Update apps/gradio_app/components.py

apps/gradio_app/components.py

@@ -1,96 +1,96 @@
-import os
-
-# File listing functions
-def list_reference_files():
-    ref_dir = "data/reference_data/"
-    try:
-        files = [os.path.join(ref_dir, f) for f in os.listdir(ref_dir) if f.endswith(".json")]
-        return files if files else ["No .json files found in data/reference_data/"]
-    except FileNotFoundError:
-        return ["Directory data/reference_data/ not found"]
-    except Exception as e:
-        return [f"Error listing files: {str(e)}"]
-
-def list_mapping_files():
-    map_dir = "ckpts/"
-    try:
-        files = [os.path.join(map_dir, f) for f in os.listdir(map_dir) if f.endswith(".json")]
-        return files if files else ["No .json files found in ckpts/"]
-    except FileNotFoundError:
-        return ["Directory ckpts/ not found"]
-    except Exception as e:
-        return [f"Error listing files: {str(e)}"]
-
-def list_classifier_files():
-    clf_dir = "ckpts/"
-    try:
-        files = [os.path.join(clf_dir, f) for f in os.listdir(clf_dir) if f.endswith(".pth")]
-        return files if files else ["No .pth files found in ckpts/"]
-    except FileNotFoundError:
-        return ["Directory ckpts/ not found"]
-    except Exception as e:
-        return [f"Error listing files: {str(e)}"]
-
-def list_edgeface_files():
-    ef_dir = "ckpts/idiap/"
-    try:
-        files = [os.path.join(ef_dir, f) for f in os.listdir(ef_dir) if f.endswith(".pt")]
-        return files if files else ["No .pt files found in ckpts/idiap/"]
-    except FileNotFoundError:
-        return ["Directory ckpts/idiap/ not found"]
-    except Exception as e:
-        return [f"Error listing files: {str(e)}"]
-
-CONTENT_DESCRIPTION = """
-**SlimFace: Advanced Face Classification with TorchVision Backbones**
-"""
-
-CONTENT_IN_1 = """
-SlimFace empowers developers to build high-accuracy face classification models using transfer learning, leveraging TorchVision's powerful pre-trained architectures. 🌟 It provides a flexible, efficient, and scalable solution for facial recognition, delivering top-tier performance for custom applications.
-"""
-CONTENT_IN_2 = """
-<p class="source">
-    For more information, you can follow below:<br>
-    Source code: 
-    <a class="badge" href="https://github.com/danhtran2mind/SlimFace">
-        <img src="https://img.shields.io/badge/GitHub-danhtran2mind%2FSlimFace-blue?style=flat" alt="GitHub Repo">
-    </a>,
-    Author: 
-    <a class="badge" href="https://github.com/danhtran2mind">
-        <img src="https://img.shields.io/badge/GitHub-danhtran2mind-blue?style=flat" alt="GitHub Profile">
-    </a>,
-    PyTorch Docs: 
-    <a class="badge" href="https://docs.pytorch.org/vision/main/models.html">
-        <img src="https://img.shields.io/badge/PyTorch-Pretrain%20Model%20Docs-blue?style=flat" alt="PyTorch Docs">
-    </a>
-</p>
-"""
-
-
-CONTENT_OUTTRO = """
-## More Information about SlimFace
-"""
-CONTENT_OUT_1 = """
-<div class="quote-container">
-    <p>
-        This project leverages code from 
-        <a class="badge" href="https://github.com/otroshi/edgeface">
-            <img src="https://img.shields.io/badge/Built%20on-otroshi%2Fedgeface-blue?style=flat&logo=github" alt="Built on edgeface">
-        </a>
-        by 
-        <a class="badge" href="https://github.com/otroshi">
-            <img src="https://img.shields.io/badge/GitHub-Hatef_Otroshi-blue?style=flat&logo=github" alt="Hatef Otroshi">
-        </a>, 
-        with our own bug fixes and enhancements available at 
-        <a class="badge" href="https://github.com/danhtran2mind/edgeface/tree/main/face_alignment">
-            <img src="https://img.shields.io/badge/GitHub-danhtran2mind%2Fedgeface-blue?style=flat&logo=github" alt="Edgeface Enhancements">
-        </a>.
-    </p>
-</div>
-"""
-CONTENT_OUT_2 = """
-**Supported Architectures:**  
-- **EfficientNet**: B0-B7 and V2 (Small, Medium, Large) for balanced performance and efficiency. 📸  
-- **RegNet**: X/Y series (400MF to 128GF) for optimized computation across diverse hardware. 💻  
-- **Vision Transformers (ViT)**: B_16, B_32, H_14, L_16, L_32 for cutting-edge feature extraction. 🚀  
+import os
+
+# File listing functions
+def list_reference_files():
+    ref_dir = "data/reference_data/"
+    try:
+        files = [os.path.join(ref_dir, f) for f in os.listdir(ref_dir) if f.endswith(".json")]
+        return files if files else ["No .json files found in data/reference_data/"]
+    except FileNotFoundError:
+        return ["Directory data/reference_data/ not found"]
+    except Exception as e:
+        return [f"Error listing files: {str(e)}"]
+
+def list_mapping_files():
+    map_dir = "ckpts/"
+    try:
+        files = [os.path.join(map_dir, f) for f in os.listdir(map_dir) if f.endswith(".json")]
+        return files if files else ["No .json files found in ckpts/"]
+    except FileNotFoundError:
+        return ["Directory ckpts/ not found"]
+    except Exception as e:
+        return [f"Error listing files: {str(e)}"]
+
+def list_classifier_files():
+    clf_dir = "ckpts/"
+    try:
+        files = [os.path.join(clf_dir, f) for f in os.listdir(clf_dir) if f.endswith(".pth")]
+        return files if files else ["No .pth files found in ckpts/"]
+    except FileNotFoundError:
+        return ["Directory ckpts/ not found"]
+    except Exception as e:
+        return [f"Error listing files: {str(e)}"]
+
+def list_edgeface_files():
+    ef_dir = "ckpts/idiap/"
+    try:
+        files = [os.path.join(ef_dir, f) for f in os.listdir(ef_dir) if f.endswith(".pt")]
+        return files if files else ["No .pt files found in ckpts/idiap/"]
+    except FileNotFoundError:
+        return ["Directory ckpts/idiap/ not found"]
+    except Exception as e:
+        return [f"Error listing files: {str(e)}"]
+
+CONTENT_DESCRIPTION = """
+**SlimFace: Advanced Face Classification with TorchVision Backbones**
+"""
+
+CONTENT_IN_1 = """
+SlimFace empowers developers to build high-accuracy face classification models by leveraging TorchVision's powerful pre-trained architectures through a transfer learning approach. 🌟 It provides a flexible, efficient, and scalable solution for facial recognition, supporting training on custom datasets and inference to deliver top-tier performance for custom applications.
+"""
+CONTENT_IN_2 = """
+<p class="source">
+    For more information, you can follow below:<br>
+    Source code: 
+    <a class="badge" href="https://github.com/danhtran2mind/SlimFace">
+        <img src="https://img.shields.io/badge/GitHub-danhtran2mind%2FSlimFace-blue?style=flat" alt="GitHub Repo">
+    </a>,
+    Author: 
+    <a class="badge" href="https://github.com/danhtran2mind">
+        <img src="https://img.shields.io/badge/GitHub-danhtran2mind-blue?style=flat" alt="GitHub Profile">
+    </a>,
+    PyTorch Docs: 
+    <a class="badge" href="https://docs.pytorch.org/vision/main/models.html">
+        <img src="https://img.shields.io/badge/PyTorch-Pretrain%20Model%20Docs-blue?style=flat" alt="PyTorch Docs">
+    </a>
+</p>
+"""
+
+
+CONTENT_OUTTRO = """
+## More Information about SlimFace
+"""
+CONTENT_OUT_1 = """
+<div class="quote-container">
+    <p>
+        This project leverages code from 
+        <a class="badge" href="https://github.com/otroshi/edgeface">
+            <img src="https://img.shields.io/badge/Built%20on-otroshi%2Fedgeface-blue?style=flat&logo=github" alt="Built on edgeface">
+        </a>
+        by 
+        <a class="badge" href="https://github.com/otroshi">
+            <img src="https://img.shields.io/badge/GitHub-Hatef_Otroshi-blue?style=flat&logo=github" alt="Hatef Otroshi">
+        </a>, 
+        with our own bug fixes and enhancements available at 
+        <a class="badge" href="https://github.com/danhtran2mind/edgeface/tree/main/face_alignment">
+            <img src="https://img.shields.io/badge/GitHub-danhtran2mind%2Fedgeface-blue?style=flat&logo=github" alt="Edgeface Enhancements">
+        </a>.
+    </p>
+</div>
+"""
+CONTENT_OUT_2 = """
+**Supported Architectures:**  
+- **EfficientNet**: B0-B7 and V2 (Small, Medium, Large) for balanced performance and efficiency. 📸  
+- **RegNet**: X/Y series (400MF to 128GF) for optimized computation across diverse hardware. 💻  
+- **Vision Transformers (ViT)**: B_16, B_32, H_14, L_16, L_32 for cutting-edge feature extraction. 🚀  
 """