Delete app.py

app.py

@@ -1,115 +0,0 @@
-# import packages
-import streamlit as st
-import os
-from utils import load_file, plot_similarity_scores
-from constants import StreamlitException
-
-from nlp import (
-    clean_text, split_text, summarize_text,
-    extract_person_names_and_email, extract_tech_skills,
-    calculate_similarity, qna_query, lang_model
-)
-
-from constants import API_TOKEN, VESRION
-os.environ["HUGGINGFACEHUB_API_TOKEN"] = API_TOKEN
-
-def process_exception(e):
-    st.error(e.message)
-    st.stop()
-
-
-if __name__ == "__main__":
-    # Set page width to a larger size
-    st.set_page_config(layout="wide")
-
-    # Streamlit app UI
-    st.write(
-        """<h1 style='display: inline-block; color: black;'>ResuMate.IO</h1> 
-        <h3 style='display: inline-block; color: grey'>🚀 Transforming the recruitment and staffing experience through Generative AI </h3>""", 
-        unsafe_allow_html=True
-    )
-    st.write("")
-    st.write("")
-
-    st.sidebar.write("")
-    with st.sidebar.expander("🤖 About", expanded=False):
-        st.write("This app is powered by free and open-source **Langchain** and **LLM technology**.")
-        st.write("Developed by **[Chandramauli Chaudhuri](https://www.linkedin.com/in/chandramaulic/)**.")
-        st.write("")
-        st.write(f"Version **{VESRION}**.")
-
-    # Upload a file, share job description and summarize its content
-    #st.sidebar.write("")
-    st.sidebar.header("User Inputs")
-    #st.sidebar.write("")
-    job_description_raw = st.sidebar.text_area("Enter the job description:")
-    #st.sidebar.write("")
-    job_description = clean_text(job_description_raw)
-    #st.write("")
-
-    uploaded_file = st.sidebar.file_uploader("Upload a resume:", type=["docx", "pdf", "ppt", "pptx"])
-    #st.sidebar.write("")
-
-    # Spinner set-up  
-    with st.spinner("Details loading, please wait.."):
-        if uploaded_file is not None:
-            load_file_result = load_file(st, uploaded_file)
-            if type(load_file_result) is StreamlitException:
-                process_exception(load_file_result)
-            else:
-                resume_text_raw, lang_loader = load_file_result
-
-            resume_text = clean_text(resume_text_raw)
-            doc = lang_model(resume_text)
-            
-            st.subheader("📃 Overview")
-            st.write("")
-
-            # Set up candidate name & email extraction
-            person_names, emails = extract_person_names_and_email(resume_text)
-            st.write("**Candidate's name:** " + ", ".join(person_names))
-            st.write("")
-            st.write("**Candidate's email address:** " + ", ".join(emails))
-            st.write("")
-
-            # Set up job description summarization
-            summarization_result = summarize_text(job_description)
-            if type(summarization_result) is StreamlitException:
-                process_exception(summarization_result)
-            else:
-                st.write("**Job description summary:** " + summarization_result)
-            st.write("")
-
-            # Set up resume summarization
-            summarization_result = summarize_text(resume_text)
-            if type(summarization_result) is StreamlitException:
-                process_exception(summarization_result)
-            else:
-                st.write("**Candidate's resume summary:** " + summarization_result)
-            st.write("")
-            
-            st.write("")
-            st.subheader("🔍 Fitment")
-            st.write("")
-
-            # Set up technical skill extraction
-            st.write("**Candidate's key technical skills:** " + ", ".join(extract_tech_skills(doc)))
-            st.write("")
-
-            # Set up percentage match calculation
-            st.write("**Percentage match between job description and candidate's resume:** " + f"{calculate_similarity(job_description, resume_text):.2f}%" + "\n")
-            st.write("")
-            
-            # Set up percentage match calculation at sentence level
-            job_description_phrases = split_text(job_description)
-            resume_phrases = split_text(resume_text)
-            st.write('**Percentage resume match against TOP 10 job description items:**')
-            if job_description_raw != '':           
-                fig = plot_similarity_scores(job_description_phrases, resume_phrases)
-                st.plotly_chart(fig, use_container_width=True)
-
-            # Set up user Q&A
-            user_input = st.sidebar.text_input("Ask any other resume-related questions:", "")
-            if user_input:
-                answer = qna_query(lang_loader, user_input)
-                st.sidebar.write(answer)