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 ---
warnings.filterwarnings("ignore", message="Possibly corrupt EXIF data.")
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}")
    raise gr.Error(f"Failed to load the model: {e}. Cannot start the application.") from e

# --- Prediction Function ---
def classify_image(image_pil):
    if image_pil is None:
        print("Warning: No image provided.")
        return {}

    print("Processing image...")
    try:
        image = image_pil.convert("RGB")
        inputs = processor(images=image, return_tensors="pt").to(DEVICE)

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

        probabilities = torch.softmax(logits, dim=-1)[0]
        results = {}
        for i, prob in enumerate(probabilities):
            label = model.config.id2label[i]
            results[label] = round(prob.item(), 4)

        print(f"Prediction results: {results}")
        return results
    except Exception as e:
        print(f"Error during prediction: {e}")
        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):
    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 for Dark Theme Adjustments ---
# Minimal CSS - let the dark theme handle most things
css = """
body { font-family: 'Inter', sans-serif; }

/* Style the main title */
#app-title {
    text-align: center;
    font-weight: bold;
    font-size: 2.5em;
    margin-bottom: 5px;
    /* color removed - let theme handle */
}

/* Style the description */
#app-description {
    text-align: center;
    font-size: 1.1em;
    margin-bottom: 25px;
    /* color removed - let theme handle */
}
#app-description code { /* Style model name - theme might handle this, but can force */
    font-weight: bold;
    background-color: rgba(255, 255, 255, 0.1); /* Slightly lighter background for code */
    padding: 2px 5px;
    border-radius: 4px;
    color: #c5f7dc; /* Light green text for code block */
}
#app-description strong { /* Style device name */
    color: #2dd4bf; /* Brighter teal/emerald for dark theme */
    font-weight: bold;
}

/* Style the results heading */
#results-heading {
    text-align: center;
    font-size: 1.2em;
    margin-bottom: 10px;
    /* color removed - let theme handle */
}

/* Add some definition to input/output columns if needed */
#input-column, #output-column {
    border: 1px solid #4b5563; /* Darker border for dark theme */
    border-radius: 12px;
    padding: 20px;
    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1); /* Subtle shadow, works on dark too */
    /* background-color removed - let theme handle */
}

/* Ensure label text inside columns is readable */
#prediction-label .label-name { font-weight: bold; font-size: 1.1em; }
#prediction-label .confidence { font-size: 1em; }


/* Footer styling */
#app-footer {
    margin-top: 40px;
    padding-top: 20px;
    border-top: 1px solid #374151; /* Darker border for footer */
    text-align: center;
    font-size: 0.9em;
    /* color removed - let theme handle */
}
#app-footer a {
    color: #60a5fa; /* Lighter blue for links */
    text-decoration: none;
}
#app-footer a:hover {
    text-decoration: underline;
}
"""

# --- Gradio Interface using Blocks and Theme ---
# Use the theme string identifier for the dark mode variant
# Other options: "default/dark", "monochrome/dark", "glass/dark"
with gr.Blocks(theme="soft/dark", css=css) as iface:  # <<< CHANGE IS HERE
    # Title and Description
    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 gr.Row(variant='panel'):
        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,
            )
            submit_button = gr.Button("🔍 Classify Image", variant="primary")

        with gr.Column(scale=1, min_width=300, elem_id="output-column"):
            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:
        gr.Examples(
            examples=example_images,
            inputs=image_input,
            outputs=result_output,
            fn=classify_image,
            cache_examples=True,
            label="✨ Click an Example to Try!"
        )

    # Footer / Article section
    gr.Markdown(f"""
       ---
       **How it Works:**
       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.

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

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

    # Connect events
    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()
    print("Gradio interface launched.")