initial commit

app.py

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+import gradio as gr
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "facebook/deit-base-distilled-patch16-224"
+extractor = AutoFeatureExtractor.from_pretrained(model_name)
+model = AutoModelForImageClassification.from_pretrained(model_name)
+
+cat_keywords = ["cat", "kitten", "feline", "tabby", "siamese", "persian", "egyptian cat"]
+
+def detect_cat(img):
+    inputs = extractor(images=img, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs).logits
+
+    probs = torch.softmax(outputs, dim=-1)
+    predicted_class = torch.argmax(probs).item()
+    confidence = probs[0][predicted_class].item()
+    label = model.config.id2label[predicted_class].lower()
+
+    if confidence < 0.60:
+        return f"🤔 Not confident it's a cat. ({label}, {confidence:.2f})"
+    elif any(k in label for k in cat_keywords):
+        return f"😺 Yes, it's a cat! ({label}, confidence: {confidence:.2f})"
+    else:
+        return f"🐶 Nope, not a cat. ({label}, confidence: {confidence:.2f})"
+
+gr.Interface(
+    fn=detect_cat,
+    inputs=gr.Image(type="pil", label="Upload your image 🖼️", height=300),
+    outputs=gr.Textbox(label="🐾 Result"),
+    title="😼 Is It a Cat?",
+    description="Upload an image to check if there's a cat in it.",
+    theme="default"
+).launch()ß

requirements.txt

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+gradio
+transformers
+torch
+pillow