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import gradio as gr
from pathlib import Path
import asyncio
import google.generativeai as genai
import os
import logging
from dotenv import load_dotenv
from typing import Optional, Tuple
from flashcard import FlashcardSet
from chat_agent import (
chat_agent,
ChatDeps,
ChatResponse
)
# Load environment variables
load_dotenv()
genai.configure(api_key=os.environ["GEMINI_API_KEY"])
async def process_message(message: dict, history: list, current_flashcards: Optional[FlashcardSet]) -> Tuple[str, list, Optional[FlashcardSet]]:
"""Process uploaded files and chat messages"""
# Get any text provided with the upload as system prompt
user_text = message.get("text", "").strip()
# Create chat dependencies
deps = ChatDeps(
message=user_text,
current_flashcards=current_flashcards
)
# Handle file uploads
if message.get("files"):
for file_path in message["files"]:
if file_path.endswith('.pdf'):
try:
with open(file_path, "rb") as pdf_file:
deps.pdf_data = pdf_file.read()
deps.system_prompt = user_text if user_text else None
# Let chat agent handle the PDF upload
result = await chat_agent.run("Process this PDF upload", deps=deps)
if result.data.should_generate_flashcards:
# Update current flashcards
current_flashcards = result.data.flashcards
history.append([
f"Uploaded: {Path(file_path).name}" +
(f"\nWith instructions: {user_text}" if user_text else ""),
result.data.response
])
return "", history, current_flashcards
except Exception as e:
error_msg = f"Error processing PDF: {str(e)}"
logging.error(error_msg)
history.append([f"Uploaded: {Path(file_path).name}", error_msg])
return "", history, current_flashcards
else:
history.append([f"Uploaded: {Path(file_path).name}", "Please upload a PDF file."])
return "", history, current_flashcards
# Handle text messages
if user_text:
try:
result = await chat_agent.run(user_text, deps=deps)
# Update flashcards if modified
if result.data.should_modify_flashcards:
current_flashcards = result.data.flashcards
history.append([user_text, result.data.response])
return "", history, current_flashcards
except Exception as e:
error_msg = f"Error processing request: {str(e)}"
logging.error(error_msg)
history.append([user_text, error_msg])
return "", history, current_flashcards
history.append(["", "Please upload a PDF file or send a message."])
return "", history, current_flashcards
async def clear_chat():
"""Reset the conversation and clear current flashcards"""
return None, None, None
# Create Gradio interface
with gr.Blocks(title="PDF Flashcard Generator") as demo:
gr.Markdown("""
# 📚 PDF Flashcard Generator
Upload a PDF document and get AI-generated flashcards to help you study!
You can provide custom instructions along with your PDF upload to guide the flashcard generation.
Powered by Google's Gemini AI
""")
chatbot = gr.Chatbot(
label="Flashcard Generation Chat",
bubble_full_width=False,
show_copy_button=True,
height=600
)
# Session state for flashcards
current_flashcards = gr.State(value=None)
with gr.Row():
chat_input = gr.MultimodalTextbox(
label="Upload PDF or type a message",
placeholder="Drop a PDF file here. You can also add instructions for how the flashcards should be generated...",
file_types=[".pdf", "application/pdf", "pdf"],
show_label=False,
sources=["upload"],
scale=20,
min_width=100
)
clear_btn = gr.Button("🗑️", variant="secondary", scale=1, min_width=50)
chat_input.submit(
fn=process_message,
inputs=[chat_input, chatbot, current_flashcards],
outputs=[chat_input, chatbot, current_flashcards]
)
clear_btn.click(
fn=clear_chat,
inputs=[],
outputs=[chat_input, chatbot, current_flashcards]
)
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
demo.launch(
share=False,
server_name="0.0.0.0",
server_port=7860
)
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