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| import streamlit as st | |
| from groq import Groq | |
| from github_analytics.chat_github import analyze_github_data | |
| import os | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| def predict(domain, content): | |
| client = Groq(api_key=os.getenv("GROQ_API_KEY_PR")) | |
| completion = client.chat.completions.create( | |
| model="llama3-70b-8192", | |
| messages=[ | |
| { | |
| "role": "system", | |
| "content": f"""You are a Mentor proficient in the {domain} that provides project recommendations | |
| in a rabular format to users based on their domain, area of interest, experience level, | |
| years of experience, tech stack, and prior projects. | |
| Strictly give the output in a tabular format | |
| """, | |
| }, | |
| { | |
| "role": "user", | |
| "content": content, | |
| } | |
| ], | |
| max_tokens=2048, | |
| ) | |
| response = completion.choices[0].message.content | |
| return response | |
| def project_recommendation(): | |
| st.title("Project Recommender Chatbot") | |
| domain = st.text_input("Enter your domain:") | |
| area_of_interest = st.text_input("Enter your area of interest:") | |
| experience_level = st.selectbox("Select your current role", ['Student', 'Working professional', 'Freelancer']) | |
| if experience_level in ["Working professional", "Freelancer"]: | |
| years_of_experience = st.number_input("Enter your years of experience:") | |
| else: | |
| years_of_experience = 0 | |
| skill_level = st.selectbox("Select your skill level", ['Beginner', 'Intermediate', 'Advanced']) | |
| tech_stack = st.text_input("Enter your technology stack/ list of tools you use for your project:") | |
| prior_projects = st.text_input("If you have any prior projects, please enter:") | |
| hours_in_hand = st.number_input("How many hours do you have for one project?") | |
| content = f"""This is my information: | |
| Domain: {domain} | |
| Area of Interest: {area_of_interest} | |
| Experience Level: {experience_level} | |
| Years of Experience: {years_of_experience} | |
| Skill level : {skill_level} | |
| Tech Stack: {tech_stack} | |
| Prior Projects: {prior_projects} | |
| Hours in Hand: {hours_in_hand} | |
| Based on this information, please provide a list of 5 project recommendations | |
| that would be suitable for me. | |
| Consider my domain, area of interest, years of experience, hours in hand, experience level, tech stack, | |
| skill level, and any prior projects I have worked on. | |
| Tailor the recommendations to my specific interests and skill level. | |
| You have an explicit instruction to follow the hours in hand criteria and | |
| not overestimate/underestimate the time | |
| For each project recommendation, provide the following details in a tabular format: | |
| 1. Project Title | |
| 2. Brief Project Description | |
| 3. Relevant Technologies/Skills Required | |
| 4. Steps to achieve the recommendation | |
| If it is a technology related domain, in 4th point above, | |
| please mention the step by step modules/files/subprojects that one can make and progress in a project | |
| Your response should be structured in a clear and concise manner, do not use </br> anywhere | |
| with each project recommendation presented as a separate section or bullet point.""" | |
| if st.button("Send"): | |
| answer = predict(domain, content) | |
| st.write("Result:", answer) | |
| st.write("If you are from a technical field and you want to analyze your github, press this button") | |
| username = st.text_input("Enter your Github Username: ") | |
| if username: | |
| # user_data, repo_data = fetch_user_data(username) | |
| output_of_analysis = analyze_github_data(username, domain, area_of_interest, experience_level, | |
| years_of_experience, skill_level, tech_stack, prior_projects, | |
| hours_in_hand) | |
| st.write("Result:", output_of_analysis) | |