app.py

import joblib
import streamlit as st
import os
import time 
import pandas as pd
from PIL import Image

st.header("Emotional Predictive Model")
@st.cache_data
def main_sheet(n):
    sheet=pd.read_excel(n)
    return sheet
sheets=main_sheet('describe.xlsx')

@st.cache_data
def main_test(n):
    test=pd.read_excel(n)
    return test
    
test=main_test('test.xlsx')

@st.cache_resource
def load_model(n):
    model=joblib.load(n)
    return model

model=load_model("EEGP.json")
good=[]
bad=[]
natural=[]
b=st.button("strat the test")
if b:
    for i in range(1,30): 
        s=sheets.query(f"image=={i}") 
        chatbot_tiny_logo = Image.open(f"E:/samira/eeg/image/{i}.jpg")
        st.image(chatbot_tiny_logo, caption=s["description"].values[0])
        d=test.sample(1)
        X=d.iloc[:,:-1]
        st.write(X)
        
        emotion=model.predict(X)
        st.write(emotion)
        st.success("signal was recieved and analysed")
        time.sleep(2)
        if emotion=='POSITIVE':
            good.append(s["description"].values[0])
        elif emotion=='NEGATIVE':
            bad.append(s["description"].values[0])  
        else:
            natural.append(s["description"].values[0])

else:
    st.write("true the toggle")
    
    
        
st.header("Design guidline report")  
new_data=pd.DataFrame(good)  
st.write(good)