update

app.py

@@ -1,117 +1,124 @@
 import os
 import streamlit as st
-from features import (ats, 
-                      analyzer, 
-                      company_recommend, 
-                      cover_letter, 
-                      enhance, 
-                      improve, 
-                      interview,
-                      linkedin,
-                      newresume,
-                      recommend, 
-                      review)
+from features import (
+    ats, analyzer, company_recommend, cover_letter, enhance, improve,
+    interview, linkedin, newresume, recommend, review
+)
 from components import docLoader
 from dotenv import load_dotenv
 import google.generativeai as genai
 from langchain_google_genai import ChatGoogleGenerativeAI
+import asyncio
 
 # Load environment variables
 load_dotenv()
 
-# Initialize CareerEnchanter
-class CareerEnchanter(object):
-    def __init__(self, title="CareerEnchanter"):
+class CareerNavigator:
+    def __init__(self, title="Career Navigator"):
         self.title = title
 
     @staticmethod
     def model():
         genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+        if not asyncio.get_event_loop().is_running():
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
         return ChatGoogleGenerativeAI(model="gemini-pro")
 
-# Initialize CareerEnchanter instance
-enchanter = CareerEnchanter()
+# Initialize CareerNavigator instance
+navigator = CareerNavigator()
 
 # Set Streamlit page configuration
-st.set_page_config(page_title=enchanter.title, page_icon='🤖', layout='wide')
+st.set_page_config(page_title=navigator.title, page_icon='🧑‍💼', layout='wide')
 
 # Main title
-st.title("🚀 Career Enchanter 🚀")
+st.title("Welcome to Career Navigator")
 
-# Load document
+# Load and display document
 text = docLoader.load_doc()
 st.session_state['doc_text'] = text
 
-jd, doc = st.columns(2)
-with jd:
-    # Job Description input
-    jd = st.text_area("Job Description: ", key="input")
-if text: 
-    with doc:
-        extracted= st.text_area("Extracted Data From Resume", value=st.session_state['doc_text'])
+jd_col, doc_col = st.columns(2)
+with jd_col:
+    jd = st.text_area("Enter Job Description:", key="input")
+if text:
+    with doc_col:
+        st.text_area("Extracted Data From Resume:", value=st.session_state['doc_text'], height=300)
 
-role=st.text_input("Role you want to Apply for")
+role = st.text_input("Desired Role:", placeholder="e.g., Software Engineer")
 st.session_state['role'] = role
 
 # Sidebar options
 with st.sidebar:
-    st.title('🔮 Career Enchanter 🔮')
-    st.subheader('Options: ')
-    option = st.radio("Select an option: ", (
-        "ATS Score", 
-        "Resume Review", 
-        "Resume Enhancements", 
-        "Resume Improvements", 
-        "Recommendation", 
-        "Keywords", 
-        "Generate Cover Letter", 
-        "Resume Generator", 
-        "Linkedin Profile Update", 
-        "Possible Interview Questions", 
-        "Company Recommendations"
-        ))
+    st.title('Career Navigator Menu')
+    st.subheader('Choose an Option:')
+    option = st.radio(
+        "Navigate to:",
+        (
+            "Calculate ATS Score", "Review Resume", "Enhance Resume",
+            "Improve Resume", "Get Recommendations", "Analyze Keywords",
+            "Generate Cover Letter", "Generate Resume",
+            "Update LinkedIn Profile", "Prepare for Interview",
+            "Company Recommendations"
+        )
+    )
 
     # Load model
-    with st.spinner("Loading Model..."):
-        llm = enchanter.model()
-if option == "ATS Score":
-            calculation_method = st.radio("Choose how you want to calculate ATS Score: ", ("Using AI", "Manually (Cosine Similarity)"), horizontal=True)
+    with st.spinner("Initializing Model..."):
+        llm = navigator.model()
 
-elif option == "Recommendation":
-            recommendation_type = st.radio("Select the type of recommendation you want: ", ("Entire Resume", "Section Wise"), horizontal=True)
+# Option-specific configurations
+if option == "Calculate ATS Score":
+    calculation_method = st.radio(
+        "Select ATS Score Calculation Method:",
+        ("Using AI", "Manually (Cosine Similarity)"),
+        horizontal=True
+    )
+
+elif option == "Get Recommendations":
+    recommendation_type = st.radio(
+        "Select Recommendation Type:",
+        ("Entire Resume", "Section Wise"),
+        horizontal=True
+    )
+
+elif option == "Analyze Keywords":
+    analyz_type = st.radio(
+        "Select Keywords Function:",
+        ("Analyze Keywords", "Keyword Synonyms"),
+        horizontal=True
+    )
 
-elif option == "Keywords":
-            analyz_type = st.radio("Select the type of Keywords Fucntion you want: ", ("Analyse Keywords", "Keyword Synonyms"), horizontal=True)
 # Dictionary mapping options to functions
 option_functions = {
-    "ATS Score": ats.run_ats,
-    "Resume Review": review.run_review,
-    "Resume Enhancements": enhance.run_enhance,
-    "Resume Improvements": improve.run_improve,
-    "Recommendation": recommend.run_recommend,
-    "Keywords": analyzer.run_analyzer,
+    "Calculate ATS Score": ats.run_ats,
+    "Review Resume": review.run_review,
+    "Enhance Resume": enhance.run_enhance,
+    "Improve Resume": improve.run_improve,
+    "Get Recommendations": recommend.run_recommend,
+    "Analyze Keywords": analyzer.run_analyzer,
     "Generate Cover Letter": cover_letter.run_letter,
-    "Resume Generator": newresume.run_newresume,
-    "Linkedin Profile Update": linkedin.run_linkedin,
-    "Possible Interview Questions": interview.run_interview,
+    "Generate Resume": newresume.run_newresume,
+    "Update LinkedIn Profile": linkedin.run_linkedin,
+    "Prepare for Interview": interview.run_interview,
     "Company Recommendations": company_recommend.run_company
 }
 
 # Handle the selected option
 if option in option_functions:
     func = option_functions[option]
-    if option == "ATS Score":
+    if option == "Calculate ATS Score":
         if calculation_method == "Manually (Cosine Similarity)":
             func(llm, st.session_state['doc_text'], jd, manual=True)
         else:
             func(llm, st.session_state['doc_text'], jd)
-    elif option == "Recommendation":
+    elif option == "Get Recommendations":
         if recommendation_type == "Entire Resume":
             func(llm, st.session_state['doc_text'], jd, section=True)
         else:
             func(llm, st.session_state['doc_text'], jd)
-    elif option == "Keywords":
-        if analyz_type == "Analyse Keywords":
+    elif option == "Analyze Keywords":
+        if analyz_type == "Analyze Keywords":
             func(llm, st.session_state['doc_text'], jd, analysis=True)
         else:
             func(llm, st.session_state['doc_text'], jd)