Create app.py

app.py

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+import gradio as gr
+import random
+
+# Simulated cat breed classifier function
+def classify_cat(image):
+    # In a real application, you'd use a proper ML model here
+    cat_breeds = ["Persian", "Siamese", "Maine Coon", "British Shorthair", "Russian Blue"]
+    confidence = random.uniform(0.7, 0.99)
+    return {breed: random.uniform(0.1, confidence) for breed in cat_breeds}
+
+# Simulated cat fact generator
+def generate_cat_fact(breed):
+    cat_facts = {
+        "Persian": "Persian cats are known for their long, luxurious fur and sweet personalities.",
+        "Siamese": "Siamese cats are vocal and intelligent, known for their striking blue eyes.",
+        "Maine Coon": "Maine Coons are one of the largest domestic cat breeds and love water!",
+        "British Shorthair": "British Shorthairs are calm and easygoing cats with plush coats.",
+        "Russian Blue": "Russian Blues are gentle and quiet cats with beautiful silver-blue fur."
+    }
+    return cat_facts.get(breed, "Please select a cat breed!")
+
+# Create the Gradio interface
+with gr.Blocks(title="Cat World Demo") as demo:
+    gr.Markdown("# Welcome to Cat World! 🐱")
+    
+    with gr.Tab("Cat Breed Classifier"):
+        with gr.Row():
+            image_input = gr.Image(label="Upload a cat image")
+            output = gr.Label(label="Predicted Breeds")
+        classify_btn = gr.Button("Classify Cat")
+        classify_btn.click(fn=classify_cat, inputs=image_input, outputs=output)
+    
+    with gr.Tab("Cat Facts"):
+        breed_dropdown = gr.Dropdown(
+            choices=["Persian", "Siamese", "Maine Coon", "British Shorthair", "Russian Blue"],
+            label="Select a cat breed"
+        )
+        fact_output = gr.Textbox(label="Cat Fact")
+        fact_btn = gr.Button("Get Cat Fact")
+        fact_btn.click(fn=generate_cat_fact, inputs=breed_dropdown, outputs=fact_output)
+
+# Launch the demo
+if __name__ == "__main__":
+    demo.launch()