Create app.py

app.py

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+import gradio as gr
+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+from safetensors.torch import load_model
+from huggingface_hub import hf_hub_download
+from timm import list_models, create_model
+import os
+import numpy as np
+
+# Intialize the model
+model_name='swin_s3_base_224'
+model = create_model(
+    model_name,
+    num_classes=36
+)
+load_model(model,f'./{model_name}/model.safetensors')
+
+# Define class names
+class_names = ["3/4 Sleeve", "Accessory", "Babydoll", "Closed Back", "Corset", "Crochet", "Cutouts", "Draped", "Floral", "Gloves", "Halter", "Lace", "Long", "Long Sleeve", "Midi", "No Slit", "Off The Shoulder", "One Shoulder", "Open Back", "Pockets", "Print", "Puff Sleeve", "Ruched", "Satin", "Sequins", "Shimmer", "Short", "Short Sleeve", "Side Slit", "Square Neck", "Strapless", "Sweetheart Neck", "Tight", "V-Neck", "Velvet", "Wrap"]
+label2id = {c:idx for idx,c in enumerate(class_names)}
+id2label = {idx:c for idx,c in enumerate(class_names)}
+
+def predict_features(image_path):
+    # Load PIL image
+    pil_image = Image.open(image_path).convert('RGB')
+
+    # Define transformations to resize and convert image to tensor
+    transform = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor()
+    ])
+    tensor_image = transform(pil_image)
+
+    inputs = tensor_image.unsqueeze(0)
+
+    with torch.no_grad():
+        logits = model(inputs)
+
+    # apply sigmoid activation to convert logits to probabilities
+    # getting labels with confidence threshold of 0.5
+    predictions = logits.sigmoid() > 0.5
+
+    # converting one-hot encoded predictions back to list of labels
+    predictions = predictions.float().numpy().flatten() # convert boolean predictions to float
+    pred_labels = np.where(predictions==1)[0] # find indices where prediction is 1
+    pred_labels = ([id2label[label] for label in pred_labels]) # converting integer labels to string
+    print(pred_labels)
+    return pred_labels
+
+
+
+def greet(image):
+    return str(predict_features(image))
+
+demo = gr.Interface(fn=greet, inputs=gr.Image(), outputs="text")
+demo.launch()