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
import cv2
import numpy as np
import matplotlib.pyplot as plt
import re

# 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 = ""
    formulas = []
    for image in images:
        try:
            # Extract text
            ocr_text += pytesseract.image_to_string(image, lang=lang).strip() + "\n"
            # Extract formulas (simple heuristic for LaTeX-style formulas)
            extracted_formula = pytesseract.image_to_string(image, config='--psm 6')
            formulas += re.findall(r'\$.*?\$', extracted_formula)
        except Exception as e:
            logging.error(f"Error in OCR: {e}")
    return ocr_text.strip(), formulas

# Function to extract formulas using Tesseract OCR
def extract_formula_using_tesseract(image_path):
    image = Image.open(image_path)
    gray_image = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2GRAY)
    _, thresh_image = cv2.threshold(gray_image, 150, 255, cv2.THRESH_BINARY_INV)
    
    custom_oem_psm_config = r'--oem 3 --psm 6'  # PSM 6 is used for block text
    extracted_text = pytesseract.image_to_string(thresh_image, config=custom_oem_psm_config)
    
    return extracted_text

# Function to extract text, images, tables, and formulas from PDF
def extract_pdf_data(pdf_path):
    data = {"text": "", "tables": [], "images": [], "formulas": []}
    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)
                    # Extract formulas from images (OCR)
                    extracted_text = extract_formula_using_tesseract(image_obj)
                    if extracted_text:
                        data["formulas"].append(extracted_text)
    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()])
        formulas = []
        # Search for formulas in the text
        for para in doc.paragraphs:
            if '$' in para.text:  # Simple LaTeX style formula detection
                formulas.append(para.text.strip())
        return text, formulas
    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 = []
    formulas = []
    if file_type == "pdf":
        pdf_data = extract_pdf_data(file_data)
        text = process_pdf_content(pdf_data)
        images = pdf_data["images"]
        formulas = pdf_data["formulas"]
    elif file_type == "docx":
        text, formulas = 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, image_formulas = extract_text_from_images(images, lang)
        formulas += image_formulas

    ocr_text, image_formulas = extract_text_from_images(images, lang)
    formulas += image_formulas
    
    return text + "\n" + ocr_text + "\n" + "\n".join(formulas)

# Function to process PDF content
def process_pdf_content(pdf_data):
    ocr_text, _ = extract_text_from_images(pdf_data["images"])  # Unpack the tuple
    combined_text = pdf_data["text"] + ocr_text  # Concatenate strings


    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 with graphs and formulas
def generate_questions_with_graphs_and_formulas(syllabus_content, num_questions, subject_name, difficulty_level):
    prompt_template = f"""
    Generate {num_questions} questions based on the syllabus content below. 
    Some questions should include graphs, charts, or LaTeX equations where applicable.
    Subject: {subject_name}
    Difficulty Levels: {difficulty_level}
    Syllabus Content: {syllabus_content}
    Format:
    - Question 1: Text with equation/graph
    """
    chain = (ChatPromptTemplate.from_template(prompt_template) | llm | StrOutputParser())
    try:
        return chain.invoke({})
    except Exception as e:
        logging.error(f"Error generating questions with graphs and formulas: {e}")
        return ""

# Function to generate bar chart for example
def generate_bar_chart(data, title="Graph"):
    plt.figure(figsize=(5, 4))
    plt.bar(data.keys(), data.values())
    plt.title(title)
    plt.xlabel("X-axis")
    plt.ylabel("Y-axis")
    plt.tight_layout()
    
    buffer = BytesIO()
    plt.savefig(buffer, format="png")
    buffer.seek(0)
    plt.close()
    return buffer

# Function to render LaTeX formulas in Streamlit
def render_latex_formula(formula):
    st.markdown(f"$$ {formula} $$")

# 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 with charts and LaTeX formulas
def download_as_pdf_with_graphs_and_formulas(content, chart_buffers=None, latex_formulas=None, file_name="output.pdf"):
    pdf = FPDF()
    pdf.add_page()
    pdf.set_font("Arial", size=12)
    
    # Add content (questions/answers)
    for line in content.split("\n"):
        pdf.cell(200, 10, txt=line, ln=True)

    # Insert chart images
    if chart_buffers:
        for buffer in chart_buffers:
            pdf.image(buffer, x=10, y=pdf.get_y(), w=180)  # Adjust coordinates and image size as needed
            pdf.ln(50)  # Add space for the next content

    # Insert LaTeX formula placeholders
    if latex_formulas:
        for formula in latex_formulas:
            pdf.multi_cell(200, 10, txt=f"Formula: {formula}", ln=True)

    # Save the buffer to memory
    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")

# Difficulty Selection
difficulty_level = st.sidebar.radio("Select Difficulty Level", ("Easy", "Medium", "Hard"))

# Handle file uploads and process them
if uploaded_file is not None:
    file_data = uploaded_file.read()
    file_type = uploaded_file.type.split("/")[1].lower()
    syllabus_content = process_content(file_data, file_type)
    st.session_state.syllabus_text = syllabus_content
    st.success("Syllabus content loaded successfully!")

# Generate Exam Paper with Graphs and Formulas
num_questions = st.sidebar.number_input("Number of Questions", min_value=1, max_value=20, value=5)

if st.sidebar.button("Generate Exam Paper"):
    questions = generate_questions_with_graphs_and_formulas(
        syllabus_content=st.session_state.syllabus_text,
        num_questions=num_questions,
        subject_name=subject_name,
        difficulty_level=difficulty_level
    )
    
    # Display generated questions
    st.session_state.generated_questions = questions
    st.markdown("### Generated Exam Questions")
    st.text_area("Exam Questions", questions, height=400)

# Download Options
if "generated_questions" in st.session_state:
    download_choice = st.radio("Select Download Format", ["DOCX", "PDF", "TXT"])
    
    if download_choice == "DOCX":
        download_buffer = download_as_docx(st.session_state.generated_questions)
        st.download_button("Download DOCX", download_buffer, file_name="exam_questions.docx", mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document")
    
    elif download_choice == "PDF":
        chart_buffer = generate_bar_chart({"Math": 80, "Science": 70, "English": 90})
        latex_formula = r"\frac{d}{dx} \sin(x) = \cos(x)"
        download_buffer = download_as_pdf_with_graphs_and_formulas(
            st.session_state.generated_questions,
            chart_buffers=[chart_buffer],
            latex_formulas=[latex_formula]
        )
        st.download_button("Download PDF", download_buffer, file_name="exam_questions.pdf", mime="application/pdf")
    
    elif download_choice == "TXT":
        st.download_button("Download TXT", st.session_state.generated_questions, file_name="exam_questions.txt", mime="text/plain")