app.py

# app.py
import os, pathlib, pandas as pd, gradio as gr
from agents import run_pipeline
from files_process import prepare_input_arg, load_input_text  # load_input_text not used here but handy

def _scores_to_df(result_json: dict) -> pd.DataFrame:
    rows = result_json.get("scores", []) or []
    df = pd.DataFrame(rows)
    # Drop justification from table
    if "justification" in df.columns:
        df = df.drop(columns=["justification"])
    cols = ["agent", "clinical_completeness", "ai_rigor",
            "trial_framing", "privacy_regulatory", "clarity_structure"]
    for c in cols:
        if c not in df.columns:
            df[c] = None
    return df[cols]

def _winner_md_block(result_json: dict) -> str:
    winner = result_json.get("winner", "") or "N/A"
    just = ""
    for r in result_json.get("scores", []):
        if r.get("agent") == winner:
            just = r.get("justification", "") or ""
            break
    if just:
        return f"### 🏆 Winner: **{winner}**\n\n> *{just}*"
    return f"### 🏆 Winner: **{winner}**"

def run_ui(text_in, file_in, oai_model, gem_model, ds_model):
    try:
        input_arg = prepare_input_arg(text_in, file_in)
        result_json = run_pipeline(
            input_arg,
            oai_model=oai_model,
            gem_model=gem_model,
            ds_model=ds_model,
        )

        # Read agent drafts saved by run_pipeline
        p1, p2, p3 = [pathlib.Path(f"agent{i}.md") for i in range(1, 4)]
        agent1_md = p1.read_text(encoding="utf-8") if p1.exists() else "*agent1.md not found*"
        agent2_md = p2.read_text(encoding="utf-8") if p2.exists() else "*agent2.md not found*"
        agent3_md = p3.read_text(encoding="utf-8") if p3.exists() else "*agent3.md not found*"

        scores_df = _scores_to_df(result_json)
        winner_md = _winner_md_block(result_json)

        return (
            agent1_md, agent2_md, agent3_md,
            scores_df, winner_md,
            str(p1) if p1.exists() else None,
            str(p2) if p2.exists() else None,
            str(p3) if p3.exists() else None
        )
    except Exception as e:
        # Keep output shapes consistent
        return f"**Error:** {e}", "", "", pd.DataFrame(), "", None, None, None

with gr.Blocks(title="Healthcare–AI Case Studies (3 Agents + Manager)") as demo:
    gr.Markdown("# Healthcare–AI Case Studies\nProvide text or upload a .txt/.docx/.pdf, then click **Run**.")

    with gr.Accordion("Models (optional)", open=False):
        m1 = gr.Textbox(value="gpt-4o-mini", label="Agent 1 (OpenAI)")
        m2 = gr.Textbox(value="gpt-4.1-nano", label="Agent 2 (style-2)")
        m3 = gr.Textbox(value="gpt-4.1-mini", label="Agent 3 (style-3)")

    gr.Markdown("### Manager Scores")
    scores_df = gr.Dataframe(label="Scores (justification hidden)")

    winner_md = gr.Markdown(label="Winner & rationale")

    gr.Markdown("### Download agent drafts")
    with gr.Row():
        dl1 = gr.DownloadButton(label="Download agent1.md")
        dl2 = gr.DownloadButton(label="Download agent2.md")
        dl3 = gr.DownloadButton(label="Download agent3.md")

    gr.Markdown("### Input")
    with gr.Row():
        txt = gr.Textbox(lines=10, label="Paste source text (optional)")
        fil = gr.File(label="Upload .txt / .docx / .pdf (optional)", file_count="single",
                      file_types=["text", ".docx", ".pdf"])
    run_btn = gr.Button("Run")

    gr.Markdown("### Agent Drafts (expand to view)")
    with gr.Accordion("Agent outputs", open=False):
        with gr.Row():
            a1_md = gr.Markdown(label="Agent 1 draft")
            a2_md = gr.Markdown(label="Agent 2 draft")
            a3_md = gr.Markdown(label="Agent 3 draft")

    run_btn.click(
        fn=run_ui,
        inputs=[txt, fil, m1, m2, m3],
        outputs=[a1_md, a2_md, a3_md, scores_df, winner_md, dl1, dl2, dl3],
    )

# On Spaces, it's enough to expose `demo`; running locally calls launch().
if __name__ == "__main__":
    demo.launch()