Create app.py

app.py

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+import gradio as gr
+from scraper import scrape_courses_json
+from text_processing import generate_text
+from embedding_storage import process_safety_with_chroma
+from qa_chatbot import create_chatbot, ask_question
+from config import BASE_URL
+
+def main(query):
+    """
+    Main function to scrape courses, process embeddings, and retrieve answers.
+
+    Args:
+        query (str): User's query for course recommendation.
+
+    Returns:
+        str: Response from the chatbot with a recommended course.
+    """
+    courses_data = scrape_courses_json(BASE_URL, num_pages=1)
+    course_text = generate_text(courses_data)
+    vector_store = process_safety_with_chroma(course_text)
+    qa_system = create_chatbot(vector_store)
+    
+    prompt = "Suggest me the best course for " + query + " in a structured format with a link."
+    return ask_question(qa_system, prompt)
+
+# 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\nWelcome! Enter your area of interest to receive a course recommendation.")
+
+    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)