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import gradio as gr |
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from scraper import scrape_courses_json |
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from text_processing import generate_text |
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from embedding_storage import process_safety_with_chroma |
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from qa_chatbot import create_chatbot, ask_question |
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from config import BASE_URL |
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def main(query): |
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""" |
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Main function to scrape courses, process embeddings, and retrieve answers. |
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Args: |
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query (str): User's query for course recommendation. |
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Returns: |
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str: Response from the chatbot with a recommended course. |
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""" |
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courses_data = scrape_courses_json(BASE_URL, num_pages=1) |
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course_text = generate_text(courses_data) |
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vector_store = process_safety_with_chroma(course_text) |
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qa_system = create_chatbot(vector_store) |
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prompt = "Suggest me the best course for " + query + " in a structured format with a link." |
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return ask_question(qa_system, prompt) |
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with gr.Blocks(css=""" |
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.container {max-width: 800px; margin: auto; text-align: center;} |
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button {background-color: orange !important; color: white !important;} |
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#input_text, #output_text {margin-bottom: 20px;} |
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""") as demo: |
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gr.Markdown("# 🎓 Course Recommendation Chatbot\nWelcome! Enter your area of interest to receive a course recommendation.") |
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input_text = gr.Textbox(label="Ask your question about courses", placeholder="e.g., Best courses for machine learning", elem_id="input_text") |
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output_text = gr.Textbox(label="Course Information", placeholder="Your course recommendation will appear here...", elem_id="output_text") |
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submit_button = gr.Button("Get Recommendation", elem_id="submit_button") |
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submit_button.click(fn=main, inputs=input_text, outputs=output_text) |
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demo.launch(share=True) |
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