Update app.py

app.py

@@ -1,139 +1,91 @@
-# app.py
 import gradio as gr
 from genesis.pipeline import research_once
 from genesis.visualization import generate_pathway_graph, generate_funding_network
 
-# Demo queries for fast testing
+# Preloaded killer demo queries
 DEMO_QUERIES = [
-    "Map all CRISPR-based living therapeutics in clinical trials since 2020",
-    "Graph metabolic engineering pathways for bio-based drug production",
-    "Synthetic biology startups developing oncolytic viruses — funding + trials",
-    "3D bioprinting advances for organ transplantation with regulatory analysis",
-    "AI-driven biosensor design for early cancer detection"
+    "CRISPR living therapeutics in clinical trials since 2020",
+    "AI-designed enzymes for plastic degradation — literature + pathways",
+    "Synthetic biology startups in oncology — funding map",
+    "Metabolic pathway for artemisinin biosynthesis in yeast",
+    "Oncolytic virus engineering — biosecurity risk analysis"
 ]
 
-# Store past results for quick access
-history_store = {}
-
-def run_pipeline(query):
-    """Run the GENESIS-AI research pipeline and return formatted outputs."""
-    if not query.strip():
-        return "⚠️ Please enter a topic", "", None, None, None
-
+def run_literature_review(query):
     report = research_once(query)
-    history_store[query] = report  # save to session
-
-    # Build citations markdown
-    cites_md = ""
-    if report["citations"]:
-        cites_md = "### 📚 Citations\n" + "\n".join(
-            [f"- [{c['type']}]({c['url']})" for c in report["citations"]]
-        )
-    else:
-        cites_md = "⚠️ _No citations found_"
-
-    # Build pathway & funding visualizations
-    pathway_fig = generate_pathway_graph(
-        entities=report["expanded_terms"],
-        relationships=[{"source": report["expanded_terms"][i],
-                        "target": report["expanded_terms"][i+1],
-                        "type": "related"}
-                       for i in range(len(report["expanded_terms"]) - 1)]
-    )
-
-    funding_fig = generate_funding_network(
-        companies=[
-            {"name": "SynBioTech", "investors": "BioFund, HealthCapital"},
-            {"name": "GeneWorks", "investors": "BioFund, InnovationAngels"}
-        ]
-    )
-
     return (
         report["summary"],
-        cites_md,
+        report["citations"],
+        report["structures"],
         report["visual_image_url"],
-        pathway_fig,
-        funding_fig
+        report["audio_url"]
     )
 
-def load_history(query):
-    """Load past research result instantly."""
-    report = history_store.get(query)
-    if not report:
-        return "", "", None, None, None
+def run_pathway_graph():
+    entities = ["CRISPR", "Cas9", "DNA Repair", "Therapeutic Delivery"]
+    relationships = [
+        {"source": "CRISPR", "target": "Cas9", "type": "guides"},
+        {"source": "Cas9", "target": "DNA Repair", "type": "triggers"},
+        {"source": "DNA Repair", "target": "Therapeutic Delivery", "type": "enables"}
+    ]
+    return generate_pathway_graph(entities, relationships)
+
+def run_funding_network():
+    companies = [
+        {"name": "SynBioCorp", "investors": "Sequoia Capital, Andreessen Horowitz"},
+        {"name": "BioTheraX", "investors": "SoftBank, ARCH Venture Partners"}
+    ]
+    return generate_funding_network(companies)
+
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="emerald", secondary_hue="lime")) as app:
+    gr.Markdown("# 🧬 GENESIS-AI — Synthetic Biology Command Center")
+    gr.Markdown("A next-generation **lab instrument** for literature review, pathway mapping, funding analysis, and biosecurity insights.")
 
-    cites_md = "### 📚 Citations\n" + "\n".join(
-        [f"- [{c['type']}]({c['url']})" for c in report["citations"]]
-    )
-    return (
-        report["summary"],
-        cites_md,
-        report["visual_image_url"],
-        generate_pathway_graph(
-            entities=report["expanded_terms"],
-            relationships=[{"source": report["expanded_terms"][i],
-                            "target": report["expanded_terms"][i+1],
-                            "type": "related"}
-                           for i in range(len(report["expanded_terms"]) - 1)]
-        ),
-        generate_funding_network(
-            companies=[
-                {"name": "SynBioTech", "investors": "BioFund, HealthCapital"},
-                {"name": "GeneWorks", "investors": "BioFund, InnovationAngels"}
-            ]
-        )
-    )
+    with gr.Row():
+        query_input = gr.Textbox(label="Enter your research query", placeholder="e.g., CRISPR living therapeutics in clinical trials since 2020", lines=2)
+        run_button = gr.Button("🚀 Run Literature Review", variant="primary")
 
-with gr.Blocks(theme=gr.themes.Soft(primary_hue="emerald", secondary_hue="gray")) as demo:
-    gr.Markdown(
-        """
-        # 🌱 **GENESIS-AI** — Synthetic Biology Intelligence Platform
-        _AI-powered research, visualization, and synthesis for the future of life sciences._
-        """
-    )
+    gr.Markdown("### 🔹 Or click a demo query:")
+    with gr.Row():
+        demo_btns = []
+        for dq in DEMO_QUERIES:
+            btn = gr.Button(dq)
+            btn.click(fn=lambda q=dq: run_literature_review(q),
+                      inputs=[],
+                      outputs=["summary_box", "citations_box", "structures_box", "image_out", "audio_out"])
+            demo_btns.append(btn)
+
+    with gr.Tab("📄 Summary"):
+        summary_box = gr.Textbox(label="AI-Generated Summary", lines=15, interactive=False)
+
+    with gr.Tab("📚 Citations"):
+        citations_box = gr.JSON(label="Citations")
+
+    with gr.Tab("🧪 Structures"):
+        structures_box = gr.JSON(label="3D Molecular Structures")
+
+    with gr.Tab("🖼 Diagram"):
+        image_out = gr.Image(label="Generated Diagram")
+
+    with gr.Tab("🔊 Narration"):
+        audio_out = gr.Audio(label="Narrated Summary", type="filepath")
+
+    gr.Markdown("## 🧭 Additional Tools")
 
     with gr.Row():
-        with gr.Column(scale=3):
-            query_box = gr.Textbox(
-                label="🔍 Enter a research topic",
-                placeholder="e.g., CRISPR gene therapy for cancer",
-                lines=2
-            )
-            run_btn = gr.Button("🚀 Run Research", variant="primary")
-            gr.Markdown("### 🔬 Quick Research Chips")
-            with gr.Row():
-                for q in DEMO_QUERIES:
-                    gr.Button(q, elem_classes="chip").click(
-                        run_pipeline, inputs=gr.Textbox(value=q, visible=False),
-                        outputs=["summary_out", "cites_out", "image_out", "pathway_out", "funding_out"]
-                    )
-
-            gr.Markdown("### 🕘 History")
-            history_list = gr.Dropdown(
-                choices=[], label="Past Research Queries", interactive=True
-            )
-            history_list.change(
-                load_history, inputs=history_list,
-                outputs=["summary_out", "cites_out", "image_out", "pathway_out", "funding_out"]
-            )
-
-        with gr.Column(scale=7):
-            with gr.Accordion("📄 Summary", open=True):
-                summary_out = gr.Markdown(elem_id="summary_out")
-            with gr.Accordion("📚 Citations", open=False):
-                cites_out = gr.Markdown(elem_id="cites_out")
-            with gr.Accordion("🖼️ Image / Diagram", open=False):
-                image_out = gr.Image(type="filepath", elem_id="image_out")
-            with gr.Accordion("🔗 Pathway Graph", open=False):
-                pathway_out = gr.Plot(elem_id="pathway_out")
-            with gr.Accordion("💰 Funding Network", open=False):
-                funding_out = gr.Plot(elem_id="funding_out")
-
-    run_btn.click(
-        fn=run_pipeline,
-        inputs=query_box,
-        outputs=[summary_out, cites_out, image_out, pathway_out, funding_out]
+        pathway_btn = gr.Button("🧬 Generate Pathway Graph")
+        pathway_img = gr.Image(label="Pathway Graph")
+        pathway_btn.click(fn=run_pathway_graph, inputs=[], outputs=pathway_img)
+
+        funding_btn = gr.Button("💰 Generate Funding Network")
+        funding_img = gr.Image(label="Funding Network")
+        funding_btn.click(fn=run_funding_network, inputs=[], outputs=funding_img)
+
+    # Link main run button
+    run_button.click(
+        fn=run_literature_review,
+        inputs=query_input,
+        outputs=[summary_box, citations_box, structures_box, image_out, audio_out]
     )
 
-if __name__ == "__main__":
-    demo.launch()
+app.launch()