# -*- coding: utf-8 -*- """App Automatically generated by Colab. Original file is located at https://colab.research.google.com/drive/1TdjbTSA8V5GUProQ3Bd-uYmTLXSInoWf """ import subprocess def install_espeak(): try: #subprocess.run(["sudo", "apt-get", "install", "espeak", "-y"], check=True) subprocess.run(["apt-get", "install", "espeak", "-y"], check=True) # Removed 'sudo' print("eSpeak installed successfully!") except subprocess.CalledProcessError as e: print(f"Error occurred while installing eSpeak: {e}") # Call the function to install eSpeak install_espeak() import gradio as gr import numpy as np from transformers import pipeline import os import groq from langchain_groq import ChatGroq from langchain.schema import HumanMessage from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.vectorstores import Chroma from langchain.embeddings import HuggingFaceEmbeddings from langchain.docstore.document import Document 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="DeepSeek-R1-Distill-Llama-70b", api_key=groq.api_key) # llama-3.3-70b-versatile # Initialize Embeddings embedding_model = HuggingFaceEmbeddings() # Initialize ChromaDB vectorstore = Chroma(embedding_function=embedding_model) # 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. 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) # Remove tags cleaned_text = re.sub(r"(\*\*|\*)", "", cleaned_text) # Remove **bold** and *italics* cleaned_text = re.sub(r"^#+\s*", "", cleaned_text, flags=re.MULTILINE) # Remove # and ### tags 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)]) # Apply text cleaning before returning the response cleaned_response = clean_response(response.content) return cleaned_response # Function to handle chatbot interactions def chat_with_groq(user_input): try: response = chat_model([HumanMessage(content=user_input)]) cleaned_response_text = clean_response(response.content) # Clean the response here audio_file = speech_playback(cleaned_response_text) # Play the speech after generating the response return cleaned_response_text, audio_file # Return both response and audio file path except Exception as e: return f"Error: {str(e)}", None # Function to play response as speech using gTTS def speech_playback(text): tts = gtts.gTTS(text, lang='en') audio_file = "output_audio.mp3" tts.save(audio_file) return audio_file # Return the path to the audio file # Function to detect encoding safely def detect_encoding(file_path): try: with open(file_path, "rb") as f: raw_data = f.read(4096) # Read first 4KB for detection detected = chardet.detect(raw_data) encoding = detected["encoding"] return encoding if encoding else "utf-8" # Default to UTF-8 if detection fails 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 f"Error: Images cannot be processed for text extraction." # Extract text based on file type 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() # Process text into chunks 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) # Generate quiz based on document content 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 # Convert to mono if stereo 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"] # Gradio UI with gr.Blocks() as demo: gr.HTML("

AI Tutor

") gr.HTML("""
""") with gr.Row(): with gr.Column(): audio_input = gr.Audio(type="numpy", label="Record Audio") transcription_output = gr.Textbox(label="Transcription") user_input = gr.Textbox(label="Ask a question") chat_output = gr.Textbox(label="Response") audio_output = gr.Audio(label="Audio Playback") # Add an audio output component submit_btn = gr.Button("Ask") with gr.Column(): file_upload = gr.File(label="Upload a document") process_status = gr.Textbox(label="Processing Status", interactive=False) process_btn = gr.Button("Process Document") audio_input.change(fn=transcribe_audio, inputs=audio_input, outputs=transcription_output) transcription_output.change(fn=lambda x: x, inputs=transcription_output, outputs=user_input) submit_btn.click(chat_with_groq, inputs=user_input, outputs=[chat_output, audio_output]) # Fixed closing brackets process_btn.click(process_document, inputs=file_upload, outputs=process_status) # Launch the Gradio app demo.launch()