Delete gradio_app

gradio_app/inference.py

@@ -1,57 +0,0 @@
-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)