Upload 8 files

.gitattributes

@@ -33,3 +33,10 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+animal_images/animal_images/cat.png filter=lfs diff=lfs merge=lfs -text
+animal_images/animal_images/frog.png filter=lfs diff=lfs merge=lfs -text
+animal_images/animal_images/hippo.png filter=lfs diff=lfs merge=lfs -text
+animal_images/animal_images/jaguar.png filter=lfs diff=lfs merge=lfs -text
+animal_images/animal_images/sloth.png filter=lfs diff=lfs merge=lfs -text
+animal_images/animal_images/toucan.png filter=lfs diff=lfs merge=lfs -text
+animal_images/animal_images/turtle.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,43 @@
+import gradio as gr
+from transformers import pipeline
+
+# Initialize the image classification pipeline with the Google ViT model
+# This model is trained on ImageNet-1k and works well for general animal classification
+classifier = pipeline("image-classification", model="google/vit-base-patch16-224")
+
+def classify_image(image):
+    """
+    Takes an image, runs it through the classifier, and returns the top predictions.
+    """
+    if image is None:
+        return None
+        
+    predictions = classifier(image)
+    # Gradio's Label component expects a dictionary of {label: confidence}
+    return {p["label"]: p["score"] for p in predictions}
+
+# List of example images located in the nested animal_images folder
+# Note: Based on your folder structure, the images are in 'animal_images/animal_images/'
+examples = [
+    ["animal_images/animal_images/cat.png"],
+    ["animal_images/animal_images/frog.png"],
+    ["animal_images/animal_images/hippo.png"],
+    ["animal_images/animal_images/jaguar.png"],
+    ["animal_images/animal_images/sloth.png"],
+    ["animal_images/animal_images/toucan.png"],
+    ["animal_images/animal_images/turtle.png"]
+]
+
+# Create the Gradio Interface
+# We map the inputs and outputs to match the labels seen in your screenshot ("inp", "output")
+demo = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil", label="inp"),
+    outputs=gr.Label(num_top_classes=3, label="output"),
+    examples=examples,
+    title="Animal Classifier",
+    description="Upload an image of an animal to classify it using the Google ViT model."
+)
+
+if __name__ == "__main__":
+    demo.launch()