app.py

import gradio as gr
from agents import scout, hypothesis, simulation, publishing, meta_learning

def run_pipeline(query):
    papers = scout.search_and_summarize(query)
    titles = [p['title'] for p in papers]
    summaries = [p['summary'] for p in papers]
    hypo = hypothesis.generate(papers)
    sim_result = simulation.simulate(hypo)
    report = publishing.save_report(hypo, sim_result)
    logs = meta_learning.log(hypo, sim_result)
    return titles, summaries, hypo, sim_result, report, logs.to_markdown()

with gr.Blocks() as demo:
    gr.Markdown("# 🤖 EurekaCrew: Self‑Evolving AI R&D Lab")
    query_in = gr.Textbox(label="Enter AI research topic")
    run_btn = gr.Button("Run EurekaCrew")
    titles_out = gr.JSON(label="Top matching paper titles")
    summaries_out = gr.JSON(label="Paper summaries")
    hypo_out = gr.Textbox(label="Generated Hypothesis")
    sim_out = gr.Textbox(label="Simulation Result")
    report_out = gr.File(label="Markdown Report")
    logs_out = gr.Markdown(label="Recent Logs")

    run_btn.click(run_pipeline, inputs=[query_in],
                  outputs=[titles_out, summaries_out, hypo_out, sim_out, report_out, logs_out])

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