app.py

import gradio as gr
import torch
from PIL import Image as PILImage
from transformers import AutoImageProcessor, SiglipForImageClassification
import os
import warnings

# --- Configuration ---
MODEL_IDENTIFIER = r"Ateeqq/ai-vs-human-image-detector"
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# --- Suppress specific warnings ---
# Suppress the specific PIL warning about potential decompression bombs
warnings.filterwarnings("ignore", message="Possibly corrupt EXIF data.")
# Suppress transformers warning about loading weights without specifying revision
warnings.filterwarnings("ignore", message=".*You are using the default legacy behaviour.*")


# --- Load Model and Processor (Load once at startup) ---
print(f"Using device: {DEVICE}")
print(f"Loading processor from: {MODEL_IDENTIFIER}")
try:
    processor = AutoImageProcessor.from_pretrained(MODEL_IDENTIFIER)
    print(f"Loading model from: {MODEL_IDENTIFIER}")
    model = SiglipForImageClassification.from_pretrained(MODEL_IDENTIFIER)
    model.to(DEVICE)
    model.eval()
    print("Model and processor loaded successfully.")
except Exception as e:
    print(f"FATAL: Error loading model or processor: {e}")
    # If the model fails to load, we raise an exception to stop the app
    raise gr.Error(f"Failed to load the model: {e}. Cannot start the application.") from e

# --- Prediction Function ---
def classify_image(image_pil):
    """
    Classifies an image as AI-generated or Human-made.

    Args:
        image_pil (PIL.Image.Image): Input image in PIL format.

    Returns:
        dict: A dictionary mapping class labels ('ai', 'human') to their
              confidence scores. Returns an empty dict if input is None.
    """
    if image_pil is None:
        # Handle case where the user clears the image input
        print("Warning: No image provided.")
        return {} # Return empty dict, Gradio Label handles this

    print("Processing image...")
    try:
        # Ensure image is RGB
        image = image_pil.convert("RGB")

        # Preprocess using the loaded processor
        inputs = processor(images=image, return_tensors="pt").to(DEVICE)

        # Perform inference
        print("Running inference...")
        with torch.no_grad():
            outputs = model(**inputs)
            logits = outputs.logits

        # Get probabilities using softmax
        # outputs.logits is shape [1, num_labels], softmax over the last dim
        probabilities = torch.softmax(logits, dim=-1)[0] # Get probabilities for the first (and only) image

        # Create a dictionary of label -> score
        results = {}
        for i, prob in enumerate(probabilities):
            label = model.config.id2label[i]
            results[label] = round(prob.item(), 4) # Round for cleaner display

        print(f"Prediction results: {results}")
        return results

    except Exception as e:
        print(f"Error during prediction: {e}")
        # Return error in the format expected by gr.Label
        # Provide a user-friendly error message in the output
        return {"Error": f"Processing failed. Please try again or use a different image."}

# --- Define Example Images ---
example_dir = "examples"
example_images = []
if os.path.exists(example_dir) and os.listdir(example_dir): # Check if dir exists AND is not empty
    for img_name in os.listdir(example_dir):
        if img_name.lower().endswith(('.png', '.jpg', '.jpeg', '.webp')):
             example_images.append(os.path.join(example_dir, img_name))
    if example_images:
        print(f"Found examples: {example_images}")
    else:
        print("No valid image files found in 'examples' directory.")
else:
    print("No 'examples' directory found or it's empty. Examples will not be shown.")


# --- Custom CSS ---
css = """
body { font-family: 'Inter', sans-serif; } /* Use a clean sans-serif font */

/* Style the main title */
#app-title {
    text-align: center;
    font-weight: bold;
    font-size: 2.5em; /* Larger title */
    margin-bottom: 5px; /* Reduced space below title */
    color: #2c3e50; /* Darker color */
}

/* Style the description */
#app-description {
    text-align: center;
    font-size: 1.1em;
    margin-bottom: 25px; /* More space below description */
    color: #576574; /* Subdued color */
}
#app-description code { /* Style model name */
    font-weight: bold;
    background-color: #f1f2f6;
    padding: 2px 5px;
    border-radius: 4px;
}
#app-description strong { /* Style device name */
    color: #1abc9c; /* Highlight color for device */
}

/* Style the results area */
#prediction-label .label-name { font-weight: bold; font-size: 1.1em; }
#prediction-label .confidence { font-size: 1em; }

/* Style the results heading */
#results-heading {
    text-align: center;
    font-size: 1.2em; /* Slightly larger heading for results */
    margin-bottom: 10px; /* Space below heading */
    color: #34495e; /* Match other heading colors */
}

/* Style the examples section */
.gradio-container .examples-container { padding-top: 15px; }
.gradio-container .examples-header { font-size: 1.1em; font-weight: bold; margin-bottom: 10px; color: #34495e; }

/* Add a subtle border/shadow to input/output columns for definition */
#input-column, #output-column {
    border: 1px solid #e0e0e0;
    border-radius: 12px; /* More rounded corners */
    padding: 20px;
    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.05); /* Subtle shadow */
    background-color: #ffffff; /* Ensure white background */
}

/* Footer styling */
#app-footer {
    margin-top: 40px;
    padding-top: 20px;
    border-top: 1px solid #dfe6e9;
    text-align: center;
    font-size: 0.9em;
    color: #8395a7;
}
#app-footer a { color: #3498db; text-decoration: none; }
#app-footer a:hover { text-decoration: underline; }
"""

# --- Gradio Interface using Blocks and Theme ---
# Choose a theme: gr.themes.Soft(), gr.themes.Monochrome(), gr.themes.Glass(), etc.
theme = gr.themes.Soft(
    primary_hue="emerald", # Color scheme based on emerald green
    secondary_hue="blue",
    neutral_hue="slate",
    radius_size=gr.themes.sizes.radius_lg, # Larger corner radius
    spacing_size=gr.themes.sizes.spacing_lg, # More spacing
).set(
    # Further fine-tuning
    body_background_fill="#f8f9fa", # Very light grey background
    block_radius="12px",
)


with gr.Blocks(theme=theme, css=css) as iface:
    # Title and Description using Markdown for better formatting
    gr.Markdown("# AI vs Human Image Detector", elem_id="app-title")
    gr.Markdown(
        f"Upload an image to classify if it was likely generated by AI or created by a human. "
        f"Uses the `{MODEL_IDENTIFIER}` model. Running on **{str(DEVICE).upper()}**.",
        elem_id="app-description"
    )

    # Main layout with Input and Output side-by-side
    with gr.Row(variant='panel'): # 'panel' adds a light border/background
        with gr.Column(scale=1, min_width=300, elem_id="input-column"):
            image_input = gr.Image(
                type="pil",
                label="🖼️ Upload Your Image",
                sources=["upload", "webcam", "clipboard"],
                height=400, # Adjust height as needed
            )
            submit_button = gr.Button("🔍 Classify Image", variant="primary") # Make button prominent

        with gr.Column(scale=1, min_width=300, elem_id="output-column"):
            # Use elem_id and target with CSS for styling
            gr.Markdown("📊 **Prediction Results**", elem_id="results-heading")
            result_output = gr.Label(
                num_top_classes=2,
                label="Classification",
                elem_id="prediction-label"
            )

    # Examples Section
    if example_images: # Only show examples if they exist and list is not empty
        gr.Examples(
            examples=example_images,
            inputs=image_input,
            outputs=result_output,
            fn=classify_image,
            cache_examples=True, # Caching is good for static examples
            label="✨ Click an Example to Try!"
        )

    # Footer / Article section
    gr.Markdown(
       """
       ---
       This application uses a fine-tuned [SigLIP](https://huggingface.co/docs/transformers/model_doc/siglip) vision model
       specifically trained to differentiate between images generated by Artificial Intelligence and those created by humans.

       You can find the model card here: <a href='https://huggingface.co/{model_id}' target='_blank'>{model_id}</a>

       Fine tuning code available at [https://exnrt.com/blog/ai/fine-tuning-siglip2/](https://exnrt.com/blog/ai/fine-tuning-siglip2/).
       """.format(model_id=MODEL_IDENTIFIER),
       elem_id="app-footer"
    )

    # Connect the button click or image change to the prediction function
    # Use api_name for potential API usage later
    submit_button.click(fn=classify_image, inputs=image_input, outputs=result_output, api_name="classify_image_button")
    image_input.change(fn=classify_image, inputs=image_input, outputs=result_output, api_name="classify_image_change")


# --- Launch the App ---
if __name__ == "__main__":
    print("Launching Gradio interface...")
    iface.launch() # Add share=True for temporary public link if needed: iface.launch(share=True)
    print("Gradio interface launched.")