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import gradio as gr
from pathlib import Path
import asyncio
import google.generativeai as genai
from flashcard import (
generate_flashcards_from_pdf,
FlashcardSet
)
import os
from dotenv import load_dotenv
import tempfile
# Load environment variables
load_dotenv()
genai.configure(api_key=os.environ["GEMINI_API_KEY"])
# Store the current flashcard set in memory
current_flashcards = None
def create_flashcard_text(flashcards: FlashcardSet) -> str:
"""Format flashcard output as a readable string"""
output = [f"π Generated {flashcards.total_cards} flashcards about: {flashcards.topic}\n"]
for i, card in enumerate(flashcards.cards, 1):
output.append(f"\n--- Flashcard {i} (Difficulty: {'β' * card.difficulty}) ---")
output.append(f"Q: {card.question}")
output.append(f"A: {card.answer}")
output.append("\n\nYou can ask me to:")
output.append("β’ Modify specific flashcards")
output.append("β’ Generate more flashcards")
output.append("β’ Change difficulty levels")
output.append("β’ Export to Anki")
return "\n".join(output)
async def handle_modification_request(text: str, flashcards: FlashcardSet) -> str:
"""Handle user requests to modify flashcards"""
model = genai.GenerativeModel('gemini-pro')
# Create a context-aware prompt
prompt = f"""Given the following flashcards and user request, suggest how to modify the flashcards.
Current flashcards:
{create_flashcard_text(flashcards)}
User request: {text}
Please provide specific suggestions for modifications."""
response = await model.generate_content_async(prompt)
return response.text
async def process_message(message: dict, history: list) -> tuple[str, list]:
"""Process uploaded files and chat messages"""
global current_flashcards
# Handle file uploads
if message.get("files"):
for file_path in message["files"]:
if file_path.endswith('.pdf'):
try:
current_flashcards = await async_process_pdf(file_path)
response = create_flashcard_text(current_flashcards)
return "", history + [
{"role": "user", "content": f"Uploaded: {Path(file_path).name}"},
{"role": "assistant", "content": response}
]
except Exception as e:
error_msg = f"Error processing PDF: {str(e)}"
return "", history + [
{"role": "user", "content": f"Uploaded: {Path(file_path).name}"},
{"role": "assistant", "content": error_msg}
]
else:
return "", history + [
{"role": "user", "content": f"Uploaded: {Path(file_path).name}"},
{"role": "assistant", "content": "Please upload a PDF file."}
]
# Handle text messages
if message.get("text"):
user_message = message["text"].strip()
# If we have flashcards and user is asking for modifications
if current_flashcards:
try:
modification_response = await handle_modification_request(user_message, current_flashcards)
return "", history + [
{"role": "user", "content": user_message},
{"role": "assistant", "content": modification_response}
]
except Exception as e:
error_msg = f"Error processing request: {str(e)}"
return "", history + [
{"role": "user", "content": user_message},
{"role": "assistant", "content": error_msg}
]
else:
return "", history + [
{"role": "user", "content": user_message},
{"role": "assistant", "content": "Please upload a PDF file first to generate flashcards."}
]
return "", history + [
{"role": "assistant", "content": "Please upload a PDF file or send a message."}
]
def export_to_anki(flashcards: FlashcardSet) -> str:
"""Convert flashcards to Anki-compatible tab-separated format and save to file"""
if not flashcards:
return None
# Create a temporary file
with tempfile.NamedTemporaryFile(mode='w', suffix='.txt', delete=False) as f:
f.write("#separator:tab\n")
f.write("#html:true\n")
f.write("#columns:Question\tAnswer\tTags\n")
for card in flashcards.cards:
question = card.question.replace('\n', '<br>')
answer = card.answer.replace('\n', '<br>')
tags = f"difficulty_{card.difficulty} {flashcards.topic.replace(' ', '_')}"
f.write(f"{question}\t{answer}\t{tags}\n")
return f.name
async def async_process_pdf(pdf_path: str) -> FlashcardSet:
"""Asynchronously process the PDF file"""
return await generate_flashcards_from_pdf(pdf_path=pdf_path)
# Create Gradio interface
with gr.Blocks(title="PDF Flashcard Generator") as demo:
gr.Markdown("""
# π PDF Flashcard Generator
Upload a PDF document and get AI-generated flashcards to help you study!
Powered by Google's Gemini AI
""")
chatbot = gr.Chatbot(
label="Flashcard Generation Chat",
bubble_full_width=False,
show_copy_button=True,
height=600
)
chat_input = gr.MultimodalTextbox(
label="Upload PDF or type a message",
placeholder="Drop a PDF file here or type a message to modify flashcards...",
file_types=["pdf", "application/pdf"],
show_label=False,
sources=["upload", "microphone"]
)
# Add clear button for better UX
clear_button = gr.Button("Clear Chat")
chat_input.change(
fn=process_message,
inputs=[chat_input, chatbot],
outputs=[chat_input, chatbot]
)
# Add clear functionality
clear_button.click(
lambda: (None, None),
outputs=[chat_input, chatbot]
)
if __name__ == "__main__":
demo.launch(
share=False,
server_name="0.0.0.0",
server_port=7860,
allowed_paths=["."]
)
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