Files Added for combo classifiers

app.py

@@ -0,0 +1,52 @@
+import gradio as gr
+from transformers import pipeline
+
+model_names = [
+    "apple/mobilevit-small",
+    "facebook/deit-base-patch16-224",
+    "facebook/convnext-base-224",
+    "google/vit-base-patch16-224",
+    "google/mobilenet_v2_1.4_224",
+    "microsoft/resnet-50",
+    "microsoft/swin-base-patch4-window7-224",
+    "microsoft/beit-base-patch16-224",
+    "nvidia/mit-b0",
+    "shi-labs/nat-base-in1k-224",
+    "shi-labs/dinat-base-in1k-224",
+]
+
+
+def process(image_file, top_k):
+    labels = []
+    for m in model_names:
+        p = pipeline("image-classification", model=m)
+        pred = p(image_file)
+        labels.append({x["label"]: x["score"] for x in pred[:top_k]})
+    return labels
+
+
+# Inputs
+image = gr.Image(type="filepath", label="Upload an image")
+top_k = gr.Slider(minimum=1, maximum=5, step=1, value=5, label="Top k classes")
+
+# Output
+labels = [gr.Label(label=m) for m in model_names]
+
+description = "This Space lets you quickly compare the most popular image classifiers available on the hub, including the recent NAT and DINAT models. All of them have been fine-tuned on the ImageNet-1k dataset. Anecdotally, the three sample images have been generated with a Stable Diffusion model :)"
+
+iface = gr.Interface(
+    theme="huggingface",
+    description=description,
+    layout="horizontal",
+    fn=process,
+    inputs=[image, top_k],
+    outputs=labels,
+    examples=[
+        ["bike.jpg", 5],
+        ["car.jpg", 5],
+        ["food.jpg", 5],
+    ],
+    allow_flagging="never",
+)
+
+iface.launch()