init

.gitignore

@@ -0,0 +1,65 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyTorch
+*.pth
+*.pt
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# Environment variables
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# macOS
+.DS_Store
+
+# Windows
+Thumbs.db
+ehthumbs.db
+Desktop.ini
+
+# Model cache (optional - comment out if you want to cache models)
+# .cache/
+# models/
+
+# Gradio temporary files
+gradio_cached_examples/
+flagged/ 

README.md

@@ -1,12 +1,60 @@
 ---
-title: Ai Image Detector
-emoji: 🌖
+title: AI Image Detector
+emoji: 🤖
 colorFrom: purple
 colorTo: indigo
 sdk: gradio
 sdk_version: 5.34.2
 app_file: app.py
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🤖 AI Image Detector
+
+Detect whether an image is AI-generated or real using state-of-the-art machine learning models.
+
+## Overview
+
+This Gradio app uses the [Organika/sdxl-detector](https://huggingface.co/Organika/sdxl-detector) model to classify images as either AI-generated or real. The model has been specifically trained to detect images generated by various AI systems including:
+
+- DALL-E
+- Midjourney  
+- Stable Diffusion (SDXL)
+- And other diffusion models
+
+## How to Use
+
+1. **Upload an Image**: Click on the upload area or drag and drop an image file (JPG, PNG, etc.)
+2. **Get Results**: The model will analyze your image and return probability scores
+3. **Interpret Results**: Higher probability for "AI-generated" suggests the image was created by AI
+
+## Model Information
+
+- **Model**: [Organika/sdxl-detector](https://huggingface.co/Organika/sdxl-detector)
+- **Task**: Image Classification (Binary: AI-generated vs Real)
+- **Framework**: Transformers + PyTorch
+- **Interface**: Gradio
+
+## Limitations
+
+⚠️ **Important Notes:**
+- The model may not be 100% accurate on all images
+- Performance may vary depending on the specific AI model used to generate the image
+- Very high-quality AI images or heavily post-processed real images might be misclassified
+- The model is primarily trained on SDXL-style generated images
+
+## Technical Details
+
+The app uses both the Transformers pipeline and direct model inference to provide robust classification results. The model outputs probabilities for each class, giving you confidence scores for the prediction.
+
+## Development
+
+This space is built with:
+- **Gradio**: For the web interface
+- **Transformers**: For model loading and inference
+- **PyTorch**: As the backend framework
+
+---
+
+*This is an educational tool for demonstrating AI image detection capabilities. Always use critical thinking when evaluating image authenticity.*

app.py

@@ -0,0 +1,81 @@
+import gradio as gr
+from transformers import pipeline, AutoImageProcessor, AutoModelForImageClassification
+
+# Load the model and processor
+print("Loading model...")
+processor = AutoImageProcessor.from_pretrained("Organika/sdxl-detector")
+model = AutoModelForImageClassification.from_pretrained("Organika/sdxl-detector")
+pipe = pipeline("image-classification", model="Organika/sdxl-detector")
+print("Model loaded successfully!")
+
+def detect_ai(image):
+    """
+    Detect if an image is AI-generated or real.
+    
+    Args:
+        image: PIL Image object
+        
+    Returns:
+        dict: Probabilities for each class (AI-generated vs Real)
+    """
+    if image is None:
+        return {}
+    
+    try:
+        # Pipeline result
+        pipe_out = pipe(image)
+        
+        # Direct model inference for more detailed results
+        inputs = processor(images=image, return_tensors="pt")
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probs = logits.softmax(dim=-1)[0].tolist()
+        id2label = model.config.id2label
+        
+        # Create result dictionary
+        result = {
+            id2label[0]: probs[0],
+            id2label[1]: probs[1],
+        }
+        
+        return result
+    
+    except Exception as e:
+        print(f"Error processing image: {e}")
+        return {"Error": "Failed to process image"}
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=detect_ai,
+    inputs=gr.Image(type="pil", label="Upload an Image"),
+    outputs=gr.Label(num_top_classes=2, label="AI vs Real Probability"),
+    title="🤖 AI‑Generated Image Detector",
+    description="""
+    Upload an image to detect whether it's AI-generated or real using the Organika/sdxl-detector model.
+    
+    This model can help identify images generated by AI systems like DALL-E, Midjourney, Stable Diffusion, and others.
+    
+    **How to use:**
+    1. Upload an image (JPG, PNG, etc.)
+    2. The model will analyze it and return probabilities
+    3. Higher probability for "AI-generated" suggests the image was created by AI
+    """,
+    article="""
+    ### About the Model
+    This detector uses the [Organika/sdxl-detector](https://huggingface.co/Organika/sdxl-detector) model 
+    to classify images as either AI-generated or real. The model has been trained to detect various AI-generated images 
+    with a focus on SDXL and similar diffusion models.
+    
+    ### Limitations
+    - The model may not be 100% accurate on all images
+    - Performance may vary depending on the AI model used to generate the image
+    - Very high-quality AI images or heavily post-processed real images might be misclassified
+    """,
+    examples=[
+        # You can add example images here if you have them
+    ],
+    cache_examples=False,
+)
+
+if __name__ == "__main__":
+    demo.launch() 

requirements.txt

@@ -0,0 +1,6 @@
+transformers
+torch
+torchvision
+gradio
+Pillow
+numpy