import gradio as gr
import numpy as np
from PIL import Image
import cv2

# -----------------------------
# STEP 1: FACE DETECTION + ROUTING (VERSION 2 - FOUNDATION)
# -----------------------------
# This version does NOT yet classify deepfakes.
# It ONLY decides:
# 1) Does the image contain a human face?
# 2) Route it to the correct analysis pipeline
# -----------------------------

# Load OpenCV Haar Cascade (lightweight & HF-friendly)
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")


def detect_faces(image_np):
    """
    Detect faces using OpenCV Haar Cascade
    Returns number of faces detected
    """
    gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
    faces = face_cascade.detectMultiScale(
        gray,
        scaleFactor=1.1,
        minNeighbors=5,
        minSize=(60, 60)
    )
    return faces


def route_image(image):
    if image is None:
        return "No image provided", "N/A"

    image_np = np.array(image)
    faces = detect_faces(image_np)

    # ROUTING LOGIC
    if len(faces) > 0:
        route = "🧑 Face Detected"
        explanation = (
            "This image contains one or more human faces.\n"
            "→ It will be analyzed using **deepfake face-detection models**\n"
            "(XceptionNet / EfficientNet in next step)."
        )
    else:
        route = "🖼️ No Face Detected"
        explanation = (
            "No human face detected in this image.\n"
            "→ It will be analyzed using **generic AI-image detection models**\n"
            "(GAN / diffusion detection)."
        )

    return route, explanation


# -----------------------------
# GRADIO UI
# -----------------------------
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # 🧭 Image Analysis Router (Version 2 – Step 1)

    This step determines **how the image should be analyzed**:
    - 👤 Face present → Deepfake detection pipeline
    - 🖼️ No face → Generic AI-image detection pipeline

    *(No deepfake classification is performed yet.)*
    """)

    with gr.Row():
        with gr.Column():
            image_input = gr.Image(type="pil", label="Upload Any Image")
            analyze_btn = gr.Button("Analyze Image 🔍")
        with gr.Column():
            route_output = gr.Textbox(label="Routing Decision")
            explanation_output = gr.Textbox(label="Explanation", lines=5)

    analyze_btn.click(
        fn=route_image,
        inputs=image_input,
        outputs=[route_output, explanation_output]
    )

    gr.Markdown("""
    ### ✅ Why this step matters
    - Prevents misuse of face-only deepfake models
    - Reduces false positives
    - Makes the system work for **ANY image**

    🔜 **Next step:** Integrate XceptionNet / EfficientNet classifiers.
    """)

# Launch
demo.launch()