app.py

import streamlit as st
from langchain_groq import ChatGroq
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from dotenv import load_dotenv
import pytesseract
from PIL import Image
import pdfplumber
import docx
from io import BytesIO
import logging
from docx import Document
from fpdf import FPDF

# Load environment variables
load_dotenv()

# Initialize logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")

# Initialize LLM
llm = ChatGroq(temperature=0.5, groq_api_key="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", model_name="llama3-8b-8192")

# OCR Configuration for Pytesseract
pytesseract.pytesseract.tesseract_cmd = r"/usr/bin/tesseract"  # Adjust to your system's path

# Enhanced OCR with configurable language option and multi-image support
def extract_text_from_images(images, lang="eng"):
    ocr_text = ""
    for image in images:
        try:
            ocr_text += pytesseract.image_to_string(image, lang=lang).strip() + "\n"
        except Exception as e:
            logging.error(f"Error in OCR: {e}")
    return ocr_text.strip()

# Function to extract text, images, tables, and formulas from PDF
def extract_pdf_data(pdf_path):
    data = {"text": "", "tables": [], "images": []}
    try:
        with pdfplumber.open(pdf_path) as pdf:
            for page in pdf.pages:
                data["text"] += page.extract_text() or ""
                tables = page.extract_tables()
                for table in tables:
                    data["tables"].append(table)
                for image in page.images:
                    base_image = pdf.extract_image(image["object_number"])
                    image_obj = Image.open(BytesIO(base_image["image"]))
                    data["images"].append(image_obj)
    except Exception as e:
        logging.error(f"Error processing PDF: {e}")
    return data

# Function to extract text from DOCX files
def extract_docx_data(docx_file):
    try:
        doc = docx.Document(docx_file)
        text = "\n".join([para.text.strip() for para in doc.paragraphs if para.text.strip()])
        return text
    except Exception as e:
        logging.error(f"Error extracting DOCX content: {e}")
        return ""

# Function to extract text from plain text files
def extract_text_file_data(text_file):
    try:
        return text_file.read().decode("utf-8").strip()
    except Exception as e:
        logging.error(f"Error extracting TXT content: {e}")
        return ""

# Function to process extracted content (PDF, DOCX, etc.)
def process_content(file_data, file_type, lang="eng"):
    text = ""
    images = []
    if file_type == "pdf":
        pdf_data = extract_pdf_data(file_data)
        text = process_pdf_content(pdf_data)
        images = pdf_data["images"]
    elif file_type == "docx":
        text = extract_docx_data(file_data)
    elif file_type == "txt":
        text = extract_text_file_data(file_data)
    elif file_type in ["png", "jpg", "jpeg"]:
        image = Image.open(file_data)
        images.append(image)

    ocr_text = extract_text_from_images(images, lang)
    return text + "\n" + ocr_text

# Function to process PDF content
def process_pdf_content(pdf_data):
    ocr_text = extract_text_from_images(pdf_data["images"])
    combined_text = pdf_data["text"] + ocr_text

    table_text = ""
    for table in pdf_data["tables"]:
        table_rows = [" | ".join(str(cell) if cell else "" for cell in row) for row in table]
        table_text += "\n".join(table_rows) + "\n"

    return (combined_text + "\n" + table_text).strip()

# Function to generate questions
def generate_questions(question_type, subject_name, instructor, class_name, institution, syllabus_context, num_questions, difficulty_level):
    prompt_template = f"""
    Based on the following syllabus content, generate {num_questions} {question_type} questions. Ensure the questions are directly derived from the provided syllabus content.
    Subject: {subject_name}
    Instructor: {instructor}
    Class: {class_name}
    Institution: {institution}
    Syllabus Content: {syllabus_context}
    Difficulty Levels:
    - Remember: {difficulty_level.get('Remember', 0)}
    - Understand: {difficulty_level.get('Understand', 0)}
    - Apply: {difficulty_level.get('Apply', 0)}
    - Analyze: {difficulty_level.get('Analyze', 0)}
    - Evaluate: {difficulty_level.get('Evaluate', 0)}
    - Create: {difficulty_level.get('Create', 0)}
    Format questions as follows:
    Q1. ________________
    Q2. ________________
    ...
    """
    chain = (ChatPromptTemplate.from_template(prompt_template) | llm | StrOutputParser())
    try:
        return chain.invoke({})
    except Exception as e:
        logging.error(f"Error generating {question_type} questions: {e}")
        return ""

# Function to generate answers
def generate_answers(questions, syllabus_context):
    prompt = f"""
    Based on the provided syllabus content, generate detailed answers for the following questions. The answers must only be based on the syllabus content.
    Syllabus Content: {syllabus_context}
    Questions:
    {questions}
    Format answers as follows:
    Answer 1: ________________
    Answer 2: ________________
    ...
    """
    chain = (ChatPromptTemplate.from_template(prompt) | llm | StrOutputParser())
    try:
        return chain.invoke({})
    except Exception as e:
        logging.error(f"Error generating answers: {e}")
        return ""

# Function to download as DOCX
def download_as_docx(content, file_name="output.docx"):
    doc = Document()
    for line in content.split("\n"):
        doc.add_paragraph(line)
    buffer = BytesIO()
    doc.save(buffer)
    buffer.seek(0)
    return buffer

# Function to download as PDF
def download_as_pdf(content, file_name="output.pdf"):
    pdf = FPDF()
    pdf.add_page()
    pdf.set_font("Arial", size=12)
    for line in content.split("\n"):
        pdf.cell(200, 10, txt=line, ln=True)
    buffer = BytesIO()
    pdf.output(buffer)
    buffer.seek(0)
    return buffer

# Streamlit app with enhanced UI and multi-image upload support
st.title("Bloom's Taxonomy Based Exam Paper Developer")
st.markdown("""
### A powerful tool to generate exam questions and answers using AI, based on syllabus content and Bloom's Taxonomy principles.
""")

# Sidebar Clear Data Button
if st.sidebar.button("Clear All Data"):
    st.session_state.clear()
    st.success("All data has been cleared. You can now upload a new syllabus.")

# Upload Syllabus and Multiple Images
uploaded_file = st.sidebar.file_uploader(
    "Upload Syllabus (PDF, DOCX, TXT)",
    type=["pdf", "docx", "txt"]
)

uploaded_images = st.sidebar.file_uploader(
    "Upload Supplementary Images (PNG, JPG, JPEG)", 
    type=["png", "jpg", "jpeg"], 
    accept_multiple_files=True
)

# Sidebar Inputs for Subject Name, Instructor, Class, and Institution
subject_name = st.sidebar.text_input("Enter Subject Name", "Subject Name")
instructor_name = st.sidebar.text_input("Enter Instructor Name", "Instructor Name")
class_name = st.sidebar.text_input("Enter Class Name", "Class Name")
institution_name = st.sidebar.text_input("Enter Institution Name", "Institution Name")

# Language Option for OCR
ocr_lang = st.sidebar.selectbox("Select OCR Language", ["eng", "spa", "fra", "deu", "ita"])

# Process uploaded file and images
if uploaded_file or uploaded_images:
    # Clear session state when new files are uploaded
    if "uploaded_filename" in st.session_state and st.session_state.uploaded_filename != uploaded_file.name:
        st.session_state.clear()
        st.success("Previous data cleared. Processing new file...")

    st.session_state.uploaded_filename = uploaded_file.name if uploaded_file else None

    # Process syllabus file
    if uploaded_file:
        file_type = uploaded_file.type.split("/")[-1]
        if file_type in ["pdf", "docx", "txt"]:
            syllabus_text = process_content(uploaded_file, file_type, lang=ocr_lang)
            st.session_state.syllabus_text = syllabus_text
        else:
            st.error("Unsupported file type. Please upload PDF, DOCX, or TXT files.")

    # Process images
    if uploaded_images:
        image_text = extract_text_from_images([Image.open(img) for img in uploaded_images], lang=ocr_lang)
        st.session_state.syllabus_text = st.session_state.get("syllabus_text", "") + "\n" + image_text

# Preview of Syllabus
if "syllabus_text" in st.session_state:
    st.markdown("### Preview of Extracted Syllabus Content")
    st.text_area("Extracted Syllabus Content", st.session_state.syllabus_text, height=300)

# Inputs for Question Generation
if "syllabus_text" in st.session_state:
    st.markdown("### Generate Questions")
    question_type = st.selectbox("Select Question Type", ["Multiple Choice", "Short Answer", "Essay"])
    num_questions = st.number_input("Number of Questions", min_value=1, max_value=50, value=10)
    difficulty_levels = {
        "Remember": st.slider("Remember (%)", 0, 100, 20),
        "Understand": st.slider("Understand (%)", 0, 100, 20),
        "Apply": st.slider("Apply (%)", 0, 100, 20),
        "Analyze": st.slider("Analyze (%)", 0, 100, 20),
        "Evaluate": st.slider("Evaluate (%)", 0, 100, 10),
        "Create": st.slider("Create (%)", 0, 100, 10),
    }

    if st.button("Generate Questions"):
        with st.spinner("Generating questions..."):
            questions = generate_questions(
                question_type,
                subject_name,
                instructor_name,
                class_name,
                institution_name,
                st.session_state.syllabus_text,
                num_questions,
                difficulty_levels,
            )
            st.session_state.generated_questions = questions
            st.success("Questions generated successfully!")

# Display Generated Questions
if "generated_questions" in st.session_state:
    st.markdown("### Generated Questions")
    st.text_area("Questions", st.session_state.generated_questions, height=300)

    if st.button("Generate Answers"):
        with st.spinner("Generating answers..."):
            answers = generate_answers(
                st.session_state.generated_questions,
                st.session_state.syllabus_text,
            )
            st.session_state.generated_answers = answers
            st.success("Answers generated successfully!")

# Display Generated Answers
if "generated_answers" in st.session_state:
    st.markdown("### Generated Answers")
    st.text_area("Answers", st.session_state.generated_answers, height=300)

# Download Options
if "generated_questions" in st.session_state or "generated_answers" in st.session_state:
    st.markdown("### Download Options")
    download_choice = st.radio("Select Download Format", ["DOCX", "PDF", "TXT"])

    content_to_download = ""
    if "generated_questions" in st.session_state:
        content_to_download += "Generated Questions:\n" + st.session_state.generated_questions + "\n\n"
    if "generated_answers" in st.session_state:
        content_to_download += "Generated Answers:\n" + st.session_state.generated_answers

    if st.button("Download"):
        if download_choice == "DOCX":
            buffer = download_as_docx(content_to_download)
            st.download_button(
                label="Download as DOCX",
                data=buffer,
                file_name="exam_content.docx",
                mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document",
            )
        elif download_choice == "PDF":
            buffer = download_as_pdf(content_to_download)
            st.download_button(
                label="Download as PDF",
                data=buffer,
                file_name="exam_content.pdf",
                mime="application/pdf",
            )
        elif download_choice == "TXT":
            buffer = BytesIO(content_to_download.encode("utf-8"))
            st.download_button(
                label="Download as TXT",
                data=buffer,
                file_name="exam_content.txt",
                mime="text/plain",
            )