app.py

app.py

@@ -1,17 +1,30 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load your model with full label scoring
 model = "VerifiedPrompts/CNTXT-Filter-Prompt-Opt"
 classifier = pipeline("text-classification", model=model, return_all_scores=True)
 
 def classify(prompt):
-    results = classifier(prompt)[0]  # list of all label scores
-    for r in results:
-        print(f"{r['label']} → {round(r['score'], 3)}")  # optional log
-    return "\n".join([f"{r['label']}: {round(r['score'], 3)}" for r in results])
+    results = classifier(prompt)[0]
+
+    # Map model labels to readable names
+    label_map = {
+        "LABEL_0": "Intent is unclear, Please input more context",
+        "LABEL_1": "has context",
+        "LABEL_2": "missing platform, audience, budget, goal"
+    }
+
+    # Convert list of label-score dicts into a usable dict
+    score_dict = {r["label"]: r["score"] for r in results}
+
+    # 🔒 Priority logic: if label_0 score > 0, block it
+    if score_dict["LABEL_0"] > 0:
+        return "Intent is unclear"
+
+    # Otherwise, return the top label
+    top_label = max(results, key=lambda r: r["score"])["label"]
+    return label_map[top_label]
 
-# Gradio UI
 demo = gr.Interface(
     fn=classify,
     inputs="textbox",