import numpy as np 
import matplotlib.pyplot as plt
import streamlit as st
from PIL import Image
import cv2
import networkx as nx  # <-- Added

def build_graph(pil_image, detections, annotations, class_names):
    def dist(p1, p2):
        return np.hypot(p1[0] - p2[0], p1[1] - p2[1])

    def angle_between(p1, p2):
        return np.degrees(np.arctan2(p2[1] - p1[1], p2[0] - p1[0])) % 180

    def lines_are_similar(line1, line2, max_distance=10, max_angle_diff=10):
        (x1, y1), (x2, y2) = line1
        (x3, y3), (x4, y4) = line2
        angle1 = angle_between((x1, y1), (x2, y2))
        angle2 = angle_between((x3, y3), (x4, y4))
        if abs(angle1 - angle2) > max_angle_diff:
            return False
        mid1 = ((x1 + x2) / 2, (y1 + y2) / 2)
        mid2 = ((x3 + x4) / 2, (y3 + y4) / 2)
        return dist(mid1, mid2) < max_distance

    def merge_similar_lines(lines):
        if not lines:
            return []
        merged, used = [], set()
        for i, l1 in enumerate(lines):
            if i in used: continue
            group = [l1]; used.add(i)
            for j, l2 in enumerate(lines):
                if j != i and j not in used and lines_are_similar(l1, l2):
                    group.append(l2); used.add(j)
            x_coords, y_coords = [], []
            for (x1, y1), (x2, y2) in group:
                x_coords.extend([x1, x2])
                y_coords.extend([y1, y2])
            merged.append(((int(min(x_coords)), int(min(y_coords))), (int(max(x_coords)), int(max(y_coords)))))
        return merged

    def point_inside_bbox(px, py, bbox):
        x1, y1, x2, y2 = bbox
        return x1 <= px <= x2 and y1 <= py <= y2

    def find_nearest_symbol(point, symbols, max_dist=15):
        px, py = point
        nearest_sym, nearest_dist = None, float('inf')
        for sym in symbols:
            sx, sy = sym['pos']
            d = dist((px, py), (sx, sy))
            if d < nearest_dist and d <= max_dist:
                nearest_sym, nearest_dist = sym, d
        if nearest_sym is None:
            for sym in symbols:
                if point_inside_bbox(px, py, sym['bbox']):
                    nearest_sym = sym
                    break
        return nearest_sym

    # Convert PIL image to OpenCV format
    image_cv = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)

    # Filter symbols
    allowed_types = {"connector", "crossing", "border_node"}
    symbols = []
    for idx, (box, class_id) in enumerate(zip(detections.xyxy, detections.class_id)):
        label = class_names[class_id]
        if label in allowed_types:
            x1, y1, x2, y2 = map(int, box)
            cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
            symbols.append({
                "id": f"{label}_{idx}",
                "type": label,
                "pos": (cx, cy),
                "bbox": (x1, y1, x2, y2)
            })

    # Hough line detection
    gray = cv2.cvtColor(image_cv, cv2.COLOR_BGR2GRAY)
    blurred = cv2.GaussianBlur(gray, (3, 3), 0)
    edges = cv2.Canny(blurred, 50, 150, apertureSize=3)
    lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=50, minLineLength=50, maxLineGap=10)
    detected_lines = [((x1, y1), (x2, y2)) for line in lines for x1, y1, x2, y2 in line] if lines is not None else []

    merged_lines = merge_similar_lines(detected_lines)

    filtered_lines = []
    for pt1, pt2 in merged_lines:
        sym1 = find_nearest_symbol(pt1, symbols)
        sym2 = find_nearest_symbol(pt2, symbols)
        if sym1 and sym2 and sym1 != sym2:
            filtered_lines.append((pt1, pt2))

    # Draw results on image
    output = image_cv.copy()
    for sym in symbols:
        x1, y1, x2, y2 = sym["bbox"]
        cx, cy = sym["pos"]
        cv2.rectangle(output, (x1, y1), (x2, y2), (255, 0, 0), 2)
        cv2.circle(output, (cx, cy), 3, (0, 255, 255), -1)
        cv2.putText(output, sym["type"], (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, .6, (255, 0, 0), 1)

    for (x1, y1), (x2, y2) in filtered_lines:
        cv2.line(output, (x1, y1), (x2, y2), (0, 100, 255), 2)

    st.image(Image.fromarray(cv2.cvtColor(output, cv2.COLOR_BGR2RGB)),
             caption="Graph: Merged Lines + Detected Symbols",
             use_column_width=True)

    # === Additional: Plot NetworkX graph ===
    # Ensure each symbol has a unique ID
    for i, sym in enumerate(symbols):
        sym['id'] = f"{sym['type']}_{i}"

    # Build graph
    G = nx.Graph()
    for sym in symbols:
        G.add_node(sym['id'], label=sym['type'], pos=sym['pos'])

    for pt1, pt2 in filtered_lines:
        sym1 = find_nearest_symbol(pt1, symbols)
        sym2 = find_nearest_symbol(pt2, symbols)
        if sym1 and sym2 and sym1['id'] != sym2['id']:
            G.add_edge(sym1['id'], sym2['id'])

    # Draw NetworkX graph in Streamlit
    fig, ax = plt.subplots(figsize=(8, 8))
    pos = {node: data['pos'] for node, data in G.nodes(data=True)}
    labels = {node: data['label'] for node, data in G.nodes(data=True)}
    nx.draw(G, pos, labels=labels, node_size=700, node_color='lightblue',
            font_size=8, with_labels=True, ax=ax)
    ax.set_title("Extracted Graph from Detected Symbols and Lines")
    st.pyplot(fig)


    return G