from transformers import pipeline import gradio as gr # Import Gradio for the interface """ For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference """ # Load a text-generation model chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium") # Customize the bot's knowledge base with predefined responses faq_responses = { "study tips": "Here are some study tips: 1) Break your study sessions into 25-minute chunks (Pomodoro Technique). 2) Test yourself frequently. 3) Stay organized using planners or apps like Notion or Todoist.", "resources for studying": "You can find free study resources on websites like Khan Academy, Coursera, and edX. For research papers, check Google Scholar.", "how to focus": "To improve focus, try studying in a quiet place, remove distractions like your phone, and use apps like Forest or Focus@Will.", "time management tips": "Start by creating a to-do list each morning. Prioritize tasks using methods like Eisenhower Matrix and allocate specific time blocks for each task.", "how to avoid procrastination": "Break tasks into smaller steps, set deadlines, and reward yourself after completing milestones. Tools like Trello can help you stay organized." } # Define the chatbot's response function def faq_chatbot(user_input): # Check if the user's input matches any FAQ keywords for key, response in faq_responses.items(): if key in user_input.lower(): return response # If no FAQ match, use the AI model to generate a response conversation = chatbot(user_input, max_length=50, num_return_sequences=1) return conversation[0]['generated_text'] # Create the Gradio interface interface = gr.Interface( fn=faq_chatbot, # The function to handle user input inputs=gr.Textbox(lines=2, placeholder="Ask me about studying tips or resources..."), # Input text box outputs="text", # Output as text title="Student FAQ Chatbot", description="Ask me for study tips, time management advice, or about resources to help with your studies!" ) # Launch the chatbot interface.launch()