Update app.py

app.py

@@ -4,7 +4,6 @@ from transformers import (
     AutoModelForCausalLM,
     AutoTokenizer,
     SynthIDTextWatermarkingConfig,
-    SynthIDTextBayesianDetector
 )
 
 # Initialize model and tokenizer
@@ -16,12 +15,10 @@ model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
 WATERMARK_KEYS = [654, 400, 836, 123, 340, 443, 597, 160, 57, 789]  # Example keys
 watermarking_config = SynthIDTextWatermarkingConfig(
     keys=WATERMARK_KEYS,
-    ngram_len=5
+    ngram_len=5,
+    gamma=0.5,  # Additional parameter to control watermark strength
 )
 
-# Initialize detector
-detector = SynthIDTextBayesianDetector(watermarking_config)
-
 def apply_watermark(text):
     """Apply SynthID watermark to input text."""
     try:
@@ -35,7 +32,9 @@ def apply_watermark(text):
                 watermarking_config=watermarking_config,
                 do_sample=True,
                 max_length=len(inputs["input_ids"][0]) + 100,  # Add some extra tokens
-                pad_token_id=tokenizer.eos_token_id
+                pad_token_id=tokenizer.eos_token_id,
+                temperature=0.7,  # Add some randomness to generation
+                top_p=0.9
             )
         
         # Decode output
@@ -44,27 +43,30 @@ def apply_watermark(text):
     except Exception as e:
         return text, f"Error applying watermark: {str(e)}"
 
-def detect_watermark(text):
-    """Detect if text contains SynthID watermark."""
+def analyze_text(text):
+    """Analyze text characteristics that might indicate watermarking."""
     try:
-        # Get detection score
-        score = detector.detect(text)
-        
-        # Interpret results
-        threshold = 0.5  # You can adjust this threshold
-        is_watermarked = score > threshold
+        # Basic text analysis (since we don't have access to the detector yet)
+        total_words = len(text.split())
+        avg_word_length = sum(len(word) for word in text.split()) / total_words if total_words > 0 else 0
         
-        result = f"Watermark Detection Score: {score:.3f}\n"
-        result += f"Verdict: {'WATERMARK DETECTED' if is_watermarked else 'NO WATERMARK DETECTED'}"
+        # Create analysis report
+        analysis = f"""Text Analysis:
+- Total words: {total_words}
+- Average word length: {avg_word_length:.2f}
+
+Note: This is a basic analysis. The official SynthID detector is not yet available in the public transformers package.
+For proper watermark detection, please refer to the official Google DeepMind implementation when it becomes available."""
         
-        return result
+        return analysis
     except Exception as e:
-        return f"Error detecting watermark: {str(e)}"
+        return f"Error analyzing text: {str(e)}"
 
 # Create Gradio interface
 with gr.Blocks(title="SynthID Text Watermarking Tool") as app:
     gr.Markdown("# SynthID Text Watermarking Tool")
-    gr.Markdown("Apply and detect SynthID watermarks in text")
+    gr.Markdown("""This demo shows how to apply SynthID watermarks to text. 
+                Note: The official detector is not yet publicly available.""")
     
     with gr.Tab("Apply Watermark"):
         with gr.Row():
@@ -74,18 +76,19 @@ with gr.Blocks(title="SynthID Text Watermarking Tool") as app:
         apply_btn = gr.Button("Apply Watermark")
         apply_btn.click(apply_watermark, inputs=[input_text], outputs=[output_text, status])
     
-    with gr.Tab("Detect Watermark"):
+    with gr.Tab("Analyze Text"):
         with gr.Row():
-            detect_input = gr.Textbox(label="Text to Check", lines=5)
-            detect_result = gr.Textbox(label="Detection Result", lines=3)
-        detect_btn = gr.Button("Detect Watermark")
-        detect_btn.click(detect_watermark, inputs=[detect_input], outputs=[detect_result])
+            analyze_input = gr.Textbox(label="Text to Analyze", lines=5)
+            analyze_result = gr.Textbox(label="Analysis Result", lines=5)
+        analyze_btn = gr.Button("Analyze Text")
+        analyze_btn.click(analyze_text, inputs=[analyze_input], outputs=[analyze_result])
     
     gr.Markdown("""
     ### Notes:
-    - The watermark is designed to be imperceptible to humans but detectable by the classifier
-    - Detection scores above 0.5 indicate likely presence of a watermark
-    - The watermark is somewhat robust to minor text modifications but may not survive major changes
+    - The watermark is designed to be imperceptible to humans
+    - This demo only implements watermark application
+    - The official detector will be available in future releases
+    - For production use, use your own secure watermark keys
     """)
 
 # Launch the app