Update app.py

app.py

@@ -1,15 +1,9 @@
 import streamlit as st
 from utils import *
 import uuid
-#https://streamlit-emoji-shortcodes-streamlit-app-gwckff.streamlit.app/
-#Creating session variables
-if 'unique_id' not in st.session_state:
-    st.session_state['unique_id'] =''
 
 def main():
 
-    #st.set_page_config(page_title="Resume Screening BOT")
-
     st.set_page_config(
     page_title="Resume Screening Assistance",
     page_icon="💁",
@@ -22,74 +16,13 @@ def main():
         }
     )
     
-    st.title("HR - Resume Screening Assistance 💁 ")
-    st.subheader("I can help you in resume screening process")
+    st.title("Smart Apps 💁 ")
+    st.subheader("Your Personal Smart Assistant")
+    st.text('These are a set of GenAI apps designed to help human intensive tasks easier')
 
     #st.sidebar.title("😎")
     st.sidebar.image('./resume_screening.jpg',width=300, use_column_width=True)
 
-    # Applying Styling
-    st.markdown("""
-    <style>
-    div.stButton > button:first-child {
-        background-color: #0099ff;
-        color:#ffffff;
-    }
-    div.stButton > button:hover {
-        background-color: #00ff00;
-        color:#FFFFFF;
-        }
-    </style>""", unsafe_allow_html=True)
-
-    job_description = st.text_area("Please paste the 'JOB DESCRIPTION' here...📝",key="1")
-    document_count = st.text_input("No.of 'RESUMES' to return",key="2")
-    # Upload the Resumes (pdf files)
-    pdf = st.file_uploader("Upload resumes here, only PDF files allowed", type=["pdf"],accept_multiple_files=True)
-
-    submit=st.button("Help me with the analysis")
-
-    if submit:
-        with st.spinner('Wait for it...'):
-
-            #Creating a unique ID, so that we can use to query and get only the user uploaded documents from PINECONE vector store
-            st.session_state['unique_id']=uuid.uuid4().hex
-
-            #Create a documents list out of all the user uploaded pdf files
-            final_docs_list=create_docs(pdf,st.session_state['unique_id'])
-            #st.write(final_docs_list)
-            
-            #Displaying the count of resumes that have been uploaded
-            st.write("*Resumes uploaded* :"+str(len(final_docs_list)))
-
-            #Create embeddings instance
-            embeddings=create_embeddings_load_data()
-
-            #Fecth relavant documents from Vectorspace
-            relavant_docs=close_matches(job_description,document_count,final_docs_list,embeddings)
-
-            #Introducing a line separator
-            st.write(":heavy_minus_sign:" * 30)
-
-            #For each item in relavant docs - we are displaying some info of it on the UI
-            for item in range(len(relavant_docs)):
-                
-                st.subheader("👉 "+str(item+1))
-
-                #Displaying Filepath
-                st.write("**File** : "+relavant_docs[item][0].metadata['name'])
-
-                #Introducing Expander feature
-                with st.expander('Show me 👀'): 
-                    st.info("**Match Score** : "+ str(1 - relavant_docs[item][1]))
-                    #st.write("***"+relavant_docs[item][0].page_content)
-                    
-                    #Gets the summary of the current item using 'get_summary' function that we have created which uses LLM & Langchain chain
-                    summary = get_summary(relavant_docs[item][0])
-                    st.write("**Summary** : "+summary)
-
-        st.success("Hope I was able to save your time❤️")
-
-
 #Invoking main function
 if __name__ == '__main__':
     main()