Create app.py

app.py

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+import gradio as gr
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from PIL import Image
+import json
+
+# --------------------------
+# Load the model
+# --------------------------
+model_path = "densenet121-a639ec97.pth"  # put this file in the Space
+
+state_dict = torch.load(model_path, map_location="cpu")
+
+model = models.densenet121(weights=None)
+model.load_state_dict(state_dict, strict=True)
+model.eval()
+
+# --------------------------
+# Load ImageNet class labels
+# --------------------------
+# Downloaded from: https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt
+with open("imagenet_classes.json", "r") as f:
+    idx_to_class = json.load(f)
+
+# --------------------------
+# Preprocessing function
+# --------------------------
+preprocess = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]
+    )
+])
+
+# --------------------------
+# Prediction function
+# --------------------------
+def predict(image):
+    image = Image.fromarray(image).convert("RGB")
+    img_tensor = preprocess(image).unsqueeze(0)
+
+    with torch.no_grad():
+        outputs = model(img_tensor)
+        probs = torch.nn.functional.softmax(outputs, dim=1)
+        top5_prob, top5_idx = torch.topk(probs, 5)
+
+    results = []
+    for p, idx in zip(top5_prob[0], top5_idx[0]):
+        results.append({
+            "class": idx_to_class[str(idx.item())],
+            "confidence": float(p.item())
+        })
+
+    return results
+
+# --------------------------
+# Gradio UI
+# --------------------------
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="numpy", label="Upload Image"),
+    outputs=gr.JSON(label="Top-5 Predictions"),
+    title="DenseNet-121 ImageNet Classifier",
+    description="Upload an image and get top-5 ImageNet predictions.",
+)
+
+demo.launch()