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import gradio as gr
import joblib
import numpy as np
from PIL import Image
# Load your model
with open("model.pkl", "rb") as f:
model = joblib.load(f)
# Preprocessing function
def preprocess_image(img: Image.Image):
# Resize and convert to a flat array (adjust according to your model's needs)
img = img.resize((64, 64)) # example size
img_array = np.array(img)
if img_array.ndim == 3 and img_array.shape[2] == 3:
img_array = img_array.mean(axis=2) # convert to grayscale if needed
img_flat = img_array.flatten()
return img_flat
# Prediction function
def predict(image):
try:
img_flat = preprocess_image(image)
prediction = model.predict([img_flat])[0]
return prediction
except Exception as e:
return f"Error: {str(e)}"
# Gradio interface
iface = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs="text",
title="Cat vs Dog Classifier",
description="Upload an image and the model will predict: cat, dog, or idk.",
)
if __name__ == "__main__":
iface.launch()
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