Update app.py

app.py

@@ -60,13 +60,14 @@ def sentence_split(text: str):
 
 # -----------------------------
 # CLASSIFY SENTENCE-BY-SENTENCE (Fakespot: id2label[1] == "AI")
+# Also compute an overall Likely-AI score (mean AI prob)
 # -----------------------------
 def classify_sentence_by_sentence(text, threshold=0.70, max_len=512):
     sents = sentence_split(text)
     if not sents:
-        return "⚠️ Please paste some text.", None, None
+        return "⚠️ Please paste some text.", "", "", None, None
 
-    # (Optional) light cleaning similar to model card guidance
+    # light cleaning similar to model card guidance
     clean_sents = [re.sub(r"\s+", " ", s).strip() for s in sents]
 
     inputs = tokenizer(
@@ -77,10 +78,15 @@ def classify_sentence_by_sentence(text, threshold=0.70, max_len=512):
         logits = model(**inputs).logits
         probs = F.softmax(logits, dim=-1)  # [:,0]=Human, [:,1]=AI
 
+    ai_probs = probs[:, 1].detach().cpu().tolist()
+    overall_ai = sum(ai_probs) / len(ai_probs)  # mean AI probability across sentences
+    overall_pct = f"{overall_ai * 100:.1f}%"
+    overall_label = "🤖 Likely AI" if overall_ai >= threshold else "🧒 Likely Human"
+
     rows = []
     highlights = []
     for i, orig in enumerate(sents, start=1):
-        ai_p = float(probs[i-1, 1].item())  # Fakespot: 1 == AI
+        ai_p = float(ai_probs[i-1])  # Fakespot: 1 == AI
         label = "AI" if ai_p >= threshold else "Human"
         pct = f"{ai_p*100:.1f}%"
 
@@ -101,10 +107,12 @@ def classify_sentence_by_sentence(text, threshold=0.70, max_len=512):
 
     html = "\n".join(highlights)
     df = pd.DataFrame(rows, columns=["#", "Sentence", "AI_Prob", "Label"])
-    return "Done ✅ (Fakespot detector)", html, df
+
+    status = "Done ✅ (Fakespot detector)"
+    return status, overall_label, overall_pct, html, df
 
 # -----------------------------
-# GRADIO UI (minimal)
+# GRADIO UI (minimal, with overall score)
 # -----------------------------
 with gr.Blocks() as demo:
     gr.Markdown("### 🧠 Sentence-by-Sentence AI Check (Fakespot)")
@@ -114,11 +122,16 @@ with gr.Blocks() as demo:
     btn = gr.Button("Analyze")
 
     status = gr.Label(label="Status")
+    overall_label = gr.Label(label="Overall Verdict")
+    overall_score = gr.Label(label="Likely AI-generated Score (avg)")
     highlights = gr.HTML(label="Per-Sentence Highlights")
     table = gr.Dataframe(headers=["#", "Sentence", "AI_Prob", "Label"], wrap=True)
 
-    btn.click(classify_sentence_by_sentence, inputs=[text_input, threshold],
-              outputs=[status, highlights, table])
+    btn.click(
+        classify_sentence_by_sentence,
+        inputs=[text_input, threshold],
+        outputs=[status, overall_label, overall_score, highlights, table]
+    )
 
 if __name__ == "__main__":
     demo.launch()