Create HR Resume Screening Assistance.py

pages/HR Resume Screening Assistance.py

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+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="💁",
+    layout="wide",
+    initial_sidebar_state="expanded",
+    menu_items={
+        'Get Help': 'https://www.extremelycoolapp.com/help',
+        'Report a bug': "https://www.extremelycoolapp.com/bug",
+        'About': "# This is a header. This is an *extremely* cool app!"
+        }
+    )
+    
+    st.title("HR - Resume Screening Assistance 💁 ")
+    st.subheader("I can help you in resume screening process")
+
+    #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()