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import cv2
import pandas as pd
import json
from thefuzz import fuzz

def preprocess_entity(entity):
    """Preprocess entities by removing commas in numerical values."""
    return entity.replace(",", "").strip()

def calculate_proximity_score(box_a, box_b):
    """Calculate a refined proximity score taking into account typical reading flows."""
    vertical_overlap = max(0, min(box_a["bottom"], box_b["bottom"]) - max(box_a["top"], box_b["top"]))
    vertical_distance = 0 if vertical_overlap > 0 else min(abs(box_a["top"] - box_b["bottom"]), abs(box_a["bottom"] - box_b["top"]))
    horizontal_overlap = max(0, min(box_a["right"], box_b["right"]) - max(box_a["left"], box_b["left"]))
    horizontal_distance = 0 if horizontal_overlap > 0 else abs(box_a["right"] - box_b["left"])
    return horizontal_distance + 2 * vertical_distance

def is_nearby(box_a, box_b, max_line_difference=1, max_distance=50):
    return calculate_proximity_score(box_a, box_b) <= max_distance + 2 * max_line_difference

def merge_boxes(boxes):
    """Merge a list of bounding boxes into a single bounding box."""
    min_left = min(box["left"] for box in boxes)
    max_right = max(box["right"] for box in boxes)
    min_top = min(box["top"] for box in boxes)
    max_bottom = max(box["bottom"] for box in boxes)
    return {"left": min_left, "right": max_right, "top": min_top, "bottom": max_bottom}

def find_potential_matches(dataframe, token, threshold=80):
    potential_matches = []
    for _, row in dataframe.iterrows():
        ocr_text = preprocess_entity(row["text"])
        score = fuzz.ratio(token, ocr_text)
        if score > threshold:
            potential_matches.append({
                "box": {"left": row["left"], "right": row["right"], "top": row["top"], "bottom": row["bottom"]},
                "score": score
            })
    return potential_matches

def find_best_sequence_heuristic(matches_list):
    if not matches_list or len(matches_list[0]) == 0:
        return []
    best_sequence = [min(matches_list[0], key=lambda match: match["score"])]
    for next_matches in matches_list[1:]:
        current_box = best_sequence[-1]["box"]
        next_best_match = min(next_matches, key=lambda match: calculate_proximity_score(current_box, match["box"]))
        best_sequence.append(next_best_match)
    return best_sequence

def process_single_token_entity(dataframe, entity, threshold=75):
    best_match = None
    best_score = threshold
    entity = preprocess_entity(entity)
    for _, row in dataframe.iterrows():
        ocr_text = preprocess_entity(row["text"])
        score = fuzz.ratio(entity, ocr_text)
        if score > best_score:
            best_score = score
            best_match = {
                "left": row["left"], "right": row["right"],
                "top": row["top"], "bottom": row["bottom"]
            }
    return best_match

def process_multi_token_entity(dataframe, entity, threshold=85):
    tokens = entity.split()
    all_potential_matches = [find_potential_matches(dataframe, token, threshold) for token in tokens]
    if not all([matches for matches in all_potential_matches]):
        return None
    best_sequence = find_best_sequence_heuristic(all_potential_matches)
    if best_sequence:
        boxes_to_merge = [match["box"] for match in best_sequence]
        return merge_boxes(boxes_to_merge)
    return None

def draw_bounding_boxes(image_path, bounding_boxes, entity_names):
    image = cv2.imread(image_path)
    font = cv2.FONT_HERSHEY_SIMPLEX
    for box, name in zip(bounding_boxes, entity_names):
        if box:
            cv2.rectangle(image, (box["left"], box["top"]), (box["right"], box["bottom"]), (0, 255, 0), 2)
            cv2.putText(image, name, (box["left"], max(box["top"] - 10, 0)), font, 0.5, (0, 0, 255), 2)
    cv2.imwrite("annotated_image.jpg_using_dp.jpg", image)

def main(json_path, csv_path, image_path):
    with open(json_path, "r") as f:
        data = json.load(f)
    dataframe = pd.read_csv(csv_path)
    bounding_boxes = []
    entity_names = []

    for section in ["invoice_details", "Payment Details", "amounts_and_tax"]:
        entities = data.get(section, {})
        for entity_name, entity_value in entities.items():
            entity_value_no_comma = preprocess_entity(entity_value)
            if " " in entity_value_no_comma:
                box = process_multi_token_entity(dataframe, entity_value_no_comma)
            else:
                box = process_single_token_entity(dataframe, entity_value_no_comma)
            if box:
                bounding_boxes.append(box)
                entity_names.append(entity_name)

    draw_bounding_boxes(image_path, bounding_boxes, entity_names)

main("/home/ritik1s/Desktop/bbox_issues/temp_GPT/row_skip.json", "/home/ritik1s/Desktop/bbox_issues/temp_GPT/check.csv", "/home/ritik1s/Desktop/bbox_issues/temp_GPT/check.jpeg")
# main("/home/ritik1s/Desktop/bbox_issues/temp_GPT/row_skip.json", "/home/ritik1s/Desktop/bbox_issues/temp_GPT/check.csv", "/home/ritik1s/Desktop/bbox_issues/temp_GPT/check.jpeg")