app.py

import streamlit as st
import sys
import os
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import torch
import numpy as np
import threading
import hmac


from processing.surface_plots import surface_plot
from processing.preprocessing import plot_timeseries
from processing.prediction import NN_prediction
from processing.classification_model import classify
from processing.barsplots_rootcauses import check_classification
from processing.cloud_access import auto_download

sprint_data_folder = "Messungen"


def get_csv_files(data_folder):
    csv_files = []
    for file in os.listdir(data_folder):
        if file.endswith(".csv"):
            csv_file = os.path.join(data_folder, file)
            csv_files.append(csv_file)
    return csv_files
    
def check_password():
    """Returns `True` if the user had the correct password."""

    def password_entered():
        """Checks whether a password entered by the user is correct."""
        if hmac.compare_digest(st.session_state["password"], os.getenv("Password")):
            st.session_state["password_correct"] = True
            del st.session_state["password"]  # Don't store the password.
        else:
            st.session_state["password_correct"] = False

    # Return True if the password is validated.
    if st.session_state.get("password_correct", False):
        return True

    # Show input for password.
    st.text_input(
        "Password", type="password", on_change=password_entered, key="password"
    )
    if "password_correct" in st.session_state:
        st.error("😕 Password incorrect")
    return False

if not check_password():
    st.stop()  # Do not continue if check_password is not True.


def main():

    sprint_csv_files = get_csv_files(sprint_data_folder)

    st.set_page_config(page_title="Assistenzsystem", page_icon=":eyeglasses:", layout="wide")
    with open("assets/style.css") as f:
        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
    
    st.write("")
    col1, col2 = st.columns([0.5, 1])
    with col1: st.image("assets/ProKI_Logo.png", width=200)
    with col2: st.markdown("<h2 style='text-align: left; color: black; font-family:Arial;font-size:2.25rem;'>Assistenzsystem</h2>",  unsafe_allow_html=True)


    tab1, tab2 = st.tabs(["Modellierung der Bauteilqualität", "Klassifizierungsergebnisse"])

    with tab1:
        
        st.markdown("#")        
        col1, col2, col3 = st.columns([0.5, 0.1, 0.4], gap="large")
        
        with col1:
            
            feed = st.slider("Vorschub [mm/min]", 400, 1300, 850)
            plaindepth = st.slider("Schnitttiefe [mm]", 0.1, 0.5, 0.3)
            wear = st.slider("Werkzeugverschleiß %", 0, 100, 50)

            try:
                fig = NN_prediction((feed-400)/900, (plaindepth-0.1)/0.4, wear/100)
                st.pyplot(fig)
            except:
                st.write("Models not loaded yet")

        with col2:
            st.write("")

        with col3:
            st.write("")

            st.image("assets/slice.png", width=400)

            auswahl = st.radio("Merkmal", ["Vorschub", "Schnitttiefe", "Werkzeugverschleiß"], horizontal=True, index=0)
            width = 500
        

            if auswahl == "Vorschub":
                st.image("assets/max_Vorschub.PNG", width=width)
            elif auswahl == "Schnitttiefe":
                st.image("assets/max_Schnitttiefe.PNG", width=width)
            else:
                st.image("assets/max_Verschleiß.PNG", width=width)

    with tab2:

        selected_file = st.selectbox("Oberflächenscan auswählen", sprint_csv_files, 0)
        col1, col_blanc, col2= st.columns([0.45, 0.1, 0.45], gap="large")

        with col1:
            try:
                fig = surface_plot(selected_file)
                fig.update_layout(height=800, width=1200)
                st.plotly_chart(fig, theme="streamlit", use_container_width=False)
            except:
                pass
        
        with col_blanc:
            st.write("")
        
        with col2:
            st.markdown("<h2 style='text-align: center; color: black; font-family:Arial;font-size:2rem;'>Klassifizierung</h2>", unsafe_allow_html=True)
            st.markdown("#")
            
            dummy, tolerance, z_data, fivetothirty = plot_timeseries(selected_file, 9)
            data = fivetothirty["z"].values
            mean = np.mean(data)
            data = data - mean
            
            try:
                data = torch.tensor(data, dtype=torch.float).to("cpu")
                data = data.view(1,1,500)
                prediction = classify(data)

                fig_feed, fig_depth, fig_condition = check_classification(prediction[0])
                st.pyplot(fig_feed)
                st.pyplot(fig_depth)
                st.pyplot(fig_condition) 
            except:
                st.write("Models not loaded yet")



if __name__ == "__main__":
    main()
    
    t1 = threading.Thread(target=auto_download(os.getenv("seafile_token")))
    t1.start()