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accessories.py

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+def recommend_accessories(style_label):
+    style_label = style_label.lower()
+
+    # Define style groups and their recommendations
+    style_groups = {
+        "suit": [
+            "tie", "cufflinks", "polished dress shoes", "pocket square", "leather belt", "stylish watch"
+        ],
+        "casual": [
+            "trendy sneakers", "denim jackets", "baseball cap"
+        ],
+        "formal": [
+            "elegant watches", "leather belts", "cufflinks"
+        ],
+        "bohemian": [
+            "layered necklaces", "floppy hats", "fringe bags"
+        ],
+        "sporty": [
+            "wristbands", "running shoes", "sweat-wicking fabrics"
+        ],
+        "vintage": [
+            "pearl earrings", "brooches", "classic handbags"
+        ]
+    }
+
+    # Try to find the best matching group based on keywords in the style_label
+    for group, accessories in style_groups.items():
+        if group in style_label:
+            rec = ", ".join(accessories)
+            return f"Try these accessories to complement your {group} style: {rec}."
+
+    # Default recommendation if no group matched
+    return "Try some trendy accessories like sunglasses and a stylish belt."

app.py

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+import gradio as gr
+from PIL import Image
+import torch
+from transformers import ViTFeatureExtractor, ViTForImageClassification
+from accessories import recommend_accessories
+
+# Load ViT Model for style classification
+def load_model():
+    feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224")
+    model = ViTForImageClassification.from_pretrained("google/vit-base-patch16-224")
+    return feature_extractor, model
+
+extractor, model = load_model()
+
+def analyze_style(image):
+    if image is None:
+        return "Please upload an image.", None, None
+
+    inputs = extractor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    predicted_class = outputs.logits.argmax(-1).item()
+    style_name = model.config.id2label[predicted_class]
+
+    style_label = style_name.lower()
+    rec = recommend_accessories(style_label)
+
+    return f"**Predicted Style Class:** {style_name}", rec, image
+
+title = "StyleCraft: AI-Enhanced Fashion Designer"
+
+description = """
+**StyleCraft** helps fashion enthusiasts and designers analyze garment styles and get accessory & fabric recommendations. Upload a photo or sketch, and let AI do the magic!
+
+**How to use:**
+1. Upload a clear image or sketch of a garment.
+2. View the predicted style.
+3. See recommended accessories and fabrics to enhance your design.
+"""
+
+with gr.Blocks() as demo:
+    gr.Markdown(f"# {title}")
+    gr.Markdown(description)
+
+    with gr.Row():
+        # Remove paste and webcam by using image upload only:
+        image_input = gr.Image(label="Upload a garment image or sketch", type="pil", 
+                               interactive=True, source="upload")
+        
+        with gr.Column():
+            style_output = gr.Markdown(label="Style Analysis")
+            rec_output = gr.Markdown(label="💍 Accessory & Fabric Recommendation")
+            clear_btn = gr.Button("Clear")
+
+    analyze_button = gr.Button("Analyze Style")
+    
+    analyze_button.click(
+        fn=analyze_style,
+        inputs=image_input,
+        outputs=[style_output, rec_output, image_input],
+    )
+
+    clear_btn.click(
+        fn=lambda: ("", "", None),
+        inputs=None,
+        outputs=[style_output, rec_output, image_input]
+    )
+
+demo.launch()

requirements.txt

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+gradio
+transformers
+torch
+Pillow
+opencv-python
+scikit-learn
+matplotlib
+numpy
+requests