import gradio as gr
import cv2
import numpy as np

def apply_four_filters(frame):
    # Gradio sends the frame as an RGB numpy array.
    # We shrink it by 50% so the 4-way grid doesn't lag your browser.
    frame = cv2.resize(frame, (0, 0), fx=0.5, fy=0.5)

    # --- 1. Original ---
    original = frame.copy()
    cv2.putText(original, "1. Original", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)

    # --- 2. Grayscale ---
    # Convert from RGB (Gradio format) to Grayscale
    gray_image = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
    # Convert back to a 3-channel image so it can be stacked with the original
    gray_3ch = cv2.cvtColor(gray_image, cv2.COLOR_GRAY2RGB)
    cv2.putText(gray_3ch, "2. Grayscale", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)

    # --- 3. Blurred ---
    blurred_image = cv2.GaussianBlur(gray_image, (3, 3), 0)
    # Convert back to 3-channels
    blur_3ch = cv2.cvtColor(blurred_image, cv2.COLOR_GRAY2RGB)
    cv2.putText(blur_3ch, "3. Blurred", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)

    # --- 4. Edges ---
    edges = cv2.Canny(blurred_image, 100, 200)
    # Convert back to 3-channels
    edges_3ch = cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)
    cv2.putText(edges_3ch, "4. Edges", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)

    # --- STACKING THE IMAGES ---
    # np.hstack tapes them side-by-side (Original | Grayscale)
    top_row = np.hstack((original, gray_3ch))
    bottom_row = np.hstack((blur_3ch, edges_3ch))

    # np.vstack tapes the top row and bottom row together
    final_grid = np.vstack((top_row, bottom_row))

    return final_grid

# Build the Gradio interface
demo = gr.Interface(
    fn=apply_four_filters,
    inputs=gr.Image(sources=["webcam"], streaming=True), # 'streaming=True' creates the live feed
    outputs="image",
    title="Live 4-Way Computer Vision Feed",
    description="Original, Grayscale, Gaussian Blur, and Canny Edge Detection running in real-time.",
    live=True # Tells Gradio to continuously loop the function
)

if __name__ == "__main__":
    demo.launch()