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