app.py

"""
app.py

Main script to run the Course Recommendation Chatbot using Gradio.
"""

import os
import gradio as gr
from document_loader import load_document
from embeddings import process_documents_with_chroma
from chatbot import create_chatbot, ask_question

# Ensure your OpenAI API key is set in the environment variables.
api_key = os.getenv("OPENAI_API_KEY")
os.environ["OPENAI_API_KEY"] = api_key

def main(query):
    """Main function to load document, create embeddings, and generate a response.

    Args:
        query (str): User input query for course recommendation.

    Returns:
        str: The chatbot's response.
    """
    file_path = "Courses_Details.pdf"
    documents = load_document(file_path)
    vector_store = process_documents_with_chroma(documents)
    chatbot_system = create_chatbot(vector_store)

    prompt = f"Suggest me best course for {query} as an output in a well-written, elaborative, and structured format along with its link."
    return ask_question(chatbot_system, prompt)

# Define the Gradio interface
with gr.Blocks(css="""
.container {max-width: 800px; margin: auto; text-align: center;}
button {background-color: orange !important; color: white !important;}
#input_text, #output_text {margin-bottom: 20px;}
""") as demo:
    gr.Markdown("""
        # 🎓 Course Recommendation Chatbot
        Welcome to the **Course Recommendation Chatbot**! This assistant can suggest the best courses based on your input, along with a well-structured description and course link.
        
        Just enter the area you’re interested in (e.g., "machine learning") to receive a curated course recommendation!
    """)
    
    with gr.Group():
        input_text = gr.Textbox(label="Ask your question about courses", placeholder="e.g., Best courses for machine learning", elem_id="input_text")
        output_text = gr.Textbox(label="Course Information", placeholder="Your course recommendation will appear here...", elem_id="output_text")
        
        submit_button = gr.Button("Get Recommendation", elem_id="submit_button")

    submit_button.click(fn=main, inputs=input_text, outputs=output_text)

demo.launch(share=True)