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| from transformers import pipeline | |
| import gradio as gr # Import Gradio for the interface | |
| # Load a text-generation model | |
| chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium") | |
| # Load the classification model | |
| classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli") | |
| # 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): | |
| # Classify the user input by passing the FAQ keywords as labels | |
| classified_user_input = classifier(user_input, candidate_labels=list(faq_responses.keys())) | |
| # Get the highest confidence score label, ie. the most likely of the FAQ | |
| predicted_label = classified_user_input["labels"][0] | |
| confidence_score = classified_user_input["scores"][0] | |
| # Confidence threshold (adjust if needed) | |
| threshold = 0.5 | |
| # If the classification confidence is high, return the corresponding FAQ response | |
| if confidence_score > threshold: | |
| return faq_responses[predicted_label] | |
| # 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 and make it public | |
| interface.launch(share=True) | |