Create app.py

app.py

@@ -0,0 +1,150 @@
+import streamlit as st
+from dotenv import load_dotenv
+from utils import *
+import uuid
+
+#Creating session variables
+if 'unique_id' not in st.session_state:
+    st.session_state['unique_id'] =''
+
+
+
+
+
+# if 'index' not in st.session_state:
+#     st.session_state['index'] = 0
+
+# def next_index(): 
+    
+#         st.session_state['index'] += 1
+
+# def prev_index():
+#     if  st.session_state['index'] > 1 :
+#         st.session_state['index'] -= 1
+#     else: 
+#         pass
+
+def main():
+    load_dotenv()
+
+    custom_css = """
+    <style>
+    body {
+    background-color: #29293d; /* Set your desired background color here */
+    }
+    </style>
+    """
+
+    st.set_page_config(page_title="Resume Matcher - Dashboard")
+    st.title("Resume Matcher")
+    st.markdown(custom_css, unsafe_allow_html=True)  
+
+
+
+    
+    job_description = st.text_area("Specify the job description to match against :",key="1")
+    document_count = st.text_input("Number of resumes to return: ",key="2")
+
+    # option2 = st.selectbox("Choose an option:", ["Upload", "Continue Without Uploading"])
+    option = st.radio("Do you want to upload new resumes with this request ? :", ["Yes", "No" ]) 
+
+    # Display content based on the selected option
+    if option == "Yes":
+        st.header("Upload Section")
+        resume = st.file_uploader("Upload resumes here, (docx pdf md files allowed) ", type=["pdf", "docx", "md"],accept_multiple_files=True)
+        if resume :
+            st.success("File uploaded successfully!")
+            # Process the uploaded file if needed
+
+    # st.subheader("I can help you in resume screening process")
+
+
+    submit=st.button("Get Resumes")
+
+    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']= "aaa365fe031e4b5ab90aba54eaf6012e"
+
+            #Create a documents list out of all the user uploaded pdf files
+
+            #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()
+
+            if option == "Yes":
+                        #Push data to PINECONE
+                    final_docs_list=create_docs(resume ,st.session_state['unique_id'])    
+                    push_to_pinecone("ad12a7c3-b36f-4b48-986e-5157cca233ef","gcp-starter","resume-db",embeddings,final_docs_list) 
+
+            #Fecth relavant documents from PINECONE
+            relevant_docs = similar_docs(job_description,document_count,"ad12a7c3-b36f-4b48-986e-5157cca233ef","gcp-starter","resume-db",embeddings,st.session_state['unique_id'])
+
+            # score, relavant_docs = similar_docs_hf(query= job_description , final_docs_list=final_docs_list, k = document_count ) 
+            #t.write(relavant_docs)
+
+            #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(relevant_docs)): 
+                
+
+            # st.button('Next ', on_click=next_index )
+            
+            # st.button('Previous' , on_click= prev_index )
+
+            # st.write(st.session_state['index'])
+
+            #Displaying Filepath
+        
+            # for item in range(len(relavant_docs)):
+
+            st.subheader("🔍 "+str("Following are best matching resumes of job description"))
+                
+            #Displaying File Name 
+            names =  metadata_filename(relevant_docs )
+            scores = get_score(relevant_docs)
+            content = docs_content(relevant_docs)
+
+            for i, name in enumerate(names):
+
+                st.subheader("👉 "+str(i+1))
+                st.write("**File** : "+str(name[0]) )
+                with st.expander('Show me 👀'): 
+                    
+                    st.info("**Match Score** : "+str(scores[i]))
+                    
+                    # st.write("***", content[i] )  
+
+                    st.write("**Summary**", get_summary(relevant_docs[i][0]))  
+
+            # st.write("**File** : "+relavant_docs[item][0].metadata['name'])
+            
+
+            #Introducing Expander feature
+            
+            # with st.expander('Show me 👀'): 
+            #     scores = get_score(relevant_docs)
+            #     st.info("**Match Score** : "+str(scores))
+            #     content = docs_content(relevant_docs)
+            #     st.write("***",content)  
+            
+                #Gets the summary of the current item using 'get_summary' function that we have created which uses LLM & Langchain chain
+                
+                # st.write("**Summary** : ",summary)
+
+        # st.success("Hope I was able to save your time❤️") 
+
+
+# #Invoking main function
+if __name__ == '__main__':
+    main()
+
+