Update app.py

app.py

@@ -1,94 +1,94 @@
-import gradio as gr
-from divscore import DivScore
-import torch
-import os
-
-# Set environment variables for Hugging Face
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-# Initialize the DivScore detector with loading state
-def load_model():
-    try:
-        detector = DivScore(
-            generalLM_name_or_path="mistralai/Mistral-7B-v0.2",
-            enhancedLM_name_or_path="RichardChenZH/DivScore_combined",
-            device="cuda:0" if torch.cuda.is_available() else "cpu",
-            use_bfloat16=True  # Use bfloat16 for better memory efficiency
-        )
-        return detector
-    except Exception as e:
-        print(f"Error loading model: {str(e)}")
-        return None
-
-# Global variable for the detector
-detector = None
-
-def detect_ai_text(text):
-    """
-    Detect if the input text is AI-generated using DivScore.
-    Returns a tuple of (score, is_ai_generated, confidence)
-    """
-    global detector
-    
-    # Initialize detector if not already done
-    if detector is None:
-        detector = load_model()
-        if detector is None:
-            return "Error: Failed to load the model. Please try again later.", False, 0.0
-    
-    if not text.strip():
-        return "Please enter some text to analyze.", False, 0.0
-    
-    try:
-        score, entropy_score, ce_score = detector.compute_score(text)
-        
-        # Based on the paper's findings, we use 0.15 as the threshold
-        is_ai_generated = score < 0.15
-
-        result = f"DivScore: {score:.4f}\nEntropy Score: {entropy_score:.4f}\nCE Score: {ce_score:.4f}"
-        return result, is_ai_generated
-        
-    except Exception as e:
-        return f"Error occurred: {str(e)}", False, 0.0
-
-# Create the Gradio interface with loading state
-with gr.Blocks(title="DivScore AI Text Detector") as demo:
-    gr.Markdown("""
-    # DivScore AI Text Detector
-    
-    This demo uses the DivScore model to detect if text was generated by an AI model.
-    Enter your text below to analyze it.
-    
-    **Note:** The model may take a few moments to load on first use.
-    """)
-    
-    with gr.Row():
-        with gr.Column():
-            text_input = gr.Textbox(
-                label="Input Text",
-                placeholder="Enter text to analyze...",
-                lines=5
-            )
-            submit_btn = gr.Button("Analyze Text")
-        
-        with gr.Column():
-            result_output = gr.Textbox(label="Analysis Results")
-            ai_generated = gr.Checkbox(label="AI Generated", interactive=False)
-    
-    gr.Examples(
-        examples=[
-            ["The quick brown fox jumps over the lazy dog."],
-            ["Based on the analysis of the data, we can conclude that the implementation of the new protocol has resulted in a statistically significant improvement in patient outcomes."]
-        ],
-        inputs=text_input
-    )
-    
-    submit_btn.click(
-        fn=detect_ai_text,
-        inputs=text_input,
-        outputs=[result_output, ai_generated]
-    )
-
-if __name__ == "__main__":
-    demo.queue()  # Enable queuing for better handling of multiple requests
+import gradio as gr
+from divscore import DivScore
+import torch
+import os
+
+# Set environment variables for Hugging Face
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+# Initialize the DivScore detector with loading state
+def load_model():
+    try:
+        detector = DivScore(
+            generalLM_name_or_path="mistral-community/Mistral-7B-v0.2",
+            enhancedLM_name_or_path="RichardChenZH/DivScore_combined",
+            device="cuda:0" if torch.cuda.is_available() else "cpu",
+            use_bfloat16=True  # Use bfloat16 for better memory efficiency
+        )
+        return detector
+    except Exception as e:
+        print(f"Error loading model: {str(e)}")
+        return None
+
+# Global variable for the detector
+detector = None
+
+def detect_ai_text(text):
+    """
+    Detect if the input text is AI-generated using DivScore.
+    Returns a tuple of (score, is_ai_generated, confidence)
+    """
+    global detector
+    
+    # Initialize detector if not already done
+    if detector is None:
+        detector = load_model()
+        if detector is None:
+            return "Error: Failed to load the model. Please try again later.", False, 0.0
+    
+    if not text.strip():
+        return "Please enter some text to analyze.", False, 0.0
+    
+    try:
+        score, entropy_score, ce_score = detector.compute_score(text)
+        
+        # Based on the paper's findings, we use 0.15 as the threshold
+        is_ai_generated = score < 0.15
+
+        result = f"DivScore: {score:.4f}\nEntropy Score: {entropy_score:.4f}\nCE Score: {ce_score:.4f}"
+        return result, is_ai_generated
+        
+    except Exception as e:
+        return f"Error occurred: {str(e)}", False, 0.0
+
+# Create the Gradio interface with loading state
+with gr.Blocks(title="DivScore AI Text Detector") as demo:
+    gr.Markdown("""
+    # DivScore AI Text Detector
+    
+    This demo uses the DivScore model to detect if text was generated by an AI model.
+    Enter your text below to analyze it.
+    
+    **Note:** The model may take a few moments to load on first use.
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            text_input = gr.Textbox(
+                label="Input Text",
+                placeholder="Enter text to analyze...",
+                lines=5
+            )
+            submit_btn = gr.Button("Analyze Text")
+        
+        with gr.Column():
+            result_output = gr.Textbox(label="Analysis Results")
+            ai_generated = gr.Checkbox(label="AI Generated", interactive=False)
+    
+    gr.Examples(
+        examples=[
+            ["The quick brown fox jumps over the lazy dog."],
+            ["Based on the analysis of the data, we can conclude that the implementation of the new protocol has resulted in a statistically significant improvement in patient outcomes."]
+        ],
+        inputs=text_input
+    )
+    
+    submit_btn.click(
+        fn=detect_ai_text,
+        inputs=text_input,
+        outputs=[result_output, ai_generated]
+    )
+
+if __name__ == "__main__":
+    demo.queue()  # Enable queuing for better handling of multiple requests
     demo.launch()