app.py

from __future__ import annotations
import os, json, asyncio, tempfile
import gradio as gr
from dotenv import load_dotenv

load_dotenv()

from genesis.pipeline import research_once
from genesis.providers import postprocess_summary, synthesize_tts
from genesis.graph import build_preview_graph_html
from genesis.graphdb import write_topic_and_papers

APP_TITLE = "GENESIS-AI — Synthetic Biology Deep Research (Safety-First)"
APP_DESC = (
    "High-level synthetic biology literature synthesis with citations. "
    "This app **never** produces operational protocols."
)

DEFAULT_POST = os.getenv("POSTPROCESSOR_DEFAULT", "none").lower()
DEFAULT_RERANK_MODEL = os.getenv("RERANK_MODEL", "mixedbread-ai/mxbai-rerank-large-v1")

async def run_pipeline(query: str, fast: bool, postprocessor: str, want_graph: bool, state: dict) -> tuple:
    out = await research_once(query, fast=fast, rerank_model=DEFAULT_RERANK_MODEL)

    # Optional post-processing (Gemini/DeepSeek) for polish ONLY (no lab steps)
    if postprocessor and postprocessor != "none":
        out["final_output"] = await postprocess_summary(
            base_text=out.get("final_output") or "",
            citations=out.get("citations", []),
            engine=postprocessor,
        )

    # Save into state for follow-ups (TTS, Graph Writer)
    state["final_text"] = out.get("final_output") or ""
    state["citations"] = out.get("citations", [])
    state["query"] = query

    # Optional graph preview
    graph_html = build_preview_graph_html(state["citations"]) if want_graph else None

    final_md = state["final_text"] or "_No output_"
    cites_list = [f"- [{c.get('title','link')}]({c.get('url','')})" for c in state["citations"]]
    cites_md = "
".join(cites_list) if cites_list else "_None detected_"
    json_blob = json.dumps(out, indent=2)
    return final_md, cites_md, json_blob, graph_html, state

async def do_tts(state: dict) -> tuple:
    text = (state or {}).get("final_text") or ""
    if not text.strip():
        return None, "Nothing to narrate yet — run research first."
    try:
        audio_bytes, mime = await synthesize_tts(text)
        if not audio_bytes:
            return None, "TTS not configured or failed. Ensure ELEVEN_LABS_API_KEY/VOICE_ID are set."
        suffix = ".mp3" if "mpeg" in (mime or "") else ".wav"
        with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as f:
            f.write(audio_bytes)
            path = f.name
        return path, "Narration ready."
    except Exception as e:
        return None, f"TTS error: {e}"

async def do_graph_write(state: dict) -> str:
    topic = (state or {}).get("query") or "Untitled Topic"
    citations = (state or {}).get("citations") or []
    if not citations:
        return "No citations present — run research first."
    try:
        counts = await write_topic_and_papers(topic, citations)
        return f"Wrote to Neo4j: nodes={counts.get('nodes',0)}, rels={counts.get('rels',0)}"
    except Exception as e:
        return f"Neo4j write error: {e}. Ensure NEO4J_* env vars are set."

with gr.Blocks(theme=gr.themes.Soft(), fill_height=True) as demo:
    gr.Markdown(f"# {APP_TITLE}")
    gr.Markdown(APP_DESC)

    state = gr.State({"final_text": "", "citations": [], "query": ""})

    with gr.Row():
        query = gr.Textbox(
            label="Your high-level research request",
            lines=5,
            placeholder=(
                "e.g., High-level synthesis of CRISPR base-editing trends in oncology (last 2 years). "
                "Summarize mechanisms, targets, ethics, and provide citations."
            ),
        )
    with gr.Row():
        fast = gr.Checkbox(label="Fast mode (o4-mini-deep-research)", value=False)
        post = gr.Dropdown(
            label="Post-processor",
            choices=["none", "gemini", "deepseek"],
            value=DEFAULT_POST,
            allow_custom_value=False,
        )
        want_graph = gr.Checkbox(label="Build graph preview", value=False)
    go = gr.Button("Run Deep Research", variant="primary")

    with gr.Tabs():
        with gr.Tab("Research Report"):
            report = gr.Markdown()
        with gr.Tab("Citations"):
            citations = gr.Markdown()
        with gr.Tab("JSON Export"):
            json_out = gr.Code(language="json")
        with gr.Tab("Graph Preview"):
            graph_html = gr.HTML()
        with gr.Tab("Graph Writer (Neo4j)"):
            write_btn = gr.Button("Write Topic & Papers to Neo4j", variant="secondary")
            write_status = gr.Markdown()
        with gr.Tab("Narration (ElevenLabs)"):
            tts_btn = gr.Button("Narrate Summary", variant="secondary")
            tts_audio = gr.Audio(label="Narration", autoplay=False)
            tts_status = gr.Markdown()

    go.click(
        fn=run_pipeline,
        inputs=[query, fast, post, want_graph, state],
        outputs=[report, citations, json_out, graph_html, state],
    )

    tts_btn.click(fn=do_tts, inputs=[state], outputs=[tts_audio, tts_status])
    write_btn.click(fn=do_graph_write, inputs=[state], outputs=[write_status])

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