# -*- coding: utf-8 -*- """app Automatically generated by Colab. Original file is located at https://colab.research.google.com/drive/1pwwcBb5Zlw1DA3u5K8W8mjrwBTBWXc1L """ import gradio as gr import numpy as np from transformers import pipeline import os import time import groq import uuid # For generating unique filenames # Updated imports to address LangChain deprecation warnings: from langchain_groq import ChatGroq from langchain.schema import HumanMessage from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.vectorstores import Chroma from langchain_community.embeddings import HuggingFaceEmbeddings from langchain.docstore.document import Document # Importing chardet (make sure to add chardet to your requirements.txt) import chardet import fitz # PyMuPDF for PDFs import docx # python-docx for Word files import gtts # Google Text-to-Speech library from pptx import Presentation # python-pptx for PowerPoint files import re # Initialize Whisper model for speech-to-text transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en") # Set API Key (Ensure it's stored securely in an environment variable) groq.api_key = os.getenv("GROQ_API_KEY") # Replace with a valid API key #___________________________________ # Authenticate with Hugging Face API using the token #hf_token = os.getenv("HF_TOKEN") # Replace with the environment variable containing your Hugging Face token #login(token=hf_token) # Load the LLaVA model #model_id = "liuhaotian/LLaVA-7B" # You can change the model ID based on what is available #processor = AutoProcessor.from_pretrained(model_id) #model = AutoModelForVision2Seq.from_pretrained(model_id, torch_dtype=torch.float16).cuda() # Load and preprocess an image #image = Image.open("your_image.jpg") # Replace with the path to your image #inputs = processor(text="Describe this image", images=image, return_tensors="pt").to("cuda") # Generate output from LLaVA model #output = model.generate(**inputs) # Decode and print the output #print(processor.decode(output[0])) #___________________________________ # Initialize Chat Model chat_model = ChatGroq(model_name="llama-3.3-70b-versatile", api_key=groq.api_key) #DeepSeek-R1-Distill-Llama-70b , llama-3.3-70b-versatile , deepseek-r1-distill-qwen-32b # Initialize Embeddings and chromaDB embedding_model = HuggingFaceEmbeddings() vectorstore = Chroma(embedding_function=embedding_model) # -*- coding: utf-8 -*- """app Automatically generated by Colab. Original file is located at https://colab.research.google.com/drive/1jdKA4WQJcLb0_aQ3vtGVM46B1wriSsDv """ import gradio as gr import numpy as np from transformers import pipeline import os import time import groq import uuid # For generating unique filenames # Updated imports to address LangChain deprecation warnings: from langchain_groq import ChatGroq from langchain.schema import HumanMessage from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.vectorstores import Chroma from langchain_community.embeddings import HuggingFaceEmbeddings from langchain.docstore.document import Document # Importing chardet (make sure to add chardet to your requirements.txt) import chardet import fitz # PyMuPDF for PDFs import docx # python-docx for Word files import gtts # Google Text-to-Speech library from pptx import Presentation # python-pptx for PowerPoint files import re # Initialize Whisper model for speech-to-text transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en") # Set API Key (Ensure it's stored securely in an environment variable) groq.api_key = os.getenv("GROQ_API_KEY", "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") # Replace with a valid API key # Initialize Chat Model chat_model = ChatGroq(model_name="llama-3.3-70b-versatile", api_key=groq.api_key) #DeepSeek-R1-Distill-Llama-70b | deepseek-r1-distill-qwen-32b # Initialize Embeddings and chromaDB os.makedirs("chroma_db", exist_ok=True) embedding_model = HuggingFaceEmbeddings() #new vectorstore = Chroma( embedding_function=embedding_model, persist_directory="chroma_db" # Set a valid folder name or path ) vectorstore.persist() #end New # Short-term memory for the LLM chat_memory = [] # Prompt for quiz generation with added remark quiz_prompt = """ You are an AI assistant specialized in education and assessment creation. Given an uploaded document or text, generate a quiz with a mix of multiple-choice questions (MCQs) and fill-in-the-blank questions. The quiz should be directly based on the key concepts, facts, and details from the provided material. Generate 20 Questions. Remove all unnecessary formatting generated by the LLM, including tags, asterisks, markdown formatting, and any bold or italic text, as well as **, ###, ##, and # tags. For each question: - Provide 4 answer choices (for MCQs), with only one correct answer. - Ensure fill-in-the-blank questions focus on key terms, phrases, or concepts from the document. - Include an answer key for all questions. - Ensure questions vary in difficulty and encourage comprehension rather than memorization. - Additionally, implement an instant feedback mechanism: - When a user selects an answer, indicate whether it is correct or incorrect. - If incorrect, provide a brief explanation from the document to guide learning. - Ensure responses are concise and educational to enhance understanding. Output Example: 1. Fill in the blank: The LLM Agent framework has a central decision-making unit called the _______________________. Answer: Agent Core Feedback: The Agent Core is the central component of the LLM Agent framework, responsible for managing goals, tool instructions, planning modules, memory integration, and agent persona. 2. What is the main limitation of LLM-based applications? a) Limited token capacity b) Lack of domain expertise c) Prone to hallucination d) All of the above Answer: d) All of the above Feedback: LLM-based applications have several limitations, including limited token capacity, lack of domain expertise, and being prone to hallucination, among others. 3. Given the following info, what is the value of P(jam|Rain)? P(no Rain) = 0.8; P(no Jam) = 0.2; P(Rain|Jam) = 0.1 a) 0.016 b) 0.025 c) 0.1 d) 0.4 Answer: d) 0.4 Feedback: This question tests understanding of Bayes’ Theorem by requiring the calculation of conditional probability using the given values. """ # Function to clean AI response by removing unwanted formatting def clean_response(response): """Removes tags, asterisks, and markdown formatting.""" cleaned_text = re.sub(r".*?", "", response, flags=re.DOTALL) cleaned_text = re.sub(r"(\*\*|\*|\[|\])", "", cleaned_text) cleaned_text = re.sub(r"^##+\s*", "", cleaned_text, flags=re.MULTILINE) cleaned_text = re.sub(r"\\", "", cleaned_text) cleaned_text = re.sub(r"---", "", cleaned_text) return cleaned_text.strip() # Function to generate quiz based on content def generate_quiz(content): prompt = f"{quiz_prompt}\n\nDocument content:\n{content}" response = chat_model([HumanMessage(content=prompt)]) cleaned_response = clean_response(response.content) return cleaned_response # Function to retrieve relevant documents from vectorstore based on user query def retrieve_documents(query): results = vectorstore.similarity_search(query, k=3) return [doc.page_content for doc in results] # Function to handle chatbot interactions with short-term memory def chat_with_groq(user_input): try: # Retrieve relevant documents for additional context relevant_docs = retrieve_documents(user_input) context = "\n".join(relevant_docs) if relevant_docs else "No relevant documents found." # Construct proper prompting with conversation history system_prompt = "You are a helpful AI assistant. Answer questions accurately and concisely." conversation_history = "\n".join(chat_memory[-10:]) # Keep the last 10 exchanges prompt = f"{system_prompt}\n\nConversation History:\n{conversation_history}\n\nUser Input: {user_input}\n\nContext:\n{context}" # Call the chat model response = chat_model([HumanMessage(content=prompt)]) # Clean response to remove any unwanted formatting cleaned_response_text = clean_response(response.content) # Append conversation history chat_memory.append(f"User: {user_input}") chat_memory.append(f"AI: {cleaned_response_text}") # Convert response to speech audio_file = speech_playback(cleaned_response_text) # Ensure the return format is a list of tuples return [(user_input, cleaned_response_text)], audio_file except Exception as e: return [("Error", str(e))], None # Function to play response as speech using gTTS def speech_playback(text): try: # Generate a unique filename for each audio file unique_id = str(uuid.uuid4()) audio_file = f"output_audio_{unique_id}.mp3" # Convert text to speech tts = gtts.gTTS(text, lang='en') tts.save(audio_file) # Return the path to the audio file return audio_file except Exception as e: print(f"Error in speech_playback: {e}") return None # Function to detect encoding safely def detect_encoding(file_path): try: with open(file_path, "rb") as f: raw_data = f.read(4096) detected = chardet.detect(raw_data) encoding = detected["encoding"] return encoding if encoding else "utf-8" except Exception: return "utf-8" # Function to extract text from PDF def extract_text_from_pdf(pdf_path): try: doc = fitz.open(pdf_path) text = "\n".join([page.get_text("text") for page in doc]) return text if text.strip() else "No extractable text found." except Exception as e: return f"Error extracting text from PDF: {str(e)}" # Function to extract text from Word files (.docx) def extract_text_from_docx(docx_path): try: doc = docx.Document(docx_path) text = "\n".join([para.text for para in doc.paragraphs]) return text if text.strip() else "No extractable text found." except Exception as e: return f"Error extracting text from Word document: {str(e)}" # Function to extract text from PowerPoint files (.pptx) def extract_text_from_pptx(pptx_path): try: presentation = Presentation(pptx_path) text = "" for slide in presentation.slides: for shape in slide.shapes: if hasattr(shape, "text"): text += shape.text + "\n" return text if text.strip() else "No extractable text found." except Exception as e: return f"Error extracting text from PowerPoint: {str(e)}" # Function to process documents safely def process_document(file): try: file_extension = os.path.splitext(file.name)[-1].lower() if file_extension in [".png", ".jpg", ".jpeg"]: return "Error: Images cannot be processed for text extraction." if file_extension == ".pdf": content = extract_text_from_pdf(file.name) elif file_extension == ".docx": content = extract_text_from_docx(file.name) elif file_extension == ".pptx": content = extract_text_from_pptx(file.name) else: encoding = detect_encoding(file.name) with open(file.name, "r", encoding=encoding, errors="replace") as f: content = f.read() text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50) documents = [Document(page_content=chunk) for chunk in text_splitter.split_text(content)] vectorstore.add_documents(documents) vectorstore.persist() # <-- Persist changes after adding documents quiz = generate_quiz(content) return f"Document processed successfully (File Type: {file_extension}). Quiz generated:\n{quiz}" except Exception as e: return f"Error processing document: {str(e)}" # Function to handle speech-to-text conversion def transcribe_audio(audio): sr, y = audio if y.ndim > 1: y = y.mean(axis=1) y = y.astype(np.float32) y /= np.max(np.abs(y)) return transcriber({"sampling_rate": sr, "raw": y})["text"] # Modify chat_with_groq function to return audio file for playback def chat_with_groq(user_input): try: # Retrieve relevant documents for additional context relevant_docs = retrieve_documents(user_input) context = "\n".join(relevant_docs) if relevant_docs else "No relevant documents found." # Construct proper prompting with conversation history system_prompt = "You are a helpful AI assistant. Answer questions accurately and concisely." conversation_history = "\n".join(chat_memory[-10:]) # Keep the last 10 exchanges prompt = f"{system_prompt}\n\nConversation History:\n{conversation_history}\n\nUser Input: {user_input}\n\nContext:\n{context}" # Call the chat model response = chat_model([HumanMessage(content=prompt)]) # Clean response to remove any unwanted formatting cleaned_response_text = clean_response(response.content) # Append conversation history chat_memory.append(f"User: {user_input}") chat_memory.append(f"AI: {cleaned_response_text}") # Convert response to speech audio_file = speech_playback(cleaned_response_text) # Return both chat response and audio file path return [(user_input, cleaned_response_text)], audio_file # Return as a tuple except Exception as e: return [("Error", str(e))], None #__________________________________________________________________________________________________________________________ def tutor_ai_chatbot(): """Main Gradio interface for the Tutor AI Chatbot.""" with gr.Blocks() as app: gr.Markdown("# 📚 AI Tutor - We.(POC)") gr.Markdown("An interactive Personal AI Tutor chatbot to help with your learning needs.") # Chatbot Tab with gr.Tab("AI Chatbot"): with gr.Row(): with gr.Column(scale=3): chatbot = gr.Chatbot(height=500) # Chatbot display area with gr.Row(): msg = gr.Textbox(label="Ask a question", placeholder="Type your question here...") submit = gr.Button("Send") #with gr.Row(): with gr.Column(scale=1): audio_input = gr.Audio(type="numpy", label="Record or Upload Audio") # Audio input for speech-to-text with gr.Column(scale=1): audio_playback = gr.Audio(label="Audio Response", type="filepath") # Clear chat history button clear_btn = gr.Button("Clear Chat") # Handle chat interaction submit.click( chat_with_groq, inputs=[msg], outputs=[chatbot, audio_playback] ) # Clear chat history function def clear_chat_history(): return None, None clear_btn.click(clear_chat_history, inputs=None, outputs=[chatbot, audio_playback]) #,audio_input # Also allow Enter key to submit msg.submit( chat_with_groq, inputs=[msg], outputs=[chatbot, audio_playback] ) # Add some examples of questions students might ask with gr.Accordion("Example Questions", open=False): gr.Examples( examples=[ "Can you explain the concept of RLHF AI?", "What are AI transformers?", "What is MoE AI?", "What's gate networks AI?", "I am making a switch, please generating baking recipe?" ], inputs=msg ) # Upload Notes & Generate Quiz Tab with gr.Tab("Upload Notes & Generate Quiz"): with gr.Row(): with gr.Column(scale=2): file_input = gr.File(label="Upload Lecture Notes (PDF, DOCX, PPTX)") #generate_btn = gr.Button("Generate Quiz") with gr.Column(scale=3): quiz_output = gr.Textbox(label="Generated Quiz", lines=10) # Introduction Video with gr.Tab("Introduction Video"): with gr.Row(): with gr.Column(scale=1): #with gr.Column(scale=1): # Adjust scale for equal width gr.Markdown("### Welcome to the Introduction Video") # Adding a heading gr.Markdown("Music from Xu Mengyuan - China-O, musician Xu Mengyuan YUAN! | 徐梦圆 - China-O 音乐人徐梦圆YUAN! ") # Adding descriptive text gr.Video("https://github.com/lesterchia1/AI_tutor/raw/main/We%20not%20me%20video.mp4", label="Introduction Video") # Connect the button to the document processing function audio_input.change(fn=transcribe_audio, inputs=audio_input, outputs=msg) # transcribe and fill the msg textbox file_input.change(process_document, inputs=file_input, outputs=quiz_output) # Launch the application app.launch(share=True) # Set share=True to create a public link # Launch the AI chatbot if __name__ == "__main__": tutor_ai_chatbot() # Short-term memory for the LLM chat_memory = [] # Prompt for quiz generation with added remark quiz_prompt = """ You are an AI assistant specialized in education and assessment creation. Given an uploaded document or text, generate a quiz with a mix of multiple-choice questions (MCQs) and fill-in-the-blank questions. The quiz should be directly based on the key concepts, facts, and details from the provided material. Generate 20 Questions. Remove all unnecessary formatting generated by the LLM, including tags, asterisks, markdown formatting, and any bold or italic text, as well as **, ###, ##, and # tags. For each question: - Provide 4 answer choices (for MCQs), with only one correct answer. - Ensure fill-in-the-blank questions focus on key terms, phrases, or concepts from the document. - Include an answer key for all questions. - Ensure questions vary in difficulty and encourage comprehension rather than memorization. - Additionally, implement an instant feedback mechanism: - When a user selects an answer, indicate whether it is correct or incorrect. - If incorrect, provide a brief explanation from the document to guide learning. - Ensure responses are concise and educational to enhance understanding. Output Example: 1. Fill in the blank: The LLM Agent framework has a central decision-making unit called the _______________________. Answer: Agent Core Feedback: The Agent Core is the central component of the LLM Agent framework, responsible for managing goals, tool instructions, planning modules, memory integration, and agent persona. 2. What is the main limitation of LLM-based applications? a) Limited token capacity b) Lack of domain expertise c) Prone to hallucination d) All of the above Answer: d) All of the above Feedback: LLM-based applications have several limitations, including limited token capacity, lack of domain expertise, and being prone to hallucination, among others. """ # Function to clean AI response by removing unwanted formatting def clean_response(response): """Removes tags, asterisks, and markdown formatting.""" cleaned_text = re.sub(r".*?", "", response, flags=re.DOTALL) cleaned_text = re.sub(r"(\*\*|\*|\[|\])", "", cleaned_text) cleaned_text = re.sub(r"^##+\s*", "", cleaned_text, flags=re.MULTILINE) cleaned_text = re.sub(r"\\", "", cleaned_text) cleaned_text = re.sub(r"---", "", cleaned_text) return cleaned_text.strip() # Function to generate quiz based on content def generate_quiz(content): prompt = f"{quiz_prompt}\n\nDocument content:\n{content}" response = chat_model([HumanMessage(content=prompt)]) cleaned_response = clean_response(response.content) return cleaned_response # Function to retrieve relevant documents from vectorstore based on user query def retrieve_documents(query): results = vectorstore.similarity_search(query, k=3) return [doc.page_content for doc in results] # Function to handle chatbot interactions with short-term memory def chat_with_groq(user_input): try: # Retrieve relevant documents for additional context relevant_docs = retrieve_documents(user_input) context = "\n".join(relevant_docs) if relevant_docs else "No relevant documents found." # Construct proper prompting with conversation history system_prompt = "You are a helpful AI assistant. Answer questions accurately and concisely." conversation_history = "\n".join(chat_memory[-10:]) # Keep the last 10 exchanges prompt = f"{system_prompt}\n\nConversation History:\n{conversation_history}\n\nUser Input: {user_input}\n\nContext:\n{context}" # Call the chat model response = chat_model([HumanMessage(content=prompt)]) # Clean response to remove any unwanted formatting cleaned_response_text = clean_response(response.content) # Append conversation history chat_memory.append(f"User: {user_input}") chat_memory.append(f"AI: {cleaned_response_text}") # Convert response to speech audio_file = speech_playback(cleaned_response_text) # Ensure the return format is a list of tuples return [(user_input, cleaned_response_text)], audio_file except Exception as e: return [("Error", str(e))], None # Function to play response as speech using gTTS def speech_playback(text): try: # Generate a unique filename for each audio file unique_id = str(uuid.uuid4()) audio_file = f"output_audio_{unique_id}.mp3" # Convert text to speech tts = gtts.gTTS(text, lang='en') tts.save(audio_file) # Return the path to the audio file return audio_file except Exception as e: print(f"Error in speech_playback: {e}") return None # Function to detect encoding safely def detect_encoding(file_path): try: with open(file_path, "rb") as f: raw_data = f.read(4096) detected = chardet.detect(raw_data) encoding = detected["encoding"] return encoding if encoding else "utf-8" except Exception: return "utf-8" # Function to extract text from PDF def extract_text_from_pdf(pdf_path): try: doc = fitz.open(pdf_path) text = "\n".join([page.get_text("text") for page in doc]) return text if text.strip() else "No extractable text found." except Exception as e: return f"Error extracting text from PDF: {str(e)}" # Function to extract text from Word files (.docx) def extract_text_from_docx(docx_path): try: doc = docx.Document(docx_path) text = "\n".join([para.text for para in doc.paragraphs]) return text if text.strip() else "No extractable text found." except Exception as e: return f"Error extracting text from Word document: {str(e)}" # Function to extract text from PowerPoint files (.pptx) def extract_text_from_pptx(pptx_path): try: presentation = Presentation(pptx_path) text = "" for slide in presentation.slides: for shape in slide.shapes: if hasattr(shape, "text"): text += shape.text + "\n" return text if text.strip() else "No extractable text found." except Exception as e: return f"Error extracting text from PowerPoint: {str(e)}" # Function to process documents safely def process_document(file): try: file_extension = os.path.splitext(file.name)[-1].lower() if file_extension in [".png", ".jpg", ".jpeg"]: return "Error: Images cannot be processed for text extraction." if file_extension == ".pdf": content = extract_text_from_pdf(file.name) elif file_extension == ".docx": content = extract_text_from_docx(file.name) elif file_extension == ".pptx": content = extract_text_from_pptx(file.name) else: encoding = detect_encoding(file.name) with open(file.name, "r", encoding=encoding, errors="replace") as f: content = f.read() text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50) documents = [Document(page_content=chunk) for chunk in text_splitter.split_text(content)] vectorstore.add_documents(documents) quiz = generate_quiz(content) return f"Document processed successfully (File Type: {file_extension}). Quiz generated:\n{quiz}" except Exception as e: return f"Error processing document: {str(e)}" # Function to handle speech-to-text conversion def transcribe_audio(audio): sr, y = audio if y.ndim > 1: y = y.mean(axis=1) y = y.astype(np.float32) y /= np.max(np.abs(y)) return transcriber({"sampling_rate": sr, "raw": y})["text"] # Modify chat_with_groq function to return audio file for playback def chat_with_groq(user_input): try: # Retrieve relevant documents for additional context relevant_docs = retrieve_documents(user_input) context = "\n".join(relevant_docs) if relevant_docs else "No relevant documents found." # Construct proper prompting with conversation history system_prompt = "You are a helpful AI assistant. Answer questions accurately and concisely." conversation_history = "\n".join(chat_memory[-10:]) # Keep the last 10 exchanges prompt = f"{system_prompt}\n\nConversation History:\n{conversation_history}\n\nUser Input: {user_input}\n\nContext:\n{context}" # Call the chat model response = chat_model([HumanMessage(content=prompt)]) # Clean response to remove any unwanted formatting cleaned_response_text = clean_response(response.content) # Append conversation history chat_memory.append(f"User: {user_input}") chat_memory.append(f"AI: {cleaned_response_text}") # Convert response to speech audio_file = speech_playback(cleaned_response_text) # Return both chat response and audio file path return [(user_input, cleaned_response_text)], audio_file # Return as a tuple except Exception as e: return [("Error", str(e))], None #__________________________________________________________________________________________________________________________ def tutor_ai_chatbot(): """Main Gradio interface for the Tutor AI Chatbot.""" with gr.Blocks() as app: gr.Markdown("# 📚 AI Tutor - We.(POC)") gr.Markdown("An interactive Personal AI Tutor chatbot to help with your learning needs.") # Chatbot Tab with gr.Tab("AI Chatbot"): with gr.Row(): with gr.Column(scale=3): chatbot = gr.Chatbot(height=500) # Chatbot display area with gr.Row(): msg = gr.Textbox(label="Ask a question", placeholder="Type your question here...") submit = gr.Button("Send") # Create the chat interface #msg = gr.ChatInterface(fn=chat_with_groq, type="messages", autofocus=False) #test #with gr.Row(): with gr.Column(scale=1): audio_input = gr.Audio(type="numpy", label="Record or Upload Audio") # Audio input for speech-to-text with gr.Column(scale=1): audio_playback = gr.Audio(label="Audio Response", type="filepath") # Clear chat history button clear_btn = gr.Button("Clear Chat") # Handle chat interaction submit.click( chat_with_groq, inputs=[msg], outputs=[chatbot, audio_playback] ) # Clear chat history function def clear_chat_history(): return None, None clear_btn.click(clear_chat_history, inputs=None, outputs=[chatbot, audio_playback]) #,audio_input # Also allow Enter key to submit msg.submit( chat_with_groq, inputs=[msg], outputs=[chatbot, audio_playback] ) # Add some examples of questions students might ask with gr.Accordion("Example Questions", open=False): gr.Examples( examples=[ "Can you explain the concept of RLHF AI?", "What are AI transformers?", "What is MoE AI?", "What's gate networks AI?", "I am making a switch, please generating baking recipe?" ], inputs=msg ) # Upload Notes & Generate Quiz Tab with gr.Tab("Upload Notes & Generate Quiz"): with gr.Row(): with gr.Column(scale=2): file_input = gr.File(label="Upload Lecture Notes (PDF, DOCX, PPTX) [Must be less than 6k of words]") #generate_btn = gr.Button("Generate Quiz") with gr.Column(scale=3): quiz_output = gr.Textbox(label="Generated Quiz", lines=10) # Introduction Video with gr.Tab("Introduction Video"): with gr.Row(): with gr.Column(scale=1): #with gr.Column(scale=1): # Adjust scale for equal width gr.Markdown("### Welcome to the Introduction Video") # Adding a heading gr.Markdown("Music from Xu Mengyuan - China-O, musician Xu Mengyuan YUAN! | 徐梦圆 - China-O 音乐人徐梦圆YUAN! ") # Adding descriptive text gr.Video("https://github.com/lesterchia1/AI_tutor/raw/main/We%20not%20me%20video.mp4", label="Introduction Video") # Connect the button to the document processing function audio_input.change(fn=transcribe_audio, inputs=audio_input, outputs=msg) # transcribe and fill the msg textbox file_input.change(process_document, inputs=file_input, outputs=quiz_output) # Launch the application app.launch(share=True) # Set share=True to create a public link # Add cleanup function to be triggered periodically (e.g., every time a button is clicked or after certain actions) #demo.load(lambda: cleanup_old_files(directory="./", age_limit=60), inputs=[], outputs=[]) # Launch the AI chatbot if __name__ == "__main__": tutor_ai_chatbot()