app.py

# import gradio as gr
# import PyPDF2
# import re
# import json
# from typing import List, Dict
# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# import torch
# import tempfile
# import os

# # Initialize the model and tokenizer directly
# print("Loading models... This may take a minute on first run.")

# model_name = "valhalla/t5-small-qg-hl"
# tokenizer = AutoTokenizer.from_pretrained(model_name)
# model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# # Set to evaluation mode and CPU
# model.eval()
# device = torch.device("cpu")
# model.to(device)

# def generate_questions(context: str, answer: str, max_length: int = 128) -> str:
#     """Generate a question using T5 model."""
#     try:
#         # Format: "generate question: <hl> answer <hl> context"
#         input_text = f"generate question: <hl> {answer} <hl> {context}"
        
#         # Tokenize
#         inputs = tokenizer(
#             input_text,
#             return_tensors="pt",
#             max_length=512,
#             truncation=True,
#             padding=True
#         ).to(device)
        
#         # Generate
#         with torch.no_grad():
#             outputs = model.generate(
#                 **inputs,
#                 max_length=max_length,
#                 num_beams=4,
#                 early_stopping=True,
#                 do_sample=True,
#                 temperature=0.7
#             )
        
#         # Decode
#         question = tokenizer.decode(outputs[0], skip_special_tokens=True)
        
#         # Clean up
#         question = re.sub(r'^(question:|q:)', '', question, flags=re.IGNORECASE).strip()
        
#         return question if len(question) > 10 else ""
        
#     except Exception as e:
#         print(f"Error generating question: {e}")
#         return ""

# def extract_text_from_pdf(pdf_file) -> str:
#     """Extract text from uploaded PDF file."""
#     text = ""
#     try:
#         if isinstance(pdf_file, str):
#             pdf_reader = PyPDF2.PdfReader(pdf_file)
#         else:
#             pdf_reader = PyPDF2.PdfReader(pdf_file)
        
#         for page in pdf_reader.pages:
#             page_text = page.extract_text()
#             if page_text:
#                 text += page_text + "\n"
#     except Exception as e:
#         return f"Error reading PDF: {str(e)}"
    
#     return text

# def clean_text(text: str) -> str:
#     """Clean and preprocess extracted text."""
#     # Remove excessive whitespace
#     text = re.sub(r'\s+', ' ', text)
#     # Remove special characters but keep sentence structure
#     text = re.sub(r'[^\w\s.,;!?-]', '', text)
#     return text.strip()

# def chunk_text(text: str, max_chunk_size: int = 512, overlap: int = 50) -> List[str]:
#     """Split text into overlapping chunks for processing."""
#     sentences = re.split(r'(?<=[.!?])\s+', text)
#     chunks = []
#     current_chunk = ""
    
#     for sentence in sentences:
#         if len(current_chunk) + len(sentence) < max_chunk_size:
#             current_chunk += " " + sentence
#         else:
#             if current_chunk:
#                 chunks.append(current_chunk.strip())
#             current_chunk = sentence
    
#     if current_chunk:
#         chunks.append(current_chunk.strip())
    
#     # Add overlap between chunks for context
#     overlapped_chunks = []
#     for i, chunk in enumerate(chunks):
#         if i > 0 and overlap > 0:
#             prev_sentences = chunks[i-1].split('. ')
#             overlap_text = '. '.join(prev_sentences[-2:]) if len(prev_sentences) > 1 else chunks[i-1][-overlap:]
#             chunk = overlap_text + " " + chunk
#         overlapped_chunks.append(chunk)
    
#     return overlapped_chunks

# def generate_qa_pairs(chunk: str, num_questions: int = 2) -> List[Dict[str, str]]:
#     """Generate question-answer pairs from a text chunk."""
#     flashcards = []
    
#     # Skip chunks that are too short
#     words = chunk.split()
#     if len(words) < 20:
#         return []
    
#     try:
#         # Split into sentences to use as answers
#         sentences = [s.strip() for s in chunk.split('. ') if len(s.strip()) > 20]
        
#         if len(sentences) < 1:
#             return []
        
#         # Generate questions for different sentences
#         for i in range(min(num_questions, len(sentences))):
#             answer = sentences[i]
            
#             # Skip very short answers
#             if len(answer.split()) < 3:
#                 continue
            
#             question = generate_questions(chunk, answer)
            
#             if question and question != answer:  # Make sure they're different
#                 flashcards.append({
#                     "question": question,
#                     "answer": answer,
#                     "context": chunk[:200] + "..." if len(chunk) > 200 else chunk
#                 })
                
#     except Exception as e:
#         print(f"Error generating QA: {e}")
    
#     return flashcards

# def process_pdf(pdf_file, questions_per_chunk: int = 2, max_chunks: int = 20):
#     """Main processing function."""
#     if pdf_file is None:
#         return "Please upload a PDF file.", "", "", "Your flashcards will appear here..."
    
#     try:
#         # Extract text
#         yield "📄 Extracting text from PDF...", "", "", "Processing..."
#         raw_text = extract_text_from_pdf(pdf_file)
        
#         if raw_text.startswith("Error"):
#             yield raw_text, "", "", "Error occurred"
#             return
        
#         if len(raw_text.strip()) < 100:
#             yield "PDF appears to be empty or contains no extractable text.", "", "", "Error occurred"
#             return
        
#         # Clean text
#         yield "🧹 Cleaning text...", "", "", "Processing..."
#         cleaned_text = clean_text(raw_text)
        
#         # Chunk text
#         yield "✂️ Chunking text into sections...", "", "", "Processing..."
#         chunks = chunk_text(cleaned_text)
        
#         # Limit chunks for CPU performance
#         chunks = chunks[:max_chunks]
        
#         # Generate flashcards
#         all_flashcards = []
#         total_chunks = len(chunks)
        
#         for i, chunk in enumerate(chunks):
#             progress = f"🎴 Generating flashcards... ({i+1}/{total_chunks} chunks processed)"
#             yield progress, "", "", "Processing..."
            
#             cards = generate_qa_pairs(chunk, questions_per_chunk)
#             all_flashcards.extend(cards)
        
#         if not all_flashcards:
#             yield "Could not generate flashcards from this PDF. Try a PDF with more textual content.", "", "", "No flashcards generated"
#             return
        
#         # Format output
#         yield "✅ Finalizing...", "", "", "Almost done..."
        
#         # Create formatted display
#         display_text = format_flashcards_display(all_flashcards)
        
#         # Create JSON download
#         json_output = json.dumps(all_flashcards, indent=2, ensure_ascii=False)
        
#         # Create Anki/CSV format
#         csv_lines = ["Question,Answer"]
#         for card in all_flashcards:
#             q = card['question'].replace('"', '""')
#             a = card['answer'].replace('"', '""')
#             csv_lines.append(f'"{q}","{a}"')
#         csv_output = "\n".join(csv_lines)
        
#         # FINAL OUTPUT - this updates all components
#         yield "✅ Done! Generated {} flashcards".format(len(all_flashcards)), csv_output, json_output, display_text
        
#     except Exception as e:
#         error_msg = f"Error processing PDF: {str(e)}"
#         print(error_msg)
#         yield error_msg, "", "", error_msg

# def format_flashcards_display(flashcards: List[Dict]) -> str:
#     """Format flashcards for nice display."""
#     lines = [f"## 🎴 Generated {len(flashcards)} Flashcards\n"]
    
#     for i, card in enumerate(flashcards, 1):
#         lines.append(f"### Card {i}")
#         lines.append(f"**Q:** {card['question']}")
#         lines.append(f"**A:** {card['answer']}")
#         lines.append(f"*Context: {card['context'][:100]}...*\n")
#         lines.append("---\n")
    
#     return "\n".join(lines)

# def create_sample_flashcard():
#     """Create a sample flashcard for demo purposes."""
#     sample = [{
#         "question": "What is the capital of France?",
#         "answer": "Paris is the capital and most populous city of France.",
#         "context": "Paris is the capital and most populous city of France..."
#     }]
#     return format_flashcards_display(sample)

# # Custom CSS for better styling
# custom_css = """
# .flashcard-container {
#     border: 2px solid #e0e0e0;
#     border-radius: 10px;
#     padding: 20px;
#     margin: 10px 0;
#     background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
#     color: white;
# }
# .question {
#     font-size: 1.2em;
#     font-weight: bold;
#     margin-bottom: 10px;
# }
# .answer {
#     font-size: 1em;
#     opacity: 0.9;
# }
# """

# # Gradio Interface
# with gr.Blocks(css=custom_css, title="PDF to Flashcards") as demo:
#     gr.Markdown("""
#     # 📚 PDF to Flashcards Generator
    
#     Upload any PDF document and automatically generate study flashcards (Q&A pairs) using AI.
    
#     **Features:**
#     - 🧠 Uses local CPU-friendly AI (no GPU needed)
#     - 📄 Extracts text from any PDF
#     - ✂️ Intelligently chunks content
#     - 🎴 Generates question-answer pairs
#     - 💾 Export to CSV (Anki-compatible) or JSON
    
#     *Note: Processing is done entirely on CPU, so large PDFs may take a few minutes.*
#     """)
    
#     with gr.Row():
#         with gr.Column(scale=1):
#             pdf_input = gr.File(
#                 label="Upload PDF",
#                 file_types=[".pdf"],
#                 type="filepath"
#             )
            
#             with gr.Row():
#                 questions_per_chunk = gr.Slider(
#                     minimum=1,
#                     maximum=5,
#                     value=2,
#                     step=1,
#                     label="Questions per section"
#                 )
#                 max_chunks = gr.Slider(
#                     minimum=5,
#                     maximum=50,
#                     value=20,
#                     step=5,
#                     label="Max sections to process"
#                 )
            
#             process_btn = gr.Button("🚀 Generate Flashcards", variant="primary")
            
#             gr.Markdown("""
#             ### 💡 Tips:
#             - Text-based PDFs work best (scanned images won't work)
#             - Academic papers and articles work great
#             - Adjust "Questions per section" based on content density
#             """)
        
#         with gr.Column(scale=2):
#             status_text = gr.Textbox(
#                 label="Status",
#                 value="Ready to process PDF...",
#                 interactive=False
#             )
            
#             output_display = gr.Markdown(
#                 label="Generated Flashcards",
#                 value="Your flashcards will appear here..."
#             )
    
#     with gr.Row():
#         with gr.Column():
#             csv_output = gr.Textbox(
#                 label="CSV Format (for Anki import)",
#                 lines=10,
#                 visible=True
#             )
#             gr.Markdown("*Copy the CSV content and save as `.csv` file to import into Anki*")
        
#         with gr.Column():
#             json_output = gr.Textbox(
#                 label="JSON Format",
#                 lines=10,
#                 visible=True
#             )
#             gr.Markdown("*Raw JSON data for custom applications*")
    
#     # FIXED: Direct binding without the broken .then() chain
#     process_btn.click(
#         fn=process_pdf,
#         inputs=[pdf_input, questions_per_chunk, max_chunks],
#         outputs=[status_text, csv_output, json_output, output_display]
#     )
    
#     # Example section
#     gr.Markdown("---")
#     gr.Markdown("### 🎯 Example Output Format")
#     gr.Markdown(create_sample_flashcard())

# if __name__ == "__main__":
#     demo.launch()














import gradio as gr
import PyPDF2
import re
import json
from typing import List, Dict
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
import tempfile
import os

# Initialize the model and tokenizer directly
print("Loading models... This may take a minute on first run.")

model_name = "valhalla/t5-small-qg-hl"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Set to evaluation mode and CPU
model.eval()
device = torch.device("cpu")
model.to(device)

def extract_key_phrases(text: str) -> List[str]:
    """Extract potential answer candidates from text."""
    # Look for noun phrases, named entities, and important concepts
    candidates = []
    
    # Pattern for capitalized words/phrases (potential named entities)
    capitalized = re.findall(r'\b[A-Z][a-z]+(?:\s+[A-Z][a-z]+)*\b', text)
    candidates.extend(capitalized[:3])
    
    # Pattern for technical terms or concepts (words with specific patterns)
    # Look for phrases like "the process of X", "the concept of X", etc.
    concept_patterns = [
        r'(?:process|method|technique|approach|concept|theory|principle|system) of ([^,.]{10,50})',
        r'(?:known as|called|termed|referred to as) ([^,.]{5,40})',
        r'(?:is|are|was|were) (\w+(?:\s+\w+){1,4}) (?:that|which|who)',
    ]
    
    for pattern in concept_patterns:
        matches = re.findall(pattern, text, re.IGNORECASE)
        candidates.extend(matches[:2])
    
    # Clean and deduplicate
    candidates = [c.strip() for c in candidates if len(c.strip()) > 5]
    return list(dict.fromkeys(candidates))[:5]  # Remove duplicates, keep order

def generate_questions(context: str, answer: str, question_type: str = "what", max_length: int = 128) -> str:
    """Generate a question using T5 model with specified type."""
    try:
        # Format: "generate question: <hl> answer <hl> context"
        input_text = f"generate question: <hl> {answer} <hl> {context}"
        
        # Tokenize
        inputs = tokenizer(
            input_text,
            return_tensors="pt",
            max_length=512,
            truncation=True,
            padding=True
        ).to(device)
        
        # Generate with different parameters based on question type
        temperature = 0.7 if question_type == "what" else 0.85
        num_beams = 4 if question_type == "what" else 5
        
        # Generate
        with torch.no_grad():
            outputs = model.generate(
                **inputs,
                max_length=max_length,
                num_beams=num_beams,
                early_stopping=True,
                do_sample=True,
                temperature=temperature
            )
        
        # Decode
        question = tokenizer.decode(outputs[0], skip_special_tokens=True)
        
        # Clean up
        question = re.sub(r'^(question:|q:)', '', question, flags=re.IGNORECASE).strip()
        
        # Post-process to improve question quality
        question = improve_question(question, answer, context, question_type)
        
        return question if len(question) > 10 else ""
        
    except Exception as e:
        print(f"Error generating question: {e}")
        return ""

def improve_question(question: str, answer: str, context: str, question_type: str) -> str:
    """Post-process generated questions to improve quality and add variety."""
    
    # Ensure question ends with question mark
    if not question.endswith('?'):
        question = question.rstrip('.') + '?'
    
    # Capitalize first letter
    question = question[0].upper() + question[1:] if question else question
    
    # Try to transform to why/how questions if specified
    if question_type == "why" and not question.lower().startswith("why"):
        # Try to convert to why question
        if re.search(r'\b(is|are|was|were|does|do|did)\b', question, re.IGNORECASE):
            question = create_why_question(question, answer, context)
    
    elif question_type == "how" and not question.lower().startswith("how"):
        # Try to convert to how question
        if re.search(r'\b(does|do|did|can|could)\b', question, re.IGNORECASE):
            question = create_how_question(question, answer, context)
    
    return question

def create_why_question(base_question: str, answer: str, context: str) -> str:
    """Transform or create a 'why' question."""
    
    # Look for causal indicators in the context
    causal_patterns = [
        r'because ([^,.]{10,60})',
        r'due to ([^,.]{10,60})',
        r'as a result of ([^,.]{10,60})',
        r'(?:leads to|causes|results in) ([^,.]{10,60})',
        r'in order to ([^,.]{10,60})'
    ]
    
    for pattern in causal_patterns:
        match = re.search(pattern, context, re.IGNORECASE)
        if match:
            # Extract the subject from context
            subject_match = re.search(r'([A-Z][a-z]+(?:\s+[a-z]+){0,3})\s+(?:is|are|was|were|does|do)', context)
            if subject_match:
                subject = subject_match.group(1)
                return f"Why does {subject.lower()} occur?"
    
    # Fallback: create a generic why question
    # Extract main subject from answer
    words = answer.split()
    if len(words) > 3:
        return f"Why is {' '.join(words[:4])}... important?"
    
    return base_question

def create_how_question(base_question: str, answer: str, context: str) -> str:
    """Transform or create a 'how' question."""
    
    # Look for process indicators
    process_patterns = [
        r'(process|method|procedure|technique|approach) (?:of|for|to) ([^,.]{10,60})',
        r'by ([^,.]{10,60})',
        r'through ([^,.]{10,60})'
    ]
    
    for pattern in process_patterns:
        match = re.search(pattern, context, re.IGNORECASE)
        if match:
            if len(match.groups()) > 1:
                process = match.group(2)
                return f"How does {process.lower()} work?"
            else:
                process = match.group(1)
                return f"How is {process.lower()} achieved?"
    
    # Fallback: create a generic how question
    verbs = re.findall(r'\b(works?|functions?|operates?|performs?|executes?)\b', context, re.IGNORECASE)
    if verbs:
        subject_match = re.search(r'([A-Z][a-z]+(?:\s+[a-z]+){0,3})\s+' + verbs[0], context, re.IGNORECASE)
        if subject_match:
            subject = subject_match.group(1)
            return f"How does {subject.lower()} {verbs[0].lower()}?"
    
    return base_question

def extract_text_from_pdf(pdf_file) -> str:
    """Extract text from uploaded PDF file."""
    text = ""
    try:
        if isinstance(pdf_file, str):
            pdf_reader = PyPDF2.PdfReader(pdf_file)
        else:
            pdf_reader = PyPDF2.PdfReader(pdf_file)
        
        for page in pdf_reader.pages:
            page_text = page.extract_text()
            if page_text:
                text += page_text + "\n"
    except Exception as e:
        return f"Error reading PDF: {str(e)}"
    
    return text

def clean_text(text: str) -> str:
    """Clean and preprocess extracted text."""
    # Remove excessive whitespace
    text = re.sub(r'\s+', ' ', text)
    # Remove special characters but keep sentence structure
    text = re.sub(r'[^\w\s.,;!?-]', '', text)
    return text.strip()

def chunk_text(text: str, max_chunk_size: int = 512, overlap: int = 50) -> List[str]:
    """Split text into overlapping chunks for processing."""
    sentences = re.split(r'(?<=[.!?])\s+', text)
    chunks = []
    current_chunk = ""
    
    for sentence in sentences:
        if len(current_chunk) + len(sentence) < max_chunk_size:
            current_chunk += " " + sentence
        else:
            if current_chunk:
                chunks.append(current_chunk.strip())
            current_chunk = sentence
    
    if current_chunk:
        chunks.append(current_chunk.strip())
    
    # Add overlap between chunks for context
    overlapped_chunks = []
    for i, chunk in enumerate(chunks):
        if i > 0 and overlap > 0:
            prev_sentences = chunks[i-1].split('. ')
            overlap_text = '. '.join(prev_sentences[-2:]) if len(prev_sentences) > 1 else chunks[i-1][-overlap:]
            chunk = overlap_text + " " + chunk
        overlapped_chunks.append(chunk)
    
    return overlapped_chunks

def generate_qa_pairs(chunk: str, num_questions: int = 3) -> List[Dict[str, str]]:
    """Generate question-answer pairs from a text chunk with variety."""
    flashcards = []
    
    # Skip chunks that are too short
    words = chunk.split()
    if len(words) < 20:
        return []
    
    try:
        # Extract key phrases for answers
        key_phrases = extract_key_phrases(chunk)
        
        # Also use sentences as potential answers
        sentences = [s.strip() for s in chunk.split('. ') if len(s.strip()) > 20]
        
        # Combine both sources
        answer_candidates = key_phrases + sentences[:2]
        
        if len(answer_candidates) < 1:
            return []
        
        # Define question types to generate
        question_types = ["what", "why", "how"]
        
        # Generate diverse questions
        questions_generated = 0
        for i, answer in enumerate(answer_candidates):
            if questions_generated >= num_questions:
                break
            
            # Skip very short answers
            if len(answer.split()) < 3:
                continue
            
            # Cycle through question types
            q_type = question_types[i % len(question_types)]
            
            question = generate_questions(chunk, answer, question_type=q_type)
            
            if question and question != answer:  # Make sure they're different
                flashcards.append({
                    "question": question,
                    "answer": answer,
                    "context": chunk[:200] + "..." if len(chunk) > 200 else chunk,
                    "type": q_type
                })
                questions_generated += 1
                
    except Exception as e:
        print(f"Error generating QA: {e}")
    
    return flashcards

def process_pdf(pdf_file, questions_per_chunk: int = 3, max_chunks: int = 20):
    """Main processing function."""
    if pdf_file is None:
        return "Please upload a PDF file.", "", "", "Your flashcards will appear here..."
    
    try:
        # Extract text
        yield "📄 Extracting text from PDF...", "", "", "Processing..."
        raw_text = extract_text_from_pdf(pdf_file)
        
        if raw_text.startswith("Error"):
            yield raw_text, "", "", "Error occurred"
            return
        
        if len(raw_text.strip()) < 100:
            yield "PDF appears to be empty or contains no extractable text.", "", "", "Error occurred"
            return
        
        # Clean text
        yield "🧹 Cleaning text...", "", "", "Processing..."
        cleaned_text = clean_text(raw_text)
        
        # Chunk text
        yield "✂️ Chunking text into sections...", "", "", "Processing..."
        chunks = chunk_text(cleaned_text)
        
        # Limit chunks for CPU performance
        chunks = chunks[:max_chunks]
        
        # Generate flashcards
        all_flashcards = []
        total_chunks = len(chunks)
        
        for i, chunk in enumerate(chunks):
            progress = f"🎴 Generating flashcards... ({i+1}/{total_chunks} chunks processed)"
            yield progress, "", "", "Processing..."
            
            cards = generate_qa_pairs(chunk, questions_per_chunk)
            all_flashcards.extend(cards)
        
        if not all_flashcards:
            yield "Could not generate flashcards from this PDF. Try a PDF with more textual content.", "", "", "No flashcards generated"
            return
        
        # Format output
        yield "✅ Finalizing...", "", "", "Almost done..."
        
        # Create formatted display
        display_text = format_flashcards_display(all_flashcards)
        
        # Create JSON download
        json_output = json.dumps(all_flashcards, indent=2, ensure_ascii=False)
        
        # Create Anki/CSV format
        csv_lines = ["Question,Answer,Type"]
        for card in all_flashcards:
            q = card['question'].replace('"', '""')
            a = card['answer'].replace('"', '""')
            t = card.get('type', 'what')
            csv_lines.append(f'"{q}","{a}","{t}"')
        csv_output = "\n".join(csv_lines)
        
        # FINAL OUTPUT - this updates all components
        stats = f"✅ Done! Generated {len(all_flashcards)} flashcards ("
        types_count = {}
        for card in all_flashcards:
            t = card.get('type', 'what')
            types_count[t] = types_count.get(t, 0) + 1
        stats += ", ".join([f"{count} {qtype}" for qtype, count in types_count.items()]) + ")"
        
        yield stats, csv_output, json_output, display_text
        
    except Exception as e:
        error_msg = f"Error processing PDF: {str(e)}"
        print(error_msg)
        yield error_msg, "", "", error_msg

def format_flashcards_display(flashcards: List[Dict]) -> str:
    """Format flashcards for nice display."""
    lines = [f"## 🎴 Generated {len(flashcards)} Flashcards\n"]
    
    # Count by type
    types_count = {}
    for card in flashcards:
        t = card.get('type', 'what')
        types_count[t] = types_count.get(t, 0) + 1
    
    lines.append(f"**Breakdown:** {', '.join([f'{count} {qtype.upper()}' for qtype, count in types_count.items()])}\n")
    lines.append("---\n")
    
    for i, card in enumerate(flashcards, 1):
        qtype = card.get('type', 'what').upper()
        emoji = "❓" if qtype == "WHAT" else "🤔" if qtype == "WHY" else "🔧"
        
        lines.append(f"### {emoji} Card {i} - {qtype}")
        lines.append(f"**Q:** {card['question']}")
        lines.append(f"**A:** {card['answer']}")
        lines.append(f"*Context: {card['context'][:100]}...*\n")
        lines.append("---\n")
    
    return "\n".join(lines)

def create_sample_flashcard():
    """Create a sample flashcard for demo purposes."""
    sample = [
        {
            "question": "What is photosynthesis?",
            "answer": "Photosynthesis is the process by which plants convert sunlight into energy.",
            "context": "Photosynthesis is the process by which plants convert sunlight into energy...",
            "type": "what"
        },
        {
            "question": "Why do plants need chlorophyll?",
            "answer": "Chlorophyll absorbs light energy needed for photosynthesis.",
            "context": "Chlorophyll absorbs light energy needed for photosynthesis...",
            "type": "why"
        },
        {
            "question": "How do plants convert light into chemical energy?",
            "answer": "Through the process of photosynthesis in the chloroplasts.",
            "context": "Through the process of photosynthesis in the chloroplasts...",
            "type": "how"
        }
    ]
    return format_flashcards_display(sample)

# Custom CSS for better styling
custom_css = """
.flashcard-container {
    border: 2px solid #e0e0e0;
    border-radius: 10px;
    padding: 20px;
    margin: 10px 0;
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
    color: white;
}
.question {
    font-size: 1.2em;
    font-weight: bold;
    margin-bottom: 10px;
}
.answer {
    font-size: 1em;
    opacity: 0.9;
}
"""

# Gradio Interface
with gr.Blocks(css=custom_css, title="PDF to Flashcards") as demo:
    gr.Markdown("""
    # 📚 PDF to Flashcards Generator (Enhanced)
    
    Upload any PDF document and automatically generate study flashcards with **What, Why, and How** questions using AI.
    
    **✨ New Features:**
    - 🎯 Generates **What** questions (factual)
    - 🤔 Generates **Why** questions (reasoning)
    - 🔧 Generates **How** questions (process)
    - 📊 Improved question quality and variety
    - 🧠 Better answer extraction
    
    **Core Features:**
    - 🧠 Uses local CPU-friendly AI (no GPU needed)
    - 📄 Extracts text from any PDF
    - ✂️ Intelligently chunks content
    - 🎴 Generates diverse question-answer pairs
    - 💾 Export to CSV (Anki-compatible) or JSON
    
    *Note: Processing is done entirely on CPU, so large PDFs may take a few minutes.*
    """)
    
    with gr.Row():
        with gr.Column(scale=1):
            pdf_input = gr.File(
                label="Upload PDF",
                file_types=[".pdf"],
                type="filepath"
            )
            
            with gr.Row():
                questions_per_chunk = gr.Slider(
                    minimum=1,
                    maximum=6,
                    value=3,
                    step=1,
                    label="Questions per section"
                )
                max_chunks = gr.Slider(
                    minimum=5,
                    maximum=50,
                    value=20,
                    step=5,
                    label="Max sections to process"
                )
            
            process_btn = gr.Button("🚀 Generate Flashcards", variant="primary")
            
            gr.Markdown("""
            ### 💡 Tips:
            - Text-based PDFs work best (scanned images won't work)
            - Academic papers and articles work great
            - Adjust "Questions per section" for more variety
            - Higher questions per section = more Why/How questions
            """)
        
        with gr.Column(scale=2):
            status_text = gr.Textbox(
                label="Status",
                value="Ready to process PDF...",
                interactive=False
            )
            
            output_display = gr.Markdown(
                label="Generated Flashcards",
                value="Your flashcards will appear here..."
            )
    
    with gr.Row():
        with gr.Column():
            csv_output = gr.Textbox(
                label="CSV Format (for Anki import)",
                lines=10,
                visible=True
            )
            gr.Markdown("*Copy the CSV content and save as `.csv` file to import into Anki*")
        
        with gr.Column():
            json_output = gr.Textbox(
                label="JSON Format",
                lines=10,
                visible=True
            )
            gr.Markdown("*Raw JSON data for custom applications*")
    
    # Direct binding
    process_btn.click(
        fn=process_pdf,
        inputs=[pdf_input, questions_per_chunk, max_chunks],
        outputs=[status_text, csv_output, json_output, output_display]
    )
    
    # Example section
    gr.Markdown("---")
    gr.Markdown("### 🎯 Example Output Format")
    gr.Markdown(create_sample_flashcard())

if __name__ == "__main__":
    demo.launch()