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| import streamlit as st | |
| import os | |
| from dotenv import load_dotenv | |
| from langchain_community.llms import OpenAI | |
| from langchain_google_genai import ChatGoogleGenerativeAI | |
| # Load environment variables | |
| load_dotenv() | |
| def analyze_job_description(job_description,model): | |
| prompt = f""" | |
| Analyze the following job description and extract the following information: | |
| - Skills | |
| - Roles | |
| - Topics | |
| - Level of Understanding | |
| - Difficulty | |
| - Domain of company | |
| - Industry of company | |
| Job Description: | |
| {job_description} | |
| .provide the answer in json format | |
| """ | |
| if model == "Open AI": | |
| llm = OpenAI(temperature=0.7, openai_api_key=st.secrets["OPENAI_API_KEY"]) | |
| analysis = llm(prompt) | |
| elif model == "Gemini": | |
| llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=st.secrets["GOOGLE_API_KEY"]) | |
| analysis = llm.invoke(prompt) | |
| analysis = analysis.content | |
| return analysis | |
| def app(): | |
| st.title("Job Description Analysis") | |
| st.header("Select AI:") | |
| model = st.radio("Model", [ "Gemini","Open AI",]) | |
| st.write("Selected option:", model) | |
| # Input: Job description | |
| job_description = st.text_area("Enter Job Description:") | |
| # Analyze button | |
| if st.button("Analyze"): | |
| if job_description: | |
| # Use the model to analyze the job description | |
| analysis = analyze_job_description(job_description,model) | |
| st.write("Analysis:") | |
| st.text(analysis) | |
| # Store analysis in session state for use in Project Suggestions page | |
| st.session_state['analysis'] = analysis | |
| model = model | |
| else: | |
| st.error("Please enter a job description.") | |