app.py

import os
import gradio as gr
from genesis.pipeline import research_once
from genesis.visualization import generate_pathway_graph, generate_funding_network

# Environment variables
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
ELEVEN_LABS_API_KEY = os.getenv("ELEVEN_LABS_API_KEY")

# Preloaded demo queries
DEMO_QUERIES = [
    "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"
]

# Custom LCARS biotech theme
CUSTOM_CSS = """
body {background-color: #001f1f; color: #ccffcc;}
.gradio-container {font-family: 'Orbitron', sans-serif;}
button {background-color: #ff6600 !important; color: white !important;}
textarea, input {background-color: #003333; color: #ccffcc;}
.tab-nav {background-color: #004d4d !important;}
"""

# -------------------- UI Functions -------------------- #
def run_text_query(query, narration):
    result = research_once(query, narration=narration)
    return (
        result["summary"],
        format_citations(result["citations"]),
        result["visual_image_url"],
        result["structures"],
        result["audio_url"] if narration else None
    )

def run_pathway_query(pathway_name):
    return generate_pathway_graph(pathway_name)

def run_funding_query(topic):
    return generate_funding_network(topic)

def format_citations(citations):
    if not citations:
        return "No citations found."
    md = ""
    for cite in citations:
        md += f"- [{cite['type']}]({cite['url']})\n"
    return md

# -------------------- Build UI -------------------- #
with gr.Blocks(css=CUSTOM_CSS, theme="default") as demo:
    gr.Markdown("# 🧬 GENESIS-AI — Synthetic Biology Command Center")
    gr.Markdown("**Multi-modal AI platform linking molecules, pathways, funding, and regulations**")

    with gr.Tabs():
        # ---------- TEXT QUERY TAB ----------
        with gr.Tab("📜 Literature & Regulatory Analysis"):
            narration = gr.Checkbox(label="Enable Narration", value=False)
            text_input = gr.Textbox(label="Enter your query", placeholder="Type your synthetic biology research question...")
            run_btn = gr.Button("Run Research")

            demo_btns = gr.Row()
            for q in DEMO_QUERIES:
                demo_btns.add(gr.Button(q))

            summary_out = gr.Textbox(label="Summary")
            cites_out = gr.Markdown()
            img_out = gr.Image(label="Generated Diagram")
            struct_out = gr.JSON(label="Molecular Structures")
            audio_out = gr.Audio(label="Narration", type="filepath")

            run_btn.click(
                fn=run_text_query,
                inputs=[text_input, narration],
                outputs=[summary_out, cites_out, img_out, struct_out, audio_out]
            )

            for q in DEMO_QUERIES:
                demo_btns.children[DEMO_QUERIES.index(q)].click(
                    fn=run_text_query,
                    inputs=[gr.Textbox(value=q, visible=False), narration],
                    outputs=[summary_out, cites_out, img_out, struct_out, audio_out]
                )

        # ---------- PATHWAY TAB ----------
        with gr.Tab("🧪 Metabolic Pathway Mapping"):
            pathway_input = gr.Textbox(label="Pathway Name", placeholder="e.g., artemisinin biosynthesis")
            pathway_btn = gr.Button("Generate Pathway Graph")
            pathway_graph_out = gr.Image(label="Pathway Graph")
            pathway_btn.click(run_pathway_query, inputs=pathway_input, outputs=pathway_graph_out)

        # ---------- FUNDING TAB ----------
        with gr.Tab("💰 Funding & Investor Networks"):
            funding_input = gr.Textbox(label="Topic", placeholder="e.g., synthetic biology oncology startups")
            funding_btn = gr.Button("Generate Funding Map")
            funding_graph_out = gr.Image(label="Funding Network")
            funding_btn.click(run_funding_query, inputs=funding_input, outputs=funding_graph_out)

        # ---------- IMAGE ANALYSIS TAB ----------
        with gr.Tab("🖼️ Microscopy / Image Analysis"):
            img_upload = gr.Image(type="filepath", label="Upload Image")
            img_btn = gr.Button("Analyze Image")
            img_analysis_out = gr.Textbox(label="Analysis Result")
            # Hook to your vision model here
            img_btn.click(lambda img: f"Analysis for {img}", inputs=img_upload, outputs=img_analysis_out)

    demo.launch(server_name="0.0.0.0", server_port=7860)