app.py

# -*- coding: utf-8 -*-
"""App

Automatically generated by Colab.
"""

# Note: The eSpeak installation code has been removed.
# Instead, ensure that "espeak" is listed in your apt.txt file for Hugging Face Spaces.

import gradio as gr
import numpy as np
from transformers import pipeline
import os
import groq

# 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

# Initialize Chat Model
chat_model = ChatGroq(model_name="DeepSeek-R1-Distill-Llama-70b", api_key=groq.api_key)

# 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 <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.
"""

# 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)
    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
def chat_with_groq(user_input):
    try:
        relevant_docs = retrieve_documents(user_input)
        context = "\n".join(relevant_docs)
        response = chat_model([HumanMessage(content=user_input + "\n\nContext:\n" + context)])
        cleaned_response_text = clean_response(response.content)
        audio_file = speech_playback(cleaned_response_text)
        return cleaned_response_text, audio_file
    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

# 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"]

# Gradio UI
with gr.Blocks() as demo:
    gr.HTML("<h2 style='text-align: center;'>AI Tutor</h2>")
    gr.HTML("""
        <div style="text-align: center; margin-bottom: 20px;">
            <img src="https://img.freepik.com/premium-photo/little-girl-is-seen-sitting-front-laptop-computer-engaged-with-nearby-robot-robot-assistant-helping-child-with-homework-ai-generated_585735-12266.jpg" style="max-width: 60%; height: auto; border-radius: 10px; box-shadow: 0 4px 8px rgba(0,0,0,0.2);" />
        </div>
    """)
    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")
            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])
    process_btn.click(process_document, inputs=file_upload, outputs=process_status)
    
# Launch the Gradio app
demo.launch()