..

app.py

@@ -12,16 +12,8 @@ import requests
 from io import BytesIO
 
 # Paths and model setup
-image_folder = "path_to_your_image_folder"  # Specify the path to your image folder
 model_path = "MichalMlodawski/nsfw-image-detection-large"
 
-# List of jpg files in the folder
-jpg_files = [file for file in os.listdir(image_folder) if file.lower().endswith(".jpg")]
-
-if not jpg_files:
-    print("🚫 No jpg files found in folder:", image_folder)
-    exit()
-
 # Load the model and feature extractor
 feature_extractor = AutoProcessor.from_pretrained(model_path)
 model = FocalNetForImageClassification.from_pretrained(model_path)
@@ -143,6 +135,18 @@ def moderate_image(image):
     else:
         return "Image does not adhere to community guidelines."
 
+# Function to classify NSFW images
+def classify_nsfw(image):
+    image_tensor = transform(image).unsqueeze(0)
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        confidence, predicted = torch.max(probabilities, 1)
+    label = model.config.id2label[predicted.item()]
+    category = label_to_category.get(label, "Unknown")
+    return f"Label: {label}, Category: {category}, Confidence: {confidence.item() * 100:.2f}%"
+
 # Create the Gradio interface
 css = """
 #col-container {
@@ -216,18 +220,6 @@ with gr.Blocks(css=css) as demo:
             selected_image = gr.Image(type="pil", label="Upload Image for NSFW Classification")
             classify_button = gr.Button("Classify Image")
             classification_result = gr.Textbox(label="Classification Result")
-
-            def classify_nsfw(image):
-                image_tensor = transform(image).unsqueeze(0)
-                inputs = feature_extractor(images=image, return_tensors="pt")
-                with torch.no_grad():
-                    outputs = model(**inputs)
-                    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
-                    confidence, predicted = torch.max(probabilities, 1)
-                label = model.config.id2label[predicted.item()]
-                category = label_to_category.get(label, "Unknown")
-                return f"Label: {label}, Category: {category}, Confidence: {confidence.item() * 100:.2f}%"
-
             classify_button.click(classify_nsfw, selected_image, classification_result)
 
 demo.launch()