Create app.py

app.py

@@ -0,0 +1,115 @@
+# 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)