app.py

# -*- 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")

# 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 <think> 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 <think> tags, asterisks, and markdown formatting."""
    cleaned_text = re.sub(r"<think>.*?</think>", "", 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
                    chatbot = gr.Chatbot(height=500, type="messages")
                    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")
                    gr.Video("https://huggingface.co/spaces/Lesterchia174/FPOC2_AI-Tutor_Chatbot/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()