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| import os | |
| import streamlit as st | |
| from groq import Groq | |
| # Set up the Groq API Key | |
| # Initialize the Groq client | |
| client = Groq(api_key=os.environ["GROQ_API_KEY"]) | |
| # Streamlit user input | |
| st.title("MangoTutor") | |
| st.write("I am Dr.Mango, ask me anything. Have lots of fun by studying a lot. ") | |
| # User input for study details | |
| study_topic = st.text_input("What is your study topic or exam?") | |
| prep_days = st.number_input("How many days do you have to prepare?", min_value=1) | |
| hours_per_day = st.number_input("How many hours can you dedicate per day?", min_value=1) | |
| # Function to generate chatbot response based on user input | |
| def generate_study_plan(topic, days, hours): | |
| prompt = ( | |
| f"I am a study assistant chatbot helping a user prepare for {topic} over {days} days " | |
| f"with {hours} hours per day. Please provide a personalized study plan, tips for effective " | |
| "study habits, and suggest specific resources for each session." | |
| ) | |
| # Generate response using Groq API | |
| chat_completion = client.chat.completions.create( | |
| messages=[{"role": "user", "content": prompt}], | |
| model="llama3-8b-8192", | |
| ) | |
| response = chat_completion.choices[0].message.content | |
| return response | |
| # Display study plan when user submits details | |
| if study_topic and prep_days and hours_per_day: | |
| study_plan = generate_study_plan(study_topic, prep_days, hours_per_day) | |
| st.write("### Your Study Plan") | |
| st.write(study_plan) | |
| else: | |
| st.write("Please enter your study topic, preparation days, and available hours per day to receive a study plan.") |