Update app.py

app.py

@@ -9,11 +9,11 @@ logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 # Load the model and processor from Hugging Face
-model = ViTForImageClassification.from_pretrained("dima806/deepfake_vs_real_image_detection")
-processor = ViTImageProcessor.from_pretrained("dima806/deepfake_vs_real_image_detection")
+model = ViTForImageClassification.from_pretrained("prithivMLmods/Deep-Fake-Detector-Model")
+processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deep-Fake-Detector-Model")
 
-def detect(image, confidence_threshold=0.7):
-    """Detect deepfake content in an image using dima806/deepfake_vs_real_image_detection"""
+def detect(image, confidence_threshold=0.5):
+    """Detect deepfake content using prithivMLmods/Deep-Fake-Detector-Model"""
     if image is None:
         raise gr.Error("Please upload an image to analyze")
     
@@ -21,7 +21,7 @@ def detect(image, confidence_threshold=0.7):
         # Convert Gradio image (filepath) to PIL Image
         pil_image = Image.open(image).convert("RGB")
         
-        # Ensure image meets ViT's expected size (224x224)
+        # Resize to match ViT input requirements (224x224)
         pil_image = pil_image.resize((224, 224), Image.Resampling.LANCZOS)
         
         # Preprocess the image
@@ -97,17 +97,18 @@ MARKDOWN0 = """
 <div class="header">
     <h1>DeepFake Detection System</h1>
     <p>Advanced AI-powered analysis for identifying manipulated media<br>
-    Powered by dima806/deepfake_vs_real_image_detection model<br>
-    Note: Accuracy may vary with recent deepfakes due to training data age</p>
+    Powered by prithivMLmods/Deep-Fake-Detector-Model (Updated Jan 2025)<br>
+    Adjustable threshold for tuning detection sensitivity</p>
 </div>
 """
 
-# Create Gradio interface
+# Create Gradio interface with threshold slider
 with gr.Blocks(css=custom_css, theme=gr.themes.Default()) as demo:
     gr.Markdown(MARKDOWN0)
     with gr.Row(elem_classes="container"):
         with gr.Column(scale=1):
             image = gr.Image(type='filepath', height=400, label="Upload Image")
+            threshold = gr.Slider(0, 1, value=0.5, step=0.01, label="Confidence Threshold (Fake)")
             detect_button = gr.Button("Analyze Image", elem_classes="button-gradient")
         with gr.Column(scale=2):
             overall = gr.Label(label="Confidence Score")
@@ -116,11 +117,11 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Default()) as demo:
     
     detect_button.click(
         fn=detect,
-        inputs=[image],
+        inputs=[image, threshold],
         outputs=[overall, aigen, deepfake]
     )
 
 # Launch the application
 demo.launch(
     debug=True
-)
+)