Upload 4 files

gradio_app.py

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+import gradio as gr
+from gradio_app.inference import run_inference
+
+def create_gradio_interface():
+    return gr.Interface(
+        fn=run_inference,
+        inputs=[
+            gr.Image(type="pil", label="Upload Image"),
+            gr.File(label="Reference Dict JSON File"),
+            gr.File(label="Index to Class Mapping JSON File"),
+            gr.File(label="Classifier Model (.pth) File"),
+            gr.Textbox(label="EdgeFace Model Name", value="edgeface_base"),
+            gr.Textbox(label="EdgeFace Model Directory", value="ckpts/idiap"),
+            gr.Dropdown(choices=["yolo", "mtcnn"], label="Face Detection Algorithm", value="yolo"),
+            gr.Dropdown(choices=["auto", "cpu", "gpu"], label="Accelerator", value="auto"),
+            gr.Slider(minimum=112, maximum=448, step=1, value=224, label="Resolution"),
+            gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.6, label="Similarity Threshold")
+        ],
+        outputs="text",
+        title="Face Classification with EdgeFace Validation",
+        description="Upload an image and required files to perform face classification with EdgeFace embedding validation."
+    )
+
+if __name__ == "__main__":
+    iface = create_gradio_interface()
+    iface.launch()

gradio_app/inference.py

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+import os
+import sys
+from PIL import Image
+
+# Append the path to the inference script's directory
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'src', 'slimface', 'inference')))
+from end2end_inference import cinference_and_confirm
+
+def run_inference(image, reference_dict_path, index_to_class_mapping_path, model_path, 
+                 edgeface_model_name="edgeface_base", edgeface_model_dir="ckpts/idiap", 
+                 algorithm="yolo", accelerator="auto", resolution=224, similarity_threshold=0.6):
+    # Save uploaded image temporarily in apps/gradio_app/
+    temp_image_path = os.path.join(os.path.dirname(__file__), "temp_image.jpg")
+    image.save(temp_image_path)
+
+    # Create args object to mimic command-line arguments
+    class Args:
+        def __init__(self):
+            self.unknown_image_path = temp_image_path
+            self.reference_dict_path = reference_dict_path.name if reference_dict_path else None
+            self.index_to_class_mapping_path = index_to_class_mapping_path.name if index_to_class_mapping_path else None
+            self.model_path = model_path.name if model_path else None
+            self.edgeface_model_name = edgeface_model_name
+            self.edgeface_model_dir = edgeface_model_dir
+            self.algorithm = algorithm
+            self.accelerator = accelerator
+            self.resolution = resolution
+            self.similarity_threshold = similarity_threshold
+
+    args = Args()
+
+    # Validate inputs
+    if not all([args.reference_dict_path, args.index_to_class_mapping_path, args.model_path]):
+        return "Error: Please provide all required files (reference dict, index-to-class mapping, and model)."
+
+    try:
+        # Call the inference function from end2end_inference.py
+        results = cinference_and_confirm(args)
+        
+        # Format output
+        output = ""
+        for result in results:
+            output += f"Image: {result['image_path']}\n"
+            output += f"Predicted Class: {result['predicted_class']}\n"
+            output += f"Confidence: {result['confidence']:.4f}\n"
+            output += f"Similarity: {result.get('similarity', 'N/A'):.4f}\n"
+            output += f"Confirmed: {result.get('confirmed', 'N/A')}\n\n"
+        
+        return output
+    
+    except Exception as e:
+        return f"Error: {str(e)}"
+    
+    finally:
+        # Clean up temporary image
+        if os.path.exists(temp_image_path):
+            os.remove(temp_image_path)