Upload app.py

app.py

@@ -0,0 +1,59 @@
+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)