Create app.py

app.py

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+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()