Create app.py

app.py

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+import os
+import torch
+from PIL import Image
+from torchvision import transforms
+from transformers import AutoProcessor, FocalNetForImageClassification
+import gradio as gr
+
+# Path to the model
+MODEL_PATH = "MichalMlodawski/nsfw-image-detection-large"
+
+# Load the model and feature extractor
+feature_extractor = AutoProcessor.from_pretrained(MODEL_PATH)
+model = FocalNetForImageClassification.from_pretrained(MODEL_PATH)
+model.eval()
+
+# Image transformations
+transform = transforms.Compose([
+    transforms.Resize((512, 512)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+# Mapping from model labels to NSFW categories
+LABEL_TO_CATEGORY = {
+    "LABEL_0": "Safe",
+    "LABEL_1": "Questionable",
+    "LABEL_2": "Unsafe"
+}
+
+def classify_image(image):
+    if image is None:
+        return "No image uploaded"
+
+    # Convert to RGB (in case of PNG with alpha channel)
+    image = Image.fromarray(image).convert("RGB")
+    
+    # Process image using feature_extractor
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    
+    # Prediction using the model
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        confidence, predicted = torch.max(probabilities, 1)
+    
+    # Get the label from the model's configuration
+    label = model.config.id2label[predicted.item()]
+    category = LABEL_TO_CATEGORY.get(label, "Unknown")
+    confidence_value = confidence.item() * 100
+
+    # Prepare the result string
+    emoji = {"Safe": "✅", "Questionable": "⚠️", "Unsafe": "🔞"}.get(category, "❓")
+    confidence_bar = "🟩" * int(confidence_value // 10) + "⬜" * (10 - int(confidence_value // 10))
+    
+    result = f"{emoji} NSFW Category: {category}\n"
+    result += f"🏷️ Model Label: {label}\n"
+    result += f"🎯 Confidence: {confidence_value:.2f}% {confidence_bar}"
+
+    return result
+
+# Define Gradio interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Textbox(label="Classification Result"),
+    title="🖼️ NSFW Image Classification 🔍",
+    description="Upload an image to classify its safety level!",
+    theme=gr.themes.Soft(primary_hue="purple"),
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    iface.launch()