Spaces:
Build error
Build error
File size: 2,940 Bytes
9e17994 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | import os
import streamlit as st
import google.generativeai as genai
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
api_key = os.getenv("GEMINI_API_KEY")
# Check if API key is set
if not api_key:
st.error("API key not found. Please set GEMINI_API_KEY in your .env file.")
st.stop()
# Configure the generative AI model
genai.configure(api_key=api_key)
generation_config = {
"temperature": 0.7,
"top_p": 0.9,
"top_k": 50,
"max_output_tokens": 1024,
"response_mime_type": "text/plain",
}
try:
model = genai.GenerativeModel(
model_name="gemini-1.5-flash",
generation_config=generation_config
)
except Exception as e:
st.error(f"Failed to load model: {str(e)}")
st.stop()
# Main function for Streamlit app
def main():
st.title("Personalized Course Recommendation System")
# List of questions for the user
questions = [
"Write your career interests shortly.",
"What is Your Academic Background?",
"What are your specific skills?",
"How many hours per week can you dedicate to learning?",
"Do you prefer short-term or long-term courses?",
"Do you prefer online or in-person learning?",
"What is your preferred working environment (e.g., office, remote, hybrid)?",
"What are your long-term career goals?",
"How do you prefer to learn new concepts (e.g., reading, hands-on practice, videos)?",
"What are your hobbies and interests?",
"What is your preferred learning style (e.g., visual, auditory, kinesthetic)?",
"What motivates you to learn new things?",
"What are your strengths and weaknesses?",
]
# Collect user responses
responses = {q: st.text_area(q, "") for q in questions}
# Button to get recommendations
if st.button("Get Career Path Recommendation"):
if all(responses.values()):
with st.spinner("Generating recommendations..."):
try:
# Start chat session and send the message
chat_session = model.start_chat(
history=[{"role": "user", "parts": [{"text": f"{q}: {a}"} for q, a in responses.items()]}]
)
response = chat_session.send_message("Based on the answers provided, which major Subject of Department of Computer Science & Engineering should the user can take?")
recommendation = response.text.strip()
# Display the recommendation
st.subheader("Career Path Recommendation:")
st.write(recommendation)
except Exception as e:
st.error(f"An error occurred while generating recommendations: {str(e)}")
else:
st.error("Please answer all the questions to get a recommendation.")
# Run the app
if __name__ == "__main__":
main()
|