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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 suggest_projects(skills, roles, topics, level, difficulty,model, domain, industry): | |
| prompt = f""" | |
| Based on the following information, suggest relevant projects: | |
| - Skills: {skills} | |
| - Roles: {roles} | |
| - Topics: {topics} | |
| - Level of Understanding: {level} | |
| - Difficulty: {difficulty} | |
| """ | |
| if domain: | |
| prompt += f"\n- Domain: {domain}" | |
| if industry: | |
| prompt += f"\n- Industry: {industry}" | |
| prompt += "\n\nSuggested Projects:" | |
| if model == "Open AI": | |
| llm = OpenAI(temperature=0.7, openai_api_key=st.secrets["OPENAI_API_KEY"]) | |
| projects = llm(prompt) | |
| elif model == "Gemini": | |
| llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=st.secrets["GOOGLE_API_KEY"]) | |
| projects = llm.invoke(prompt) | |
| projects = projects.content | |
| return projects | |
| def app(): | |
| st.title("Project Suggestions") | |
| if 'analysis' in st.session_state: | |
| analysis = st.session_state['analysis'] | |
| st.header("Select AI:") | |
| model = st.radio("Model", [ "Gemini","Open AI",]) | |
| st.write("Selected option:", model) | |
| st.write("Job Description Analysis:") | |
| st.text(analysis) | |
| # Parse the analysis (assuming it returns JSON-like structure) | |
| analysis_data = eval(analysis) # Convert string to dictionary | |
| # Extract the details from analysis | |
| skills = analysis_data.get("Skills", "") | |
| roles = analysis_data.get("Roles", "") | |
| topics = analysis_data.get("Topics", "") | |
| level = analysis_data.get("Level of Understanding", "") | |
| difficulty = analysis_data.get("Difficulty", "") | |
| domain = analysis_data.get("domain", "") | |
| industry = analysis_data.get("industry", "") | |
| # Optional fields for domain and industry | |
| # domain = st.text_input("Optional: Domain") | |
| # industry = st.text_input("Optional: Industry") | |
| # Suggest projects based on analysis | |
| if st.button("Suggest Projects"): | |
| projects = suggest_projects(skills, roles, topics, level, difficulty,model, domain, industry) | |
| st.write("Suggested Projects:") | |
| st.text(projects) | |
| st.session_state['projects'] = projects.split('\n') | |
| else: | |
| st.error("Please go to the Job Description Analysis page first to analyze a job description.") | |