from PIL import Image, ImageFilter
import cv2
import pandas as pd

def get_index(string, substring):
    
    start_index = string.find(substring)  # Starting index of "shahzain"
    end_index = start_index + len(substring)  # Ending index of "shahzain"
    
    return int(start_index), int(end_index)

def get_latest_value_from_csv(search_key):
    try:
        output_dict = {}
        df = pd.read_csv("/content/log.csv")
        filtered_df = df[df["OCR Extracted Text"] == search_key]
        # Sort the filtered DataFrame by the timestamp column in descending order
        sorted_df = filtered_df.sort_values("timestamp", ascending=False)
        if not filtered_df.empty:
            # Extract the latest row
            latest_row = sorted_df.iloc[0]
            # Print the values of specific columns that are not null
            columns_to_print = ["NAME", "DOB", "GENDER", "COUNTRY", "DOCUMENT NUMBER"]
            for column in columns_to_print:
                if pd.notnull(latest_row[column]):
                    output_dict[column] = latest_row[column]
            print("Training data from user", output_dict)
        else:
            print("Annotated Data Not Found")
        
        return True, output_dict
    except Exception as e:
        print("Exception in e ", str(e))
        return False, output_dict
    
def image_enhacement(file_path):

    image = Image.open(file_path)
    upscaled_image = image.resize((image.width*2, image.height*2), resample=Image.Resampling.BILINEAR)
    sharpened_image = upscaled_image.filter(ImageFilter.SHARPEN)
    sharpened_image.save(file_path)
    
    sharpened_image = cv2.imread(file_path)
    smoothed_image = cv2.GaussianBlur(sharpened_image, (5, 5), 0)     # Apply smoothing
    
    # Set the desired output level range
    output_min = 55
    output_max = 255

    # Calculate the minimum and maximum pixel values in the image
    # min_value = np.min(image)
    # max_value = np.max(image)

    normalized_image = cv2.normalize(smoothed_image, None, output_min, output_max, cv2.NORM_MINMAX) # Normalize the image to the desired output level range
    cv2.imwrite(file_path, normalized_image)