Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import os
+
+from PIL import Image
+from transformers import pipeline
+
+def predict_image(image):
+    pipe = pipeline("image-classification", model="itsTomLie/genders_microsoft_resnet50")
+
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image.astype('uint8'))
+    elif isinstance(image, str): 
+        image = Image.open(image)
+
+    result = pipe(image)
+
+    label = result[0]['label']
+    confidence = result[0]['score']
+
+    print(f"Prediction: {label}, Confidence: {confidence}")
+
+    return label, confidence
+
+example_images = [
+    os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples"))
+]
+
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="numpy", label="Upload an Image"),
+    outputs=[gr.Textbox(label="Prediction"), gr.Textbox(label="Confidence")],
+    examples=example_images
+)
+
+interface.launch()