Create project_recommender/app.py

project_recommender/app.py

@@ -0,0 +1,93 @@
+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
+                           to users based on their domain, area of interest, experience level,
+                           years of experience, tech stack, and prior projects.
+                           """,
+            },
+            {
+                "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)