import gradio as gr
import cv2
import numpy as np
import matplotlib.pyplot as plt
from typing import Tuple, List
import tempfile
import os

def detect_bends_and_angles(
    image,
    blur_kernel_size: int = 7,
    canny_threshold1: int = 30,
    canny_threshold2: int = 150,
    dilation_kernel_size: int = 2,
    hough_threshold: int = 50,
    min_line_length: int = 10,
    max_line_gap: int = 60,
    bend_threshold: int = 15,
    debug: bool = True
) -> Tuple[List[Tuple[int, int]], List[Tuple[Tuple[int, int], float]]]:
    """
    Detect bends and calculate angles relative to horizontal with configurable parameters.
    """
    # Convert image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    
    # Step 2: Apply Gaussian blur
    blurred = cv2.GaussianBlur(gray, (blur_kernel_size, blur_kernel_size), 0)
    
    # Step 3: Perform edge detection
    edges = cv2.Canny(blurred, canny_threshold1, canny_threshold2)
    
    # Step 4: Dilate edges
    kernel = np.ones((dilation_kernel_size, dilation_kernel_size), np.uint8)
    dilated = cv2.dilate(edges, kernel, iterations=1)
    
    # Step 5: Detect parallel lines and identify bends
    height, width = dilated.shape
    lines = cv2.HoughLinesP(
        dilated, 
        rho=1, 
        theta=np.pi/180, 
        threshold=hough_threshold, 
        minLineLength=min_line_length, 
        maxLineGap=max_line_gap
    )
    
    bend_points = []
    if lines is not None:
        segments = []
        for line in lines:
            x1, y1, x2, y2 = line[0]
            if x1 > x2:
                x1, x2 = x2, x1
                y1, y2 = y2, y1
            segments.append((x1, y1, x2, y2))

        segments.sort(key=lambda seg: seg[0], reverse=True)

        for i in range(len(segments) - 1):
            x1, y1, x2, y2 = segments[i]
            x1_next, y1_next, x2_next, y2_next = segments[i + 1]

            if abs(x1 - x1_next) < bend_threshold and abs(y1 - y1_next) < bend_threshold:
                bend_points.append((x1, y1))
    
    # Step 6: Calculate angles between bends
    angles = []
    for i in range(len(bend_points) - 1):
        x1, y1 = bend_points[i]
        x2, y2 = bend_points[i + 1]
        dx, dy = x2 - x1, y2 - y1
        angle = np.arctan2(dy, dx) * 180 / np.pi
        angle = angle if angle >= 0 else angle + 180
        angles.append((bend_points[i], angle))

    return bend_points, angles

def process_image(
    image,
    blur_kernel_size: int,
    canny_threshold1: int,
    canny_threshold2: int,
    dilation_kernel_size: int,
    hough_threshold: int,
    min_line_length: int,
    max_line_gap: int,
    bend_threshold: int
) -> Tuple[np.ndarray, str]:
    """
    Process the image and return the visualization and angle measurements.
    """
    bend_points, angles = detect_bends_and_angles(
        image,
        blur_kernel_size,
        canny_threshold1,
        canny_threshold2,
        dilation_kernel_size,
        hough_threshold,
        min_line_length,
        max_line_gap,
        bend_threshold
    )
    
    # Create visualization
    result_img = image.copy()
    for i, (x, y) in enumerate(bend_points):
        cv2.circle(result_img, (x, y), 5, (0, 0, 255), -1)
        cv2.putText(
            result_img, f"Bend {chr(65 + i)}", (x, y - 10),
            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1
        )
    for (x, y), angle in angles:
        cv2.putText(
            result_img, f"{angle:.1f}°", (x, y + 20),
            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1
        )
    
    # Create angle measurements text
    measurements = "Angle Measurements:\n"
    for i, ((x, y), angle) in enumerate(angles):
        measurements += f"Bend {chr(65 + i)} at ({x}, {y}): {angle:.1f}°\n"
    
    return result_img, measurements

# Create Gradio interface
def create_gradio_interface():
    with gr.Blocks(title="Angle Detection App", theme=gr.themes.Soft()) as interface:
        gr.Markdown("# Angle Detection App")
        gr.Markdown("Upload an image to detect bends and measure angles.")
        
        with gr.Row():
            with gr.Column():
                input_image = gr.Image(label="Input Image")
                
                with gr.Accordion("Algorithm Parameters", open=False):
                    blur_kernel_size = gr.Slider(
                        minimum=3, maximum=15, step=2,
                        value=7, label="Blur Kernel Size"
                    )
                    canny_threshold1 = gr.Slider(
                        minimum=0, maximum=100, step=10,
                        value=30, label="Canny Threshold 1"
                    )
                    canny_threshold2 = gr.Slider(
                        minimum=100, maximum=300, step=10,
                        value=150, label="Canny Threshold 2"
                    )
                    dilation_kernel_size = gr.Slider(
                        minimum=1, maximum=5, step=1,
                        value=2, label="Dilation Kernel Size"
                    )
                    hough_threshold = gr.Slider(
                        minimum=10, maximum=100, step=10,
                        value=50, label="Hough Threshold"
                    )
                    min_line_length = gr.Slider(
                        minimum=5, maximum=50, step=5,
                        value=10, label="Minimum Line Length"
                    )
                    max_line_gap = gr.Slider(
                        minimum=10, maximum=100, step=10,
                        value=60, label="Maximum Line Gap"
                    )
                    bend_threshold = gr.Slider(
                        minimum=5, maximum=30, step=5,
                        value=15, label="Bend Threshold"
                    )
                
                process_btn = gr.Button("Process Image", variant="primary")
            
            with gr.Column():
                output_image = gr.Image(label="Result")
                output_text = gr.Textbox(label="Measurements", lines=10)
        
        process_btn.click(
            fn=process_image,
            inputs=[
                input_image,
                blur_kernel_size,
                canny_threshold1,
                canny_threshold2,
                dilation_kernel_size,
                hough_threshold,
                min_line_length,
                max_line_gap,
                bend_threshold
            ],
            outputs=[output_image, output_text]
        )
        
        
    
    return interface

if __name__ == "__main__":
    interface = create_gradio_interface()
    # Get port from environment variable or use default
    port = int(os.environ.get("PORT", 7860))
    # Get host from environment variable or use default
    host = os.environ.get("HOST", "0.0.0.0")
    # Launch the interface
    interface.launch(
        server_name=host,
        server_port=port,
        share=False  # Set to True if you want to create a public URL
    )