Update app.py

app.py

@@ -158,8 +158,7 @@ def main():
     with st.sidebar:
         st.image(side_img, width=300)
     st.sidebar.subheader("Menu")
-    website_menu = st.sidebar.selectbox("Menu", ("Emotion Recognition", "Project description", "Our team",
-                                                 "Leave feedback", "Relax"))
+    website_menu = st.sidebar.selectbox("Menu", ("Emotion Recognition", "Project description", "Relax"))
     st.set_option('deprecation.showfileUploaderEncoding', False)
 
     if website_menu == "Emotion Recognition":
@@ -417,8 +416,7 @@ def main():
         st.markdown(txt, unsafe_allow_html=True)
 
         st.subheader("Theory")
-        link = '[Theory behind - Medium article]' \
-               '(https://talbaram3192.medium.com/classifying-emotions-using-audio-recordings-and-python-434e748a95eb)'
+        link = '[Theory behind - ]'
         st.markdown(link + ":clap::clap::clap: Tal!", unsafe_allow_html=True)
         with st.expander("See Wikipedia definition"):
             components.iframe("https://en.wikipedia.org/wiki/Emotion_recognition",
@@ -426,13 +424,15 @@ def main():
 
         st.subheader("Dataset")
         txt = """
-            This web-application is a part of the final **Data Mining** project for **ITC Fellow Program 2020**. 
+            This machine learning web-application PROJECT is a partial fulfillment of requirement of Higher  National Diploma (HND) computer science **The Federal College of Animal Health and Production Technology**  **FCAHPTIB, 2023**. 
 
             Datasets used in this project
             * Crowd-sourced Emotional Mutimodal Actors Dataset (**Crema-D**)
             * Ryerson Audio-Visual Database of Emotional Speech and Song (**Ravdess**)
             * Surrey Audio-Visual Expressed Emotion (**Savee**)
-            * Toronto emotional speech set (**Tess**)    
+            * Toronto emotional speech set (**Tess**) 
+
+            The above datasets was used in the model training of this software before deployment
             """
         st.markdown(txt, unsafe_allow_html=True)
 
@@ -440,52 +440,6 @@ def main():
         fig = px.violin(df, y="source", x="emotion4", color="actors", box=True, points="all", hover_data=df.columns)
         st.plotly_chart(fig, use_container_width=True)
 
-        st.subheader("FYI")
-        st.write("Since we are currently using a free tier instance of AWS, "
-                 "we disabled mel-spec and ensemble models.\n\n"
-                 "If you want to try them we recommend to clone our GitHub repo")
-        st.code("git clone https://github.com/CyberMaryVer/speech-emotion-webapp.git", language='bash')
-
-        st.write("After that, just uncomment the relevant sections in the app.py file "
-                 "to use these models:")
-
-    elif website_menu == "Our team":
-        st.subheader("Our team")
-        st.balloons()
-        col1, col2 = st.columns([3, 2])
-        with col1:
-            st.info("[email protected]")
-            st.info("[email protected]")
-            st.info("[email protected]")
-        with col2:
-            liimg = Image.open("images/LI-Logo.png")
-            st.image(liimg)
-            st.markdown(f""":speech_balloon: [Maria Startseva](https://www.linkedin.com/in/maria-startseva)""",
-                        unsafe_allow_html=True)
-            st.markdown(f""":speech_balloon: [Tal Baram](https://www.linkedin.com/in/tal-baram-b00b66180)""",
-                        unsafe_allow_html=True)
-            st.markdown(f""":speech_balloon: [Asher Holder](https://www.linkedin.com/in/asher-holder-526a05173)""",
-                        unsafe_allow_html=True)
-
-    elif website_menu == "Leave feedback":
-        st.subheader("Leave feedback")
-        user_input = st.text_area("Your feedback is greatly appreciated")
-        user_name = st.selectbox("Choose your personality", ["checker1", "checker2", "checker3", "checker4"])
-
-        if st.button("Submit"):
-            st.success(f"Message\n\"\"\"{user_input}\"\"\"\nwas sent")
-
-            if user_input == "log123456" and user_name == "checker4":
-                with open("log0.txt", "r", encoding="utf8") as f:
-                    st.text(f.read())
-            elif user_input == "feedback123456" and user_name == "checker4":
-                with open("log.txt", "r", encoding="utf8") as f:
-                    st.text(f.read())
-            else:
-                log_file(user_name + " " + user_input)
-                thankimg = Image.open("images/sticky.png")
-                st.image(thankimg)
-
     else:
         import requests
         import json