Upload 2 files

app.py

@@ -0,0 +1,61 @@
+import gradio as gr
+import torch
+from PIL import Image as PILImage
+from transformers import AutoImageProcessor, SiglipForImageClassification
+
+# --- Model Loading ---
+MODEL_IDENTIFIER = r"Ateeqq/ai-vs-human-image-detector"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+print(f"Loading model: {MODEL_IDENTIFIER} on device: {device}")
+processor = AutoImageProcessor.from_pretrained(MODEL_IDENTIFIER)
+model = SiglipForImageClassification.from_pretrained(MODEL_IDENTIFIER)
+model.to(device)
+model.eval()
+print("Model loaded successfully.")
+
+# --- Prediction Function ---
+def predict(image):
+    """
+    Takes a PIL image, preprocesses it, and returns the prediction probabilities.
+    """
+    if image is None:
+        return None
+        
+    # Preprocess the image
+    inputs = processor(images=image, return_tensors="pt").to(device)
+
+    # Perform inference
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        
+    # Get probabilities
+    probabilities = torch.softmax(logits, dim=-1)[0]
+    
+    # Create a dictionary of labels and their scores
+    confidences = {model.config.id2label[i]: score.item() for i, score in enumerate(probabilities)}
+    return confidences
+
+# --- Gradio Interface ---
+# Define the Gradio interface components
+image_input = gr.Image(type="pil", label="Upload an Image")
+label_output = gr.Label(num_top_classes=2, label="Prediction")
+
+# The title and description for the app
+title = "AI vs Human Image Detector"
+description = """
+This Space uses the `Ateeqq/ai-vs-human-image-detector` model to classify an image as either AI-generated or Human-made. 
+Upload an image to see the prediction.
+"""
+article = "Model by [Ateeqq](https://huggingface.co/Ateeqq) | Gradio app created with AI"
+
+# Launch the Gradio app
+gr.Interface(
+    fn=predict,
+    inputs=image_input,
+    outputs=label_output,
+    title=title,
+    description=description,
+    article=article
+).launch(share=True, server_name="0.0.0.0") 

requirements.txt

@@ -0,0 +1,5 @@
+transformers
+torch
+Pillow
+accelerate
+gradio