Update app.py

app.py

@@ -1,153 +1,65 @@
-from __future__ import annotations
-
+# app.py
 import os
-import json
-import 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[str, str, str, str | None, dict]:
-    """
-    Orchestrates deep research + optional post-processing + optional graph preview.
-    Returns: (final_markdown, citations_markdown, json_blob, graph_html, state)
-    """
-    out = await research_once(query, fast=fast, rerank_model=DEFAULT_RERANK_MODEL)
-
-    # Optional polish (Gemini/DeepSeek) — never add 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,
-        )
-
-    # Keep state for TTS / graph writer
-    state = state or {}
-    state["final_text"] = out.get("final_output") or ""
-    state["citations"] = out.get("citations", [])
-    state["query"] = query
-
-    # Graph preview HTML
-    graph_html = build_preview_graph_html(state["citations"]) if want_graph else None
+from genesis.narration import narrate_text
 
-    # Report
-    final_md = state["final_text"] if state["final_text"] else "_No output_"
+# Preloaded demo queries for tomorrow
+demo_queries = [
+    "Designing synthetic gene circuits for targeted cancer therapy",
+    "CRISPR-based microbial engineering for biofuel production",
+    "Synthetic biology approaches to carbon capture and sequestration",
+    "Engineering probiotics for mental health treatments",
+    "DNA-based nanorobots for targeted drug delivery"
+]
 
-    # Citations list (robust string building)
-    cite_lines: list[str] = []
-    for c in state["citations"]:
-        title = c.get("title") or "link"
-        url = c.get("url") or ""
-        cite_lines.append(f"- [{title}]({url})")
-    cites_md = "\n".join(cite_lines) if cite_lines else "_None detected_"
+def run_pipeline_ui(query):
+    """Run the full synthetic biology research pipeline."""
+    report, citations, structures, graph_status, image_url = research_once(query)
 
-    # JSON export
-    json_blob = json.dumps(out, indent=2)
+    audio_path = narrate_text(report) or "Narration not available"
 
-    return final_md, cites_md, json_blob, graph_html, state
+    cites_md = "### Citations\n"
+    for cite in citations:
+        cites_md += f"- [{cite['type']} {cite['id']}]({cite['url']})\n"
 
+    struct_md = "### Molecular Structures\n"
+    for s in structures:
+        struct_md += f"- {s['term']}: [{s['pdb_id']}]({s['pdbe_url']}) ([RCSB]({s['rcsb_url']}))\n"
 
-async def do_tts(state: dict) -> tuple[str | None, str]:
-    """Narrate the high-level summary via ElevenLabs."""
-    text = (state or {}).get("final_text") or ""
-    if not text.strip():
-        return None, "Nothing to narrate yet — run research first."
+    img_md = "### Generated Visual\n"
+    if image_url:
+        img_md += f"![Diagram]({image_url})\n"
 
-    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."
+    return report, cites_md, struct_md, graph_status, audio_path, img_md
 
-    suffix = ".mp3" if (mime or "").find("mpeg") >= 0 else ".wav"
-    with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as f:
-        f.write(audio_bytes)
-        path = f.name
-    return path, "Narration ready."
-
-
-async def do_graph_write(state: dict) -> str:
-    """Write Topic -> Paper knowledge graph into Neo4j."""
-    topic = (state or {}).get("query") or "Untitled Topic"
-    citations = (state or {}).get("citations") or []
-    if not citations:
-        return "No citations present — run research first."
-    counts = await write_topic_and_papers(topic, citations)
-    return f"Wrote to Neo4j: nodes={counts.get('nodes',0)}, rels={counts.get('rels',0)}"
-
-
-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.Blocks(title="GENESIS-AI: Synthetic Biology Deep Research") as demo:
+    gr.Markdown("# 🧬 GENESIS-AI\nThe most advanced synthetic biology research assistant ever built.")
+    
     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],
+        query_box = gr.Dropdown(choices=demo_queries, label="Select a Research Topic", value=demo_queries[0])
+        run_btn = gr.Button("🚀 Run Research")
+
+    with gr.Tab("📄 Report"):
+        report_out = gr.Textbox(label="Full Research Report", lines=20)
+    with gr.Tab("📚 Citations"):
+        cites_out = gr.Markdown()
+    with gr.Tab("🔬 Structures"):
+        struct_out = gr.Markdown()
+    with gr.Tab("🕸 Graph"):
+        graph_status_out = gr.Textbox(label="Graph DB Status")
+    with gr.Tab("🔊 Narration"):
+        audio_out = gr.Audio(label="Narrated Summary")
+    with gr.Tab("🖼 Visual Diagram"):
+        img_out = gr.Markdown()
+
+    run_btn.click(
+        fn=run_pipeline_ui,
+        inputs=[query_box],
+        outputs=[report_out, cites_out, struct_out, graph_status_out, audio_out, img_out]
     )
 
-    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()