Birger Moell
Added files for HF
0de5d5b
import streamlit as st
import numpy as np
import scipy.stats as stats
from fpdf import FPDF
import base64
import os
from plots import test_profile
import matplotlib.pyplot as plt
from PIL import Image
test_dict = {
"animal": {
"low_age_low_education": {
"mean": 21.0,
"std": 7.0
}, "low_age_high_education": {
"mean": 22.4,
"std": 6.8
}, "high_age_low_education": {
"mean": 22.1,
"std": 5.7
}, "high_age_high_education": {
"mean": 25.6,
"std": 5.6
}
}, "verb": {
"low_age_low_education": {
"mean": 17.6,
"std": 4.3
}, "low_age_high_education": {
"mean": 20.5,
"std": 5.4
}, "high_age_low_education": {
"mean": 16.7,
"std": 6.1
}, "high_age_high_education": {
"mean": 22.7,
"std": 5.1
}
}, "repetition": {
"low_age_low_education": {
"mean": 24.8,
"std": 4.8
}, "low_age_high_education": {
"mean": 25.9,
"std": 3.7
}, "high_age_low_education": {
"mean": 24.0,
"std": 4.9
}, "high_age_high_education": {
"mean": 25.8,
"std": 3.8
}
}, "months_backward": {
"low_age_low_education": {
"mean": 10.3,
"std": 4.5
}, "low_age_high_education": {
"mean": 9.8,
"std": 3.2
}, "high_age_low_education": {
"mean": 10.0,
"std": 3.2
}, "high_age_high_education": {
"mean": 9.9,
"std": 3.5
}
},
"logicogrammatic": {
"low_age_low_education": {
"mean": 27.2,
"std": 3.8
}, "low_age_high_education": {
"mean": 27.4,
"std": 3.1
}, "high_age_low_education": {
"mean": 23.5,
"std": 4.2
}, "high_age_high_education": {
"mean": 27.2,
"std": 3.3
}
},"inference": {
"low_age_low_education": {
"mean": 28.2,
"std": 2.4
}, "low_age_high_education": {
"mean": 28.4,
"std": 2.8
}, "high_age_low_education": {
"mean": 25.2,
"std": 3.9
}, "high_age_high_education": {
"mean": 27.3,
"std": 2.9
}
}, "reading_speed": {
"low_age_low_education": {
"mean": 21.5,
"std": 5.4
}, "low_age_high_education": {
"mean": 27.3,
"std": 5.5
}, "high_age_low_education": {
"mean": 23.0,
"std": 7.2
}, "high_age_high_education": {
"mean": 28.8,
"std": 5.2
}
}, "decoding_words": {
"low_age_low_education": {
"mean": 107.6,
"std": 27.8
}, "low_age_high_education": {
"mean": 112.9,
"std": 22.7
}, "high_age_low_education": {
"mean": 111.4,
"std": 26.1
}, "high_age_high_education": {
"mean": 122.9,
"std": 28.2
}
}, "decoding_non_words": {
"low_age_low_education": {
"mean": 95.8,
"std": 26.6
}, "low_age_high_education": {
"mean": 105.4,
"std": 29.5
}, "high_age_low_education": {
"mean": 103.4,
"std": 25.2
}, "high_age_high_education": {
"mean": 118.0,
"std": 26.8
}
},
# jönsson och winnerstam 2012
"pataka": {
"low_age_low_education": {
"mean": 5.8,
"std": 1.0
}, "low_age_high_education": {
"mean": 5.8,
"std": 1.0
}, "high_age_low_education": {
"mean": 5.8,
"std": 1.0
}, "high_age_high_education": {
"mean": 5.8,
"std": 1.0
}
}
}
# Function to calculate z-score
def calculate_z_score(test_score, mean, std_dev):
return (test_score - mean) / std_dev
def z_score_calculator(value, norm_mean, norm_sd):
z_value = (value - norm_mean) / norm_sd
stanine_value = round(1.25 * z_value + 5.5)
z_score = round(z_value, 2)
return z_score, stanine_value
def test_calculator(age, education, values, test):
if age <= 60 and education <= 12:
norm_mean = test_dict[test]["low_age_low_education"]["mean"]
norm_sd = test_dict[test]["low_age_low_education"]["std"]
z_score, stanine_value = z_score_calculator(values, norm_mean, norm_sd)
return norm_mean, norm_sd, z_score, stanine_value
elif age <= 60 and education > 12:
norm_mean = test_dict[test]["low_age_high_education"]["mean"]
norm_sd = test_dict[test]["low_age_high_education"]["std"]
z_score, stanine_value = z_score_calculator(values, norm_mean, norm_sd)
return norm_mean, norm_sd, z_score, stanine_value
elif age > 60 and education <= 12:
norm_mean = test_dict[test]["high_age_low_education"]["mean"]
norm_sd = test_dict[test]["high_age_low_education"]["std"]
z_score, stanine_value = z_score_calculator(values, norm_mean, norm_sd)
return norm_mean, norm_sd, z_score, stanine_value
elif age > 60 and education > 12:
norm_mean = test_dict[test]["high_age_high_education"]["mean"]
norm_sd = test_dict[test]["high_age_high_education"]["std"]
z_score, stanine_value = z_score_calculator(values, norm_mean, norm_sd)
return norm_mean, norm_sd, z_score, stanine_value
else:
print("missing value/ wrong format")
def bnt_calculator(age, education, bnt):
if age <= 60 and education <= 12:
norm_mean = 54.5
norm_sd = 3.2
z_score, stanine_value = z_score_calculator(bnt, norm_mean, norm_sd)
return norm_mean, norm_sd, z_score, stanine_value
elif age <= 60 and education > 12:
norm_mean = 54.0
norm_sd = 4.4
z_score, stanine_value = z_score_calculator(bnt, norm_mean, norm_sd)
return norm_mean, norm_sd, z_score, stanine_value
elif age > 60 and education <= 12:
norm_mean = 54.8
norm_sd = 3.3
z_score, stanine_value = z_score_calculator(bnt, norm_mean, norm_sd)
return norm_mean, norm_sd, z_score, stanine_value
elif age > 60 and education > 12:
norm_mean = 56.2
norm_sd = 3.4
z_score, stanine_value = z_score_calculator(bnt, norm_mean, norm_sd)
return norm_mean, norm_sd, z_score, stanine_value
else:
print("missing value/ wrong format")
def fas_calculator(age, education, fas):
if age <= 60 and education <= 12:
norm_mean = 42.7
norm_sd = 13.7
z_score, stanine_value = z_score_calculator(fas, norm_mean, norm_sd)
return norm_mean, norm_sd, z_score, stanine_value
elif age <= 60 and education > 12:
norm_mean = 46.7
norm_sd = 13.7
z_score, stanine_value = z_score_calculator(fas, norm_mean, norm_sd)
return norm_mean, norm_sd, z_score, stanine_value
elif age > 60 and education <= 12:
norm_mean = 46.9
norm_sd = 10.4
z_score, stanine_value = z_score_calculator(fas, norm_mean, norm_sd)
return norm_mean, norm_sd, z_score, stanine_value
elif age > 60 and education > 12:
norm_mean = 51.6
norm_sd = 12.6
z_score, stanine_value = z_score_calculator(fas, norm_mean, norm_sd)
return norm_mean, norm_sd, z_score, stanine_value
else:
print("missing value/ wrong format")
def generate_graph(test_dict):
# Create a plot
fig, ax = plt.subplots()
# Adjust the margins to fix the labels being cut off
fig.subplots_adjust(left=0.25)
# Set axis labels and title
ax.set_xlabel('Stanine values')
ax.set_ylabel('Test')
# Set the x-axis to display the stanine values
ax.set_xticks([1, 2, 3, 4, 5, 6, 7, 8, 9])
ax.set_xticklabels(['1', '2', '3', '4', '5', '6', '7', '8', '9'])
# Set the y-axis to display the tests
ax.set_yticks([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
# Set the test labels to the keys in test_dict (in reverse order)
ax.set_yticklabels(reversed(list(test_dict.keys())))
# Set the range of the x-axis
ax.set_xlim([0, 10])
i = 1
for test in reversed(list(test_dict.keys())):
#print("the test is", test)
ax.scatter(test_dict[test][4], i, s=50, color='black', label=test)
i = i + 1
# Save the graph as a png file
fig.savefig('test_profile.png')
return 'test_profile.png'
def create_pdf(test_dict, logo_path, plot_path):
pdf = FPDF()
pdf.add_page()
pdf.set_xy(0, 0)
pdf.set_font("Arial", size=12)
# Add logos
x_positions = [25, 85, 145]
for i, logo_path in enumerate(logo_paths):
pdf.image(logo_path, x=x_positions[i], y=8, w=40)
pdf.set_xy(10, 50)
# Add title and center it
title = "Patient Summary"
pdf.set_font("Times", style="B", size=12)
title_width = pdf.get_string_width(title) + 6
pdf.cell((210 - title_width) / 2)
pdf.cell(title_width, 10, title, 0, 1, "C")
# Add z-score and center it
pdf.set_font("Times", size=12)
tests_per_line = 1
tests_count = len(test_dict)
line_count = tests_count // tests_per_line + (1 if tests_count % tests_per_line > 0 else 0)
test_index = 0
left_margin = 15
col_width = (210 - 2 * left_margin) / tests_per_line
for line in range(line_count):
pdf.set_x(left_margin)
for test in list(test_dict.keys())[test_index:test_index + tests_per_line]:
# Set the font to bold for the test value
pdf.set_font("Arial", style="B", size=8)
test_text = "{}: ".format(test)
test_width = pdf.get_string_width(test_text)
pdf.cell(test_width, 6, test_text, 0, 0, "C") # Changed the line height to 6
# Set the font to normal for the rest of the text
pdf.set_font("Arial", size=8)
z_score_text = "{:.2f} (Mean: {:.2f}, Std: {:.2f})".format(
test_dict[test][3], test_dict[test][1], test_dict[test][2]
)
z_score_width = pdf.get_string_width(z_score_text) + 2 # Reduced the additional width to 2
pdf.cell((210 / tests_per_line - test_width - z_score_width) / 2)
pdf.cell(z_score_width, 6, z_score_text, 0, 0, "C") # Changed the line height to 6
test_index += 1
pdf.ln(12) # Reduced the line spacing to 12
# Add logo
pdf.add_page()
pdf.image(plot_path, x=10, y=20, w=200)
# pdf.set_xy(10, 40)
# Add tool description and center it
pdf.set_xy(10, 200)
pdf.set_font("Arial", size=10)
description = "This PDF report was generated using the Patient Summary App."
pdf.multi_cell(0, 10, description, 0, "C")
# Add explanatory text about the collaboration between KI and KTH
pdf.set_xy(10, 220)
pdf.set_font("Arial", size=8)
collaboration_text = (
"Den här PDF:en är en del av ett samarbetsprojekt mellan Karolinska Institutet (KI) och "
"Kungliga Tekniska Högskolan (KTH) med målsättningen att använda artificiell intelligens (AI) och "
"teknik för att minska administration i sjukhusarbete. Projektet fokuserar på att utveckla och "
"implementera AI-baserade lösningar för att förbättra arbetsflöden, öka effektiviteten och "
"minska den administrativa bördan för sjukvårdspersonal. För frågor om formuläret kontakta Fredrik Sand fredrik.sand-aronsson@regionstockholm.se, för frågor om teknik kontakta Birger Moëll bmoell@kth.se."
)
line_width = 190
line_height = pdf.font_size_pt * 0.6
lines = collaboration_text.split(' ')
current_line = ''
for word in lines:
if pdf.get_string_width(current_line + word) < line_width:
current_line += word + ' '
else:
pdf.cell(line_width, line_height, current_line, 0, 1)
current_line = word + ' '
pdf.cell(line_width, line_height, current_line, 0, 1)
return pdf
def pdf_to_base64(pdf):
with open(pdf, "rb") as file:
return base64.b64encode(file.read()).decode('utf-8')
# Title and description
st.title("Z-Score Calculator")
st.write("Enter your test score, age, and education level to calculate the z-score.")
# Input fields
#test_score = st.number_input("Test Score", min_value=0, value=0, step=1)
age = st.number_input("Age", min_value=0, value=18, step=1)
education_level = st.number_input("Education Level in years", min_value=0, value=18, step=1)
isw = st.number_input("ISW", min_value=0.0, value=0.0, step=0.01)
bnt = st.number_input("BNT", min_value=0, value=0, step=1)
fas = st.number_input("FAS", min_value=0, value=0, step=1)
animal = st.number_input("Animal", min_value=0, value=0, step=1)
verb = st.number_input("Verb", min_value=0, value=0, step=1)
repetition = st.number_input("Repetition", min_value=0, value=0, step=1)
logicogrammatic = st.number_input("Logicogrammatic", min_value=0, value=0, step=1)
inference = st.number_input("Inference", min_value=0, value=0, step=1)
reading_speed = st.number_input("Reading Speed", min_value=0, value=0, step=1)
decoding_words = st.number_input("Decoding Words", min_value=0.0, value=0.0, step=0.01)
decoding_non_words = st.number_input("Decoding Non-Words", min_value=0.0, value=0.0, step=0.01)
months_backward = st.number_input("Months Backward", min_value=0.0, value=0.0, step=0.01)
pataka = st.number_input("Pataka", min_value=0.0, value=0.0, step=0.01)
# add all the tests
# Calculate mean and standard deviation based on age and education level
# For simplicity, we will use made-up values for mean and std_dev
mean = np.random.randint(50, 100)
std_dev = np.random.randint(10, 30)
# Calculate z-score and display result
if st.button("Calculate Z-Score"):
profile = test_profile(age, education_level, isw, bnt, fas)
# for each value in the profile, calculate the z-score
bnt_mean, bnt_std, z_bnt, stanine_bnt = bnt_calculator(age, education_level, bnt)
fas_mean, fas_std, z_fas, stanine_fas = fas_calculator(age, education_level, fas)
animal_mean, animal_std, animal_z, animal_stanine = test_calculator(age, education_level, animal, "animal")
verb_mean, verb_std, verb_z, verb_stanine = test_calculator(age, education_level, verb, "verb")
repetition_mean, repetition_std, repetition_z, repetition_stanine = test_calculator(age, education_level, repetition, "repetition")
months_backward_mean, months_backward_std, months_backward_z, months_backward_stanine = test_calculator(age, education_level, months_backward, "months_backward")
logicogrammatic_mean, logicogrammatic_std, logicogrammatic_z, logicogrammatic_stanine = test_calculator(age, education_level, logicogrammatic, "logicogrammatic")
inference_mean, inference_std, inference_z, inference_stanine = test_calculator(age, education_level, inference, "inference")
reading_speed_mean, reading_speed_std, reading_speed_z, reading_speed_stanine = test_calculator(age, education_level, reading_speed, "reading_speed")
decoding_words_mean, decoding_words_std, decoding_words_z, decoding_words_stanine = test_calculator(age, education_level, decoding_words, "decoding_words")
decoding_non_words_mean, decoding_non_words_std, decoding_non_words_z, decoding_non_words_stanine = test_calculator(age, education_level, decoding_non_words, "decoding_non_words")
pataka_mean, pataka_std, pataka_z, pataka_stanine = test_calculator(age, education_level, pataka, "pataka")
## add all the tests with their values to an array
## CREATA A DICTORINARY WITH ALL THE TESTS AND THEIR Z-SCORES AND STANINE VALUES
test_dict = {
"bnt": [bnt, bnt_mean, bnt_std, z_bnt, stanine_bnt],
"fas": [fas, fas_mean, fas_std, z_fas, stanine_fas],
"animal": [animal, animal_mean, animal_std, animal_z, animal_stanine],
"verb": [verb, verb_mean, verb_std, verb_z, verb_stanine],
"repetition": [repetition, repetition_mean, repetition_std, repetition_z, repetition_stanine],
"months_backward": [months_backward, months_backward_mean, months_backward_std, months_backward_z, months_backward_stanine],
"logicogrammatic": [logicogrammatic, logicogrammatic_mean, logicogrammatic_std, logicogrammatic_z, logicogrammatic_stanine],
"inference": [inference, inference_mean, inference_std, inference_z, inference_stanine],
"reading_speed": [reading_speed, reading_speed_mean, reading_speed_std, reading_speed_z, reading_speed_stanine],
"decoding_words": [decoding_words, decoding_words_mean, decoding_words_std, decoding_words_z, decoding_words_stanine],
"decoding_non_words": [decoding_non_words, decoding_non_words_mean, decoding_non_words_std, decoding_non_words_z, decoding_non_words_stanine],
"pataka": [pataka, pataka_mean, pataka_std, pataka_z, pataka_stanine]
}
# z_values = [z_bnt, z_fas, animal_z, verb_z, repetition_z, months_backward_z, logicogrammatic_z, inference_z, reading_speed_z, decoding_words_z, decoding_non_words_z, pataka_z]
# ## add all the stanines to a list
# stanine_values = [stanine_bnt, stanine_fas, animal_stanine, verb_stanine, repetition_stanine, months_backward_stanine, logicogrammatic_stanine, inference_stanine, reading_speed_stanine, decoding_words_stanine, decoding_non_words_stanine, pataka_stanine]
# loop over and write out all the z values
# loop over all the values in test dict and print out the z-score, mean and std_dev and stanine
for key, value in test_dict.items():
st.write(f"Your {key} z-score is: {value[3]:.2f}")
st.write(f"Mean: {value[1]}, Standard Deviation: {value[2]}")
st.write(f"Stanine: {value[4]}")
# Create PDF
logo_paths = ["logo.jpg", "logo2.jpg", "logo3.jpg"]
# create the plot from the dataframe
# check if education level is more than 12 years, if more than 12, set value to one, otherwise zero
plot_path = generate_graph(test_dict)
# create an image from the plot and add to streamlit display
image = Image.open(plot_path)
st.image(image, caption='Stanine plot', use_column_width=True)
pdf_filename = "z_score_report.pdf"
pdf = create_pdf(test_dict, logo_paths, plot_path)
pdf.output(name=pdf_filename)
# Download PDF
with open(pdf_filename, "rb") as file:
base64_pdf = base64.b64encode(file.read()).decode('utf-8')
pdf_display = f'<a href="data:application/octet-stream;base64,{base64_pdf}" download="{pdf_filename}">Download PDF</a>'
st.markdown(pdf_display, unsafe_allow_html=True)
# Remove PDF file after download
if os.path.exists(pdf_filename):
os.remove(pdf_filename)