Upload app

app.py

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+import os
+import gradio as gr
+import torch
+import torch.nn as nn
+import timm
+import json
+from torchvision import transforms
+from PIL import Image
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+MODEL_PATH = os.path.join(BASE_DIR, "artifacts", "best_model.pt")
+LABELS_PATH = os.path.join(BASE_DIR, "data", "style_labels.json")
+
+class ArtStyleClassifier(nn.Module):
+    def __init__(self, num_classes):
+        super().__init__()
+        self.backbone = timm.create_model("efficientnet_b0", pretrained=False, num_classes=0)
+        num_features = self.backbone.num_features
+        self.classifier = nn.Sequential(
+            nn.Dropout(p=0.3),
+            nn.Linear(num_features, 512),
+            nn.ReLU(),
+            nn.Dropout(p=0.2),
+            nn.Linear(512, num_classes)
+        )
+    def forward(self, x):
+        features = self.backbone(x)
+        return self.classifier(features)
+
+with open(LABELS_PATH, "r") as f:
+    style_to_idx = json.load(f)
+idx_to_style = {int(i): s for s, i in style_to_idx.items()}
+
+model = ArtStyleClassifier(num_classes=len(style_to_idx))
+model.load_state_dict(torch.load(MODEL_PATH, map_location=torch.device("cpu")))
+model.eval()
+
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225]),
+])
+
+def predict(image):
+    if image is None:
+        return "No image provided."
+    pil_image = Image.fromarray(image).convert("RGB")
+    tensor = transform(pil_image).unsqueeze(0)
+    with torch.no_grad():
+        outputs = model(tensor)
+        probs = torch.softmax(outputs, dim=1)[0]
+    top_probs, top_idxs = torch.topk(probs, 3)
+    lines = []
+    medals = ["🥇", "🥈", "🥉"]
+    for i, (p, idx) in enumerate(zip(top_probs, top_idxs)):
+        style = idx_to_style[idx.item()]
+        confidence = float(p.item()) * 100
+        lines.append(f"{medals[i]}  {style}  —  {confidence:.1f}%")
+    return "\n".join(lines)
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(label="Upload your artwork"),
+    outputs=gr.Text(label="Art Style Predictions"),
+    title="Art Style Classifier",
+    description="Upload a photo of your artwork to identify which art style it most resembles.",
+)
+
+demo.launch()