import gradio as gr
import os
import threading
from research_agent import ResearchAgent

lock = threading.Lock()

def invoke(openai_api_key: str, question: str) -> str:
    if not openai_api_key:
        raise gr.Error("OpenAI API Key is required.")
    if not question:
        raise gr.Error("Question is required.")
    
    with lock:
        try:
            agent = ResearchAgent(openai_api_key)
            result = agent.perform_research(question)
            return result
        except Exception as e:
            raise gr.Error(f"Error: {str(e)}")

gr.close_all()

demo = gr.Interface(
    fn=invoke,
    inputs=[
        gr.Textbox(
            label="OpenAI API Key",
            type="password",
            lines=1,
            placeholder="Enter your OpenAI API key"
        ),
        gr.Textbox(
            label="Medical Research Question",
            lines=3,
            placeholder="Enter your medical research question here..."
        )
    ],
    outputs=gr.Markdown(
        label="Research Results",
        show_label=True
    ),
    title="Medical Research Assistant",
    description="""This AI-powered tool helps you research medical questions by:
    1. Analyzing your question to identify key search terms
    2. Searching PubMed Central for relevant articles
    3. Analyzing the content using RAG (Retrieval Augmented Generation)
    4. Providing a comprehensive answer with references to scientific literature
    
    Please enter your medical research question above.""",
    theme="default",
    css=".gradio-container {max-width: 800px; margin: auto;}"
)

demo.launch()