updated

app.py

@@ -10,6 +10,8 @@ import torch
 import numpy as np
 from typing import List, Dict, Tuple
 import logging
+from transformers import pipeline
+
 
 # Setup logging
 logging.basicConfig(level=logging.INFO)
@@ -32,11 +34,24 @@ t5_tokenizer = AutoTokenizer.from_pretrained("Vamsi/T5_Paraphrase_Paws")
 t5_model = AutoModelForSeq2SeqLM.from_pretrained("Vamsi/T5_Paraphrase_Paws")
 t5_model.to(device)
 
+
 similarity_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", device=device)
 nlp = spacy.load("en_core_web_sm")
 
+
+ai_detector_pipe = pipeline("text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
+
 print("Models loaded successfully!")
 
+
+
+# ============================================================================
+# AI Detection
+# ============================================================================
+def predict_ai_content(text):
+    res = ai_detector_pipe(text)[0]
+    return res['label'], res['score']
+
 # ============================================================================
 # STAGE 1: PARAPHRASING WITH T5 MODEL
 # ============================================================================
@@ -405,6 +420,7 @@ def humanize_text(
         
         # Calculate similarity
         similarity = calculate_similarity(input_text, result)
+        ai_content_label, ai_content_score = predict_ai_content(result)
         
         # Generate status message
         if not stages_applied:
@@ -412,7 +428,7 @@ def humanize_text(
         else:
             status = f"✅ Successfully applied: {', '.join(stages_applied)}"
         
-        return result, similarity, status
+        return result, similarity, status,ai_content_label, ai_content_score
     
     except Exception as e:
         logger.error(f"Error in humanization: {e}")
@@ -455,6 +471,8 @@ def create_gradio_interface():
                 with gr.Row():
                     similarity_output = gr.Number(label="Similarity Score", precision=4)
                     status_output = gr.Textbox(label="Status",interactive=False,lines=2, max_lines=10)
+                    ai_content_label = gr.Textbox(label="AI Content Detection",interactive=False)
+                    ai_content_score = gr.Number(label="AI Content Score", precision=4,interactive=False)
             
             with gr.Column(scale=1):
                 gr.Markdown("## 🎛️ Pipeline Configuration")
@@ -502,7 +520,7 @@ def create_gradio_interface():
                 hedge_prob, booster_prob, connector_prob, starter_prob,
                 split_prob, merge_prob, min_split_length, max_merge_length
             ],
-            outputs=[output_text, similarity_output, status_output]
+            outputs=[output_text, similarity_output, status_output,ai_content_label, ai_content_score]
         )
         
         clear_btn.click(