Create app.py

app.py

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+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
+
+# 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 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(BNT_stanine, FAS_stanine):
+        # Create a plot
+        fig, ax = plt.subplots()
+
+        # Set axis labels and title
+        ax.set_xlabel('Stanine values')
+        ax.set_ylabel('Test')
+
+        # Set the y-axis to display the tests
+        ax.set_yticks([1, 2])
+        ax.set_yticklabels(['BNT', 'FAS'])
+
+        # Set the range of the x-axis
+        ax.set_xlim([0, 10])
+
+        # Add dots for BNT and FAS scores
+        ax.scatter(BNT_stanine, 1, s=100, label='BNT')
+        ax.scatter(FAS_stanine, 2, s=100, label='FAS')
+
+        # Add legend
+        ax.legend()
+
+        # Show the plot
+        # plt.show()
+
+        # Save the graph as a png file
+        fig.savefig('test_profile.png')
+        return 'test_profile.png'
+
+
+
+def create_pdf(z_score, mean, std_dev, 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 = "Z-Score Report"
+    pdf.set_font("Arial", style="B", size=16)
+    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("Arial", size=12)
+    z_score_text = "Your z-score is: {:.2f}".format(z_score)
+    z_score_width = pdf.get_string_width(z_score_text) + 6
+    pdf.cell((210 - z_score_width) / 2)
+    pdf.cell(z_score_width, 10, z_score_text, 0, 1, "C")
+
+    # Add mean and standard deviation and center it
+    mean_std_text = "Mean: {}, Standard Deviation: {}".format(mean, std_dev)
+    mean_std_width = pdf.get_string_width(mean_std_text) + 6
+    pdf.cell((210 - mean_std_width) / 2)
+    pdf.cell(mean_std_width, 10, mean_std_text, 0, 1, "C")
+
+    # Add logo
+    pdf.image(plot_path, x=10, y=80, w=200)
+    # pdf.set_xy(10, 40)
+
+    # Add tool description and center it
+    pdf.set_xy(10, 230)
+    pdf.set_font("Arial", size=10)
+    description = "This PDF report was generated using the Z-Score Calculator Streamlit App."
+    pdf.multi_cell(0, 10, description, 0, "C")
+
+     # Add explanatory text about the collaboration between KI and KTH
+    pdf.set_xy(10, 255)
+    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, value=0, step=1)
+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, value=0, step=1)
+decoding_non_words = st.number_input("Decoding Non-Words", min_value=0, value=0, step=1)
+months_backward = st.number_input("Months Backward", min_value=0, value=0, step=1)
+pataka = st.number_input("Pataka", min_value=0, value=0, step=1)
+
+
+# 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)
+    
+    # z_score = calculate_z_score(test_score, mean, std_dev)
+    st.write(f"Your bnt z-score is: {z_bnt:.2f}")
+    st.write(f"Mean: {bnt_mean}, Standard Deviation: {bnt_std}")
+
+    st.write(f"Your fas z-score is: {z_fas:.2f}")
+    st.write(f"Mean: {fas_mean}, Standard Deviation: {fas_std}")
+    # Create PDF
+    # logo_path="logo.jpg"
+
+    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
+    education_level = 1 if education_level > 12 else 0
+
+    plot_path = generate_graph(stanine_bnt, stanine_fas)
+
+    # 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(z_bnt, bnt_mean, bnt_std, 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)