app.py

import streamlit as st
import requests
import json
import fitz  # PyMuPDF
from fpdf import FPDF
import os
import tempfile
import base64
import dotenv
from dotenv import load_dotenv
import torch
from transformers import DistilBertForSequenceClassification, DistilBertTokenizer
from torch.nn.functional import softmax
from doctr.models import ocr_predictor
from doctr.io import DocumentFile

load_dotenv()

model = DistilBertForSequenceClassification.from_pretrained('./fine_tuned_distilbert')
tokenizer = DistilBertTokenizer.from_pretrained('./fine_tuned_distilbert')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
mapping = {"Remembering": 0, "Understanding": 1, "Applying": 2, "Analyzing": 3, "Evaluating": 4, "Creating": 5}
reverse_mapping = {v: k for k, v in mapping.items()}
modelocr = ocr_predictor(det_arch='db_resnet50', reco_arch='crnn_vgg16_bn', pretrained=True)

# Previous functions from Question Generator
def get_pdf_path(pdf_source=None, uploaded_file=None):
    try:
        # If a file is uploaded locally
        if uploaded_file is not None:
            # Create a temporary file to save the uploaded PDF
            temp_dir = tempfile.mkdtemp()
            pdf_path = os.path.join(temp_dir, uploaded_file.name)
            
            # Save the uploaded file
            with open(pdf_path, "wb") as pdf_file:
                pdf_file.write(uploaded_file.getvalue())
            return pdf_path

        # If a URL is provided
        if pdf_source:
            response = requests.get(pdf_source, timeout=30)
            response.raise_for_status()
            
            # Create a temporary file
            temp_dir = tempfile.mkdtemp()
            pdf_path = os.path.join(temp_dir, "downloaded.pdf")
            
            with open(pdf_path, "wb") as pdf_file:
                pdf_file.write(response.content)
            return pdf_path

        # If no source is provided
        st.error("No PDF source provided.")
        return None
    except Exception as e:
        st.error(f"Error getting PDF: {e}")
        return None

def extract_text_pymupdf(pdf_path):
    try:
        doc = fitz.open(pdf_path)
        pages_content = []
        for page_num in range(len(doc)):
            page = doc[page_num]
            pages_content.append(page.get_text())
        doc.close()
        return " ".join(pages_content)  # Join all pages into one large context string
    except Exception as e:
        st.error(f"Error extracting text from PDF: {e}")
        return ""

def generate_ai_response(api_key, assistant_context, user_query, role_description, response_instructions, bloom_taxonomy_weights, num_questions):
    try:
        url = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash-latest:generateContent?key={api_key}"
        
        prompt = f"""
        You are a highly knowledgeable assistant. Your task is to assist the user with the following context from an academic paper.

        **Role**: {role_description}

        **Context**: {assistant_context}

        **Instructions**: {response_instructions}

        **Bloom's Taxonomy Weights**:
        Knowledge: {bloom_taxonomy_weights['Knowledge']}%
        Comprehension: {bloom_taxonomy_weights['Comprehension']}%
        Application: {bloom_taxonomy_weights['Application']}%
        Analysis: {bloom_taxonomy_weights['Analysis']}%
        Synthesis: {bloom_taxonomy_weights['Synthesis']}%
        Evaluation: {bloom_taxonomy_weights['Evaluation']}%

        **Query**: {user_query}

        **Number of Questions**: {num_questions}
        """
        
        payload = {
            "contents": [
                {
                    "parts": [
                        {"text": prompt}
                    ]
                }
            ]
        }
        headers = {"Content-Type": "application/json"}

        response = requests.post(url, headers=headers, data=json.dumps(payload), timeout=60)
        response.raise_for_status()
        
        result = response.json()
        questions = result.get("candidates", [{}])[0].get("content", {}).get("parts", [{}])[0].get("text", "")
        questions_list = [question.strip() for question in questions.split("\n") if question.strip()]
        return questions_list
    except requests.RequestException as e:
        st.error(f"API request error: {e}")
        return []
    except Exception as e:
        st.error(f"Error generating questions: {e}")
        return []

def normalize_bloom_weights(bloom_weights):
    total = sum(bloom_weights.values())
    if total != 100:
        normalization_factor = 100 / total
        # Normalize each weight by multiplying it by the normalization factor
        bloom_weights = {key: round(value * normalization_factor, 2) for key, value in bloom_weights.items()}
    return bloom_weights

def generate_pdf(questions, filename="questions.pdf"):
    try:
        pdf = FPDF()
        pdf.set_auto_page_break(auto=True, margin=15)
        pdf.add_page()

        # Set font
        pdf.set_font("Arial", size=12)
        
        # Add a title or heading
        pdf.cell(200, 10, txt="Generated Questions", ln=True, align="C")

        # Add space between title and questions
        pdf.ln(10)

        # Loop through questions and add them to the PDF
        for i, question in enumerate(questions, 1):
            # Using multi_cell for wrapping the text in case it's too long
            pdf.multi_cell(0, 10, f"Q{i}: {question}")

        # Save the generated PDF to the file
        pdf.output(filename)
        return filename
    except Exception as e:
        st.error(f"Error generating PDF: {e}")
        return None

def process_pdf_and_generate_questions(pdf_source, uploaded_file, api_key, role_description, response_instructions, bloom_taxonomy_weights, num_questions):
    try:
        # Get PDF path (either from URL or uploaded file)
        pdf_path = get_pdf_path(pdf_source, uploaded_file)
        if not pdf_path:
            return []

        # Extract text
        pdf_text = extract_text_pymupdf(pdf_path)
        if not pdf_text:
            return []

        # Generate questions
        assistant_context = pdf_text
        user_query = "Generate questions based on the above context."
        normalized_bloom_weights = normalize_bloom_weights(bloom_taxonomy_weights)
        questions = generate_ai_response(
            api_key, 
            assistant_context, 
            user_query, 
            role_description, 
            response_instructions, 
            normalized_bloom_weights, 
            num_questions
        )

        # Clean up temporary PDF file
        try:
            os.remove(pdf_path)
            # Remove the temporary directory
            os.rmdir(os.path.dirname(pdf_path))
        except Exception as e:
            st.warning(f"Could not delete temporary PDF file: {e}")

        return questions
    except Exception as e:
        st.error(f"Error processing PDF and generating questions: {e}")
        return []

def main():
    st.set_page_config(page_title="Academic Paper Tool", page_icon="📝", layout="wide")
    # Tabs for different functionalities
    st.markdown("""
       <style>
            .stTabs [data-baseweb="tab"] {
                margin-bottom: 1rem;
                flex: 1;
                justify-content: center;
            }
            .stTabs [data-baseweb="tab-list"] button [data-testid="stMarkdownContainer"] p {
            font-size:2rem;
            padding: 0 2rem;
            margin: 0;
            }
        </style>
    """, unsafe_allow_html=True)
    tab1, tab2 = st.tabs(["Question Generator", "Paper Scorer"])

    if 'totalscore' not in st.session_state:
        st.session_state.totalscore = None
    if 'show_details' not in st.session_state:
        st.session_state.show_details = False

    
    # Question Generator Tab
    with tab1:
        st.title("🎓 Academic Paper Question Generator")
        st.markdown("Generate insightful questions from academic papers using Bloom's Taxonomy")

        # Initialize session state variables with defaults
        if 'pdf_source_type' not in st.session_state:
            st.session_state.pdf_source_type = "URL"
        if 'pdf_url' not in st.session_state:
            st.session_state.pdf_url = "https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf"
        if 'uploaded_file' not in st.session_state:
            st.session_state.uploaded_file = None
        if 'questions' not in st.session_state:
            st.session_state.questions = []
        if 'accepted_questions' not in st.session_state:
            st.session_state.accepted_questions = []

        # API Configuration
        api_key = os.getenv('GEMINI_API_KEY')
        # api_key = st.sidebar.text_input("Enter Gemini API Key", type="password", value=apivalue)
        
        # Main form for PDF and question generation
        with st.form(key='pdf_generation_form'):
            st.header("PDF Source Configuration")
            
            st.session_state.pdf_url = st.text_input(
                    "Enter the URL of the PDF", 
                    value=st.session_state.pdf_url, 
                    key="pdf_url_input"
                )
            
            st.markdown("<h3 style='text-align: center;'>OR</h3>", unsafe_allow_html=True)

            st.session_state.uploaded_file = st.file_uploader(
                    "Upload a PDF file", 
                    type=['pdf'], 
                    key="pdf_file_upload"
            )

            # Bloom's Taxonomy Weights
            st.subheader("Adjust Bloom's Taxonomy Weights")
            col1, col2, col3 = st.columns(3)

            with col1:
                knowledge = st.slider("Knowledge: Remembering information", 0, 100, 20, key='knowledge_slider')
                application = st.slider("Application: Using abstractions in concrete situations", 0, 100, 20, key='application_slider')

            with col2:
                comprehension = st.slider("Comprehension: Explaining the meaning of information", 0, 100, 20, key='comprehension_slider')
                analysis = st.slider("Analysis: Breaking down a whole into component parts", 0, 100, 20, key='analysis_slider')

            with col3:
                synthesis = st.slider("Synthesis: Putting parts together to form a new and integrated whole", 0, 100, 10, key='synthesis_slider')
                evaluation = st.slider("Evaluation: Making and defending judgments based on internal evidence or external criteria", 0, 100, 10, key='evaluation_slider')

            # Collect the Bloom's Taxonomy weights
            bloom_taxonomy_weights = {
                "Knowledge": knowledge,
                "Comprehension": comprehension,
                "Application": application,
                "Analysis": analysis,
                "Synthesis": synthesis,
                "Evaluation": evaluation
            }

            # Number of questions
            num_questions = st.slider("How many questions would you like to generate?", min_value=1, max_value=20, value=5, key='num_questions_slider')

            # Submit button within the form
            submit_button = st.form_submit_button(label='Generate Questions')

        # Process form submission
        if submit_button:
            # Validate API key
            if not api_key:
                st.error("Please enter a valid Gemini API key.")
            # Validate PDF source
            elif not st.session_state.pdf_url and not st.session_state.uploaded_file:
                st.error("Please enter a PDF URL or upload a PDF file.")
            else:
                # Normalize the Bloom's weights
                normalized_bloom_weights = normalize_bloom_weights(bloom_taxonomy_weights)
                
                st.info("Normalized Bloom's Taxonomy Weights:")
                st.json(normalized_bloom_weights)

                # Role and instructions for the AI
                role_description = "You are a question-generating AI agent, given context and instruction, you need to generate questions from the context."
                response_instructions = "Please generate questions that are clear and relevant to the content of the paper. Generate questions which are separated by new lines, without any numbering or additional context."

                # Generate questions
                with st.spinner('Generating questions...'):
                    st.session_state.questions = process_pdf_and_generate_questions(
                        pdf_source=st.session_state.pdf_url if st.session_state.pdf_url else None, 
                        uploaded_file=st.session_state.uploaded_file if st.session_state.uploaded_file else None,
                        api_key=api_key, 
                        role_description=role_description, 
                        response_instructions=response_instructions, 
                        bloom_taxonomy_weights=normalized_bloom_weights, 
                        num_questions=num_questions
                    )
        if st.session_state.questions:
            st.header("Generated Questions")
            
            # Create a form for question management to prevent reload
            with st.form(key='questions_form'):
                for idx, question in enumerate(st.session_state.questions, 1):
                    cols = st.columns([4, 1])  # Create two columns for radio buttons (Accept, Discard)
                    
                    with cols[0]:
                        st.write(f"Q{idx}: {question}")
                    
                    # Use radio buttons for selection
                    with cols[1]:
                        # Default value is 'Discard', so users can change it to 'Accept'
                        selected_option = st.radio(f"Select an option for Q{idx}", ["Accept", "Discard"], key=f"radio_{idx}", index=1)
                    
                    # Handle radio button state changes
                    if selected_option == "Accept":
                        # Add to accepted questions if 'Accept' is selected
                        if question not in st.session_state.accepted_questions:
                            st.session_state.accepted_questions.append(question)
                    else:
                        # Remove from accepted questions if 'Discard' is selected
                        if question in st.session_state.accepted_questions:
                            st.session_state.accepted_questions.remove(question)

                # Submit button for question selection
                submit_questions = st.form_submit_button("Update Accepted Questions")


            # Show accepted questions
            if st.session_state.accepted_questions:
                st.header("Accepted Questions")
                for q in st.session_state.accepted_questions:
                    st.write(q)

                # Download button for accepted questions
                if st.button("Download Accepted Questions as PDF"):
                    filename = generate_pdf(st.session_state.accepted_questions, filename="accepted_questions.pdf")
                    if filename:
                        with open(filename, "rb") as pdf_file:
                            st.download_button(
                                label="Click to Download PDF",
                                data=pdf_file,
                                file_name="accepted_questions.pdf",
                                mime="application/pdf"
                            )
                        st.success("PDF generated successfully!")
            else:
                st.info("No questions selected yet.")

        # Add some footer information
        st.markdown("---")
        st.markdown("""
        ### About this Tool
        - Generate academic paper questions using Bloom's Taxonomy
        - Customize question generation weights
        - Select and refine generated questions
        - Support for PDF via URL or local upload
        """)
    with tab2:
        st.title("📄 Academic Paper Scorer")
        
        # Add a descriptive subheader
        st.markdown("### Evaluate the Quality of Your Academic Paper")
        
        # Create a styled container for the upload section
        st.markdown("""
        <style>
        .upload-container {
            background-color: #f0f2f6;
            border-radius: 10px;
            padding: 20px;
            border: 2px dashed #4a6cf7;
            text-align: center;
        }
        .score-breakdown {
            background-color: #f8f9fa;
            border-radius: 8px;
            padding: 15px;
            margin-bottom: 15px;
        }
        .score-header {
            font-weight: bold;
            color: #4a6cf7;
            margin-bottom: 10px;
        }
        </style>
        """, unsafe_allow_html=True)
        
        with st.form(key='paper_scorer_form'):
            st.header("Upload Your Academic Paper")
            uploaded_file = st.file_uploader(
                "Choose a PDF file", 
                type=['pdf','jpg','png','jpeg'],
                label_visibility="collapsed"
            )
            # Custom submit button with some styling
            submit_button = st.form_submit_button(
                "Score Paper", 
                use_container_width=True,
                type="primary"
            )

        if submit_button:
            # Calculate total score
            print(uploaded_file.name)
            dummydata = sendtogemini(uploaded_file.name)
            print(dummydata)
            total_score = {'Remembering': 0, 'Understanding': 0, 'Applying': 0, 'Analyzing': 0, 'Evaluating': 0, 'Creating': 0}
            for item in dummydata:
                for category in total_score:
                    total_score[category] += item['score'][category]

            # average_score = total_score / (len(dummydata) * 6 * 10) * 100
            
            # Score display columns
            categories = ['Remembering', 'Understanding', 'Applying', 'Analyzing', 'Evaluating', 'Creating']

            # Create 6 columns in a single row
            cols = st.columns(6)

            # Iterate through categories and populate columns
            for i, category in enumerate(categories):
                with cols[i]:
                    score = round(total_score[category] / (len(dummydata) ),ndigits=3)
                    color = 'green' if score > .7 else 'orange' if score > .4 else 'red'
                    st.markdown(f"""
                    <div class="score-breakdown">
                        <div class="score-header" style="color: {color}">{category}</div>
                        <div style="font-size: 24px; color: {color};">{score}/1</div>
                    </div>
                    """, unsafe_allow_html=True)
            
            with st.expander("Show Detailed Scores", expanded=True):
                for idx, item in enumerate(dummydata, 1):
                    
                    # Question header
                    st.markdown(f'<div class="score-header">Question {idx}: {item["question"]}</div>', unsafe_allow_html=True)
                    
                    # Create columns for score display
                    score_cols = st.columns(6)
                    
                    # Scoring categories
                    categories = ['Remembering', 'Understanding', 'Applying', 'Analyzing', 'Evaluating', 'Creating']
                    
                    for col, category in zip(score_cols, categories):
                        with col:
                            # Determine color based on score
                            score = round(item['score'][category],ndigits=3)
                            color = 'green' if score > .7 else 'orange' if score > .4 else 'red'
                            
                            st.markdown(f"""
                            <div style="text-align: center; 
                                        background-color: #f1f1f1; 
                                        border-radius: 5px; 
                                        padding: 5px; 
                                        margin-bottom: 5px;">
                                <div style="font-weight: bold; color: {color};">{category}</div>
                                <div style="font-size: 18px; color: {color};">{score}/1</div>
                            </div>
                            """, unsafe_allow_html=True)
                    
                    st.markdown('</div>', unsafe_allow_html=True)
                    
                    # Add a separator between questions
                    if idx < len(dummydata):
                        st.markdown('---') 

def predict_with_loaded_model(text):
    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=512)
    input_ids = inputs['input_ids'].to(device)
    model.eval()
    with torch.no_grad():
        outputs = model(input_ids)
        logits = outputs.logits
        probabilities = softmax(logits, dim=-1)
    probabilities = probabilities.squeeze().cpu().numpy()
    # Convert to float and format to 3 decimal places
    class_probabilities = {reverse_mapping[i]: float(f"{prob:.3f}") for i, prob in enumerate(probabilities)}
    return class_probabilities

# def process_document(input_path):
#     return {'Avg_Confidence': 0.9397169561947093, 'String': ['What are the key differences between classification and regression tasks in', 'supervised learning, and how do you determine which algorithm to use for a', 'specific problem?', 'e How does clustering differ from dimensionality reduction, and can you', 'provide real-world examples of where each is applied?', 'What are common evaluation metrics for classification models, and how do', 'precision, recall, and F1-score relate to each other?', 'How do convolutional neural networks (CNNS) and recurrent neural networks', '(RNNS) differ in their architecture and applications?', 'What steps can be taken to identify and mitigate bias in machine learning', 'models, and why is this an important consideration?']}

def process_document(input_path):
    if input_path.lower().endswith(".pdf"):
        doc = DocumentFile.from_pdf(input_path)
        #print(f"Number of pages: {len(doc)}")
    elif input_path.lower().endswith((".jpg", ".jpeg", ".png", ".bmp", ".tiff")):
        doc = DocumentFile.from_images(input_path)
    else:
        raise ValueError("Unsupported file type. Please provide a PDF or an image file.")
    result = modelocr(doc)
    def calculate_average_confidence(result):
        total_confidence = 0
        word_count = 0
        for page in result.pages:
            for block in page.blocks:
                for line in block.lines:
                    for word in line.words:
                        total_confidence += word.confidence
                        word_count += 1
        average_confidence = total_confidence / word_count if word_count > 0 else 0
        return average_confidence
    average_confidence = calculate_average_confidence(result)
    string_result = result.render()
    return {'Avg_Confidence': average_confidence, 'String':string_result.split('\n')}

def sendtogemini(inputpath):
    qw = process_document(inputpath)
    questionset = str(qw['String'])
    # send this prompt to gemini : 
    questionset += """You are given a list of text fragments containing questions fragments extracted by an ocr model. Your task is to:
    # only Merge the question fragments into complete and coherent questions.Don't answer then.
    # Separate each question , start a new question with @ to make them easily distinguishable for further processing."""
    url = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash-latest:generateContent?key={os.getenv('GEMINI_API_KEY')}"
    
    payload = {
            "contents": [
                {
                    "parts": [
                        {"text": questionset}
                    ]
                }
            ]
        }
    headers = {"Content-Type": "application/json"}

    response = requests.post(url, headers=headers, data=json.dumps(payload), timeout=60)
    result = response.json()
    res1 = result.get("candidates", [{}])[0].get("content", {}).get("parts", [{}])[0].get("text", "")
    question = []
    for i in res1.split('\n'):
        i = i.strip()
        if len(i) > 0:
            if i[0] == '@':
                question.append(i[1:].strip())
    data = []
    for i in question:
        d = {}
        d['question'] = i
        d['score'] = predict_with_loaded_model(i)
        data.append(d)
    return data

# Run Streamlit app
if __name__ == "__main__":
    main()