Create app.py

app.py

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+from PIL import Image
+import gradio as gr
+from transformers import pipeline
+
+# Load the image classification pipeline
+classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection")
+
+def classify_image(image):
+    """
+    Classifies the input image using the NSFW image detection pipeline.
+
+    Args:
+        image: A PIL Image object or a NumPy array.
+
+    Returns:
+        A dictionary of labels and scores, or a list of dictionaries if a batch of images is provided.
+    """
+    predictions = classifier(image)
+    # Format the output for Gradio
+    return {prediction['label']: prediction['score'] for prediction in predictions}
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil", label="Upload Image"),  # Use gr.Image for image input
+    outputs=gr.Label(num_top_classes=5, label = "Predictions"),  # Use gr.Label to display the results
+    title="NSFW Image Classifier",
+    description="Upload an image to classify it as NSFW (Not Safe For Work) or SFW (Safe For Work). This model uses the Falconsai/nsfw_image_detection model from Hugging Face.",
+    examples=[
+      ["cat.jpeg"],
+      ["dog.png"]
+    ],
+)
+
+iface.launch()