"""
Biopesticide-AI Gradio UI v2 -- high-end startup-grade interface.
Redesigned with:
- Hero section with project pitch and key metrics
- Tabbed workflow: Design | Analytics | Safety | Regulatory | About
- Data visualizations (efficacy chart, off-target heatmap, half-life chart)
- Candidate cards instead of plain tables
- CSV/JSON export buttons
- Professional color scheme and typography
- Loading states and empty states
- Backend status strip with live model/LLM info
Usage:
python -m bioai.ui.gradio_app # launch on 0.0.0.0:7860
python -m bioai.ui.gradio_app --port 8080
python -m bioai.ui.gradio_app --share # public share link
"""
from __future__ import annotations
import argparse
import csv
import io
import json
import os
import sys
import time
from pathlib import Path
import gradio as gr
# Make sure we can import bioai from anywhere
_PROJECT_ROOT = Path(__file__).resolve().parents[2]
if str(_PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(_PROJECT_ROOT))
from bioai.orchestrator import BiopesticideOrchestrator # noqa: E402
from bioai.sequence_utils import SAFETY_SPECIES, PEST_SPECIES # noqa: E402
from bioai.ui.charts import efficacy_bar_chart, offtarget_heatmap, halflife_chart # noqa: E402
# ─────────────────────────────────────────────────────────────────────────────
# Branding
# ─────────────────────────────────────────────────────────────────────────────
TITLE = "Biopesticide-AI"
TAGLINE = "Design species-specific dsRNA biopesticides in minutes, not months"
SUBTITLE = "Local Llama 3.2 3B + PyTorch + 14-species safety panel + physics-informed fate model"
EXAMPLES = [
["Brown planthopper infestation in my rice paddy near Coimbatore, Tamil Nadu. Severity moderate, second generation this season.", 10],
["Fall armyworm outbreak in maize field in Karnataka. Severe damage on 30% of plants, spreading fast.", 10],
["Desert locust swarm reported in wheat fields of Rajasthan. Need rapid response biopesticide design.", 10],
["Colorado potato beetle devastating my potato crop in Himachal Pradesh. Resistance to neonicotinoids suspected.", 8],
["Tobacco whitefly infestation in tomato greenhouse in Maharashtra. Mild severity but persistent.", 5],
["Peach-potato aphid outbreak in vegetable garden. Organic farm, need bee-safe solution.", 5],
]
# ─────────────────────────────────────────────────────────────────────────────
# Singleton orchestrator
# ─────────────────────────────────────────────────────────────────────────────
_ORCHESTRATOR: BiopesticideOrchestrator | None = None
def get_orchestrator() -> BiopesticideOrchestrator:
global _ORCHESTRATOR
if _ORCHESTRATOR is None:
print("[gradio_app] initializing orchestrator...")
_ORCHESTRATOR = BiopesticideOrchestrator()
print(f"[gradio_app] backend = {type(_ORCHESTRATOR.ranker.sirna_model).__name__}, degraded_mode = {_ORCHESTRATOR.degraded_mode}")
return _ORCHESTRATOR
# ─────────────────────────────────────────────────────────────────────────────
# HTML/CSS helpers
# ─────────────────────────────────────────────────────────────────────────────
CUSTOM_CSS = """
:root {
--bioai-bg: #f4f5f6;
--bioai-surface: #ffffff;
--bioai-card: #ecedee;
--bioai-accent: #2f86b2;
--bioai-accent-2: #ba5a6a;
--bioai-text: #242627;
--bioai-muted: #71777a;
--bioai-border: #a1b9c6;
--bioai-success: #449f63;
--bioai-warning: #b69045;
--bioai-error: #964039;
}
.gradio-container { max-width: 1200px !important; }
.bioai-hero {
background: linear-gradient(135deg, #4a616c 0%, #2f86b2 100%);
color: white;
padding: 32px 28px;
border-radius: 12px;
margin-bottom: 20px;
}
.bioai-hero h1 {
font-size: 32px !important;
font-weight: 800 !important;
margin: 0 0 8px 0 !important;
letter-spacing: -0.5px;
}
.bioai-hero p {
font-size: 14px !important;
margin: 4px 0 !important;
opacity: 0.92;
}
.bioai-hero .tagline {
font-size: 18px !important;
font-weight: 500 !important;
margin: 12px 0 4px 0 !important;
}
.bioai-metric-row {
display: flex;
gap: 24px;
margin-top: 20px;
flex-wrap: wrap;
}
.bioai-metric {
text-align: center;
}
.bioai-metric .num {
font-size: 28px;
font-weight: 800;
color: white;
}
.bioai-metric .lbl {
font-size: 11px;
text-transform: uppercase;
letter-spacing: 0.5px;
opacity: 0.85;
margin-top: 2px;
}
.bioai-card {
background: var(--bioai-surface);
border: 1px solid var(--bioai-border);
border-radius: 8px;
padding: 16px 20px;
margin-bottom: 12px;
}
.bioai-status-strip {
display: flex;
gap: 8px;
justify-content: center;
margin: 8px 0 16px 0;
flex-wrap: wrap;
}
.bioai-pill {
padding: 4px 12px;
border-radius: 12px;
font-size: 11px;
font-weight: 600;
color: white;
}
.bioai-stats-strip {
display: flex;
gap: 28px;
justify-content: center;
margin: 12px 0 20px 0;
padding: 14px 0;
border-top: 1px solid var(--bioai-border);
border-bottom: 1px solid var(--bioai-border);
flex-wrap: wrap;
}
.bioai-stat {
text-align: center;
}
.bioai-stat .num {
font-size: 22px;
font-weight: 700;
color: var(--bioai-accent);
}
.bioai-stat .lbl {
font-size: 10px;
color: var(--bioai-muted);
text-transform: uppercase;
letter-spacing: 0.5px;
}
.bioai-candidate-card {
background: var(--bioai-surface);
border: 1px solid var(--bioai-border);
border-left: 4px solid var(--bioai-accent);
border-radius: 6px;
padding: 14px 18px;
margin-bottom: 10px;
}
.bioai-candidate-card .header {
display: flex;
justify-content: space-between;
align-items: center;
margin-bottom: 8px;
}
.bioai-candidate-card .rank {
font-size: 11px;
font-weight: 700;
color: var(--bioai-accent);
text-transform: uppercase;
}
.bioai-candidate-card .seq {
font-family: monospace;
font-size: 14px;
color: var(--bioai-text);
font-weight: 600;
}
.bioai-candidate-card .metrics {
display: flex;
gap: 16px;
font-size: 12px;
color: var(--bioai-muted);
}
.bioai-candidate-card .metric-val {
font-weight: 700;
color: var(--bioai-text);
}
.bioai-footer {
text-align: center;
color: var(--bioai-muted);
font-size: 11px;
padding: 16px 0;
border-top: 1px solid var(--bioai-border);
margin-top: 24px;
}
"""
def _hero_html() -> str:
return f"""
Biopesticide-AI
{TAGLINE}
{SUBTITLE}
"""
def _status_pill(text: str, color: str = "#4a616c") -> str:
return f'{text}'
def _backend_status_html(orch: BiopesticideOrchestrator) -> str:
llm_text = "Ollama Llama 3.2 3B (local)" if not orch.degraded_mode else "Degraded mode (Ollama not running)"
llm_color = "#449f63" if not orch.degraded_mode else "#b69045"
model_class = type(orch.ranker.sirna_model).__name__
model_text = "Caduceus-Ph-1" if model_class == "CaduceusAdapter" else "Dilated CNN (HyenaDNA-inspired)"
model_color = "#2f86b2" if model_class == "CaduceusAdapter" else "#4a616c"
device = str(orch.ranker.device)
return f"""
{_status_pill('Model: ' + model_text, model_color)}
{_status_pill('LLM: ' + llm_text, llm_color)}
{_status_pill('Device: ' + device, '#71777a')}
{_status_pill('Safety panel: 14 species', '#4c7094')}
{_status_pill('Pest targets: 7 species', '#ba5a6a')}
"""
def _stats_strip(result: dict, elapsed: float) -> str:
n_tr = result.get("n_transcripts", 0)
n_pre = result.get("n_precursors", 0)
n_si = result.get("n_sirnas", 0)
cost = result.get("total_cost_estimate", 0.0)
return f"""
{elapsed:.1f}s
Design loop
"""
def _pest_report_html(pest: dict) -> str:
if not pest:
return "No pest report parsed.
"
species = pest.get("pest_species", pest.get("species", "unknown"))
crop = pest.get("crop", "unknown")
severity = pest.get("severity", "unknown")
location = pest.get("location", "unknown")
notes = pest.get("notes", "")
notes_html = f"| Notes | {notes} |
" if notes else ""
return f"""
| Target species | {species} |
| Crop | {crop} |
| Severity | {severity} |
| Location | {location} |
{notes_html}
"""
def _candidate_cards_html(candidates: list) -> str:
"""Render candidates as styled cards instead of a plain table."""
if not candidates:
return "No candidates generated. Run the design pipeline first.
"
cards = []
for i, c in enumerate(candidates, 1):
seq = c.get("sirna_seq", "")
eff = c.get("efficacy", 0)
ot = c.get("offtarget_max", 0)
hl = c.get("half_life_hours", 0)
score = c.get("final_score", 0)
hl_days = hl / 24
# Risk tier color for the left border
if score > 0.3:
border_color = "#449f63" # success
elif score > 0.15:
border_color = "#b69045" # warning
else:
border_color = "#964039" # error
cards.append(f"""
Efficacy: {eff:.3f}
Off-target max: {ot:.3f}
Half-life: {hl:.1f}h ({hl_days:.1f}d)
Final score: {score:.3f}
""")
return "".join(cards)
def _candidates_to_dataframe(candidates: list) -> list:
rows = []
for i, c in enumerate(candidates, 1):
rows.append([
i,
c.get("sirna_seq", ""),
f"{c.get('efficacy', 0):.3f}",
f"{c.get('offtarget_max', 0):.3f}",
f"{c.get('half_life_hours', 0):.1f}h",
f"{c.get('final_score', 0):.3f}",
])
return rows
def _safety_cards_md(result: dict) -> str:
sc = result.get("safety_cards", "")
if isinstance(sc, list):
parts = []
for i, c in enumerate(sc, 1):
if isinstance(c, dict):
parts.append(f"### Candidate #{i}: `{c.get('sirna_seq', '')}`\n\n{c.get('card_markdown', '')}")
else:
parts.append(str(c))
return "\n\n---\n\n".join(parts) if parts else "_(no safety cards generated)_"
return sc or "_(no safety cards generated)_"
def _export_csv(candidates: list) -> str:
"""Generate CSV string for download."""
if not candidates:
return ""
output = io.StringIO()
writer = csv.writer(output)
writer.writerow(["rank", "sirna_seq", "efficacy", "offtarget_max", "half_life_hours", "final_score"])
for i, c in enumerate(candidates, 1):
writer.writerow([
i,
c.get("sirna_seq", ""),
f"{c.get('efficacy', 0):.4f}",
f"{c.get('offtarget_max', 0):.4f}",
f"{c.get('half_life_hours', 0):.2f}",
f"{c.get('final_score', 0):.4f}",
])
return output.getvalue()
def _export_json(result: dict) -> str:
"""Generate JSON string for download."""
slim = {k: v for k, v in result.items() if k not in ("safety_cards", "regulatory_memo")}
if "candidates" in slim and isinstance(slim["candidates"], list):
slim["candidates"] = slim["candidates"][:10]
return json.dumps(slim, indent=2, default=str)
# ─────────────────────────────────────────────────────────────────────────────
# Main design handler
# ─────────────────────────────────────────────────────────────────────────────
def design_handler(user_text: str, top_k: int):
"""Run the design pipeline and return all UI outputs."""
if not user_text or not user_text.strip():
empty_status = "Please describe your pest problem above.
"
return (
empty_status,
gr.update(value=[]),
"No pest report parsed.
",
"",
None, None, None, # charts
"",
"_(no safety cards generated)_",
"_(no regulatory memo generated)_",
"",
"",
)
orch = get_orchestrator()
t0 = time.time()
try:
result = orch.design(user_text, top_k=int(top_k))
except Exception as e:
import traceback
tb = traceback.format_exc()
err = f"Pipeline error: {type(e).__name__}: {e}
{tb}"
return (err, gr.update(value=[]), "No pest report parsed.
", "", None, None, None, "", "_(no safety cards generated)_", "_(no regulatory memo generated)_", "", "")
elapsed = time.time() - t0
candidates = result.get("candidates", [])
status_html = _backend_status_html(orch) + _stats_strip(result, elapsed)
candidates_rows = _candidates_to_dataframe(candidates)
candidates_html = _candidate_cards_html(candidates)
pest_html = _pest_report_html(result.get("pest_report", {}))
safety_md = _safety_cards_md(result)
memo_md = result.get("regulatory_memo", "_(no regulatory memo generated)_")
csv_str = _export_csv(candidates)
json_str = _export_json(result)
# Generate charts
efficacy_chart = efficacy_bar_chart(candidates) if candidates else None
offtarget_chart = offtarget_heatmap(candidates, SAFETY_SPECIES) if candidates else None
halflife_chart_path = halflife_chart(candidates) if candidates else None
return (
status_html,
candidates_rows,
candidates_html,
pest_html,
"",
efficacy_chart,
offtarget_chart,
halflife_chart_path,
safety_md,
memo_md,
csv_str,
json_str,
)
# ─────────────────────────────────────────────────────────────────────────────
# Gradio Blocks UI
# ─────────────────────────────────────────────────────────────────────────────
def build_ui() -> gr.Blocks:
demo = gr.Blocks(title="Biopesticide-AI")
with demo:
# ─── Hero ─────────────────────────────────────────────────────────
gr.HTML(_hero_html())
# ─── Initial status ───────────────────────────────────────────────
try:
orch = get_orchestrator()
initial_status = _backend_status_html(orch) + "Click Design dsRNA candidates to run the pipeline.
"
except Exception as e:
initial_status = f"Failed to initialize orchestrator: {e}
"
status_box = gr.HTML(value=initial_status, label="Pipeline status")
# ─── Main tabs ────────────────────────────────────────────────────
with gr.Tabs():
# ── Tab 1: Design ─────────────────────────────────────────────
with gr.Tab("Design", id=0):
gr.Markdown("### Describe your pest problem in plain English")
user_text = gr.Textbox(
label="Pest report",
placeholder="e.g. 'Brown planthopper infestation in my rice paddy near Coimbatore, Tamil Nadu. Severity moderate, second generation this season.'",
lines=4,
value=EXAMPLES[0][0],
)
with gr.Accordion("Advanced settings", open=False):
top_k = gr.Slider(minimum=1, maximum=20, value=10, step=1, label="Top-K candidates to return")
run_btn = gr.Button("Design dsRNA candidates", variant="primary", size="lg")
gr.Examples(
examples=EXAMPLES,
inputs=[user_text, top_k],
label="Try one of these preset pest reports",
)
gr.Markdown("---")
gr.Markdown("### Parsed pest report")
pest_html = gr.HTML(value="Run the pipeline to see the parsed pest report.
")
gr.Markdown("### Top candidates")
candidates_html = gr.HTML(value="Run the pipeline to see ranked candidates.
")
# Hidden dataframe for CSV export compatibility
candidates_table = gr.Dataframe(
visible=False,
headers=["Rank", "siRNA", "Efficacy", "Off-target", "Half-life", "Score"],
value=[],
)
# ── Tab 2: Analytics ──────────────────────────────────────────
with gr.Tab("Analytics", id=1):
gr.Markdown("### Efficacy scores")
efficacy_img = gr.Image(label="", show_label=False, height=350)
gr.Markdown("### Off-target risk heatmap (candidates x 14 safety species)")
offtarget_img = gr.Image(label="", show_label=False, height=400)
gr.Markdown("### Environmental fate (predicted half-life)")
halflife_img = gr.Image(label="", show_label=False, height=350)
# ── Tab 3: Safety ─────────────────────────────────────────────
with gr.Tab("Safety cards", id=2):
gr.Markdown("### Per-candidate safety cards (generated by local Llama 3.2 3B)")
safety_md = gr.Markdown(value="Run the pipeline to see safety cards for the top candidates.
")
# ── Tab 4: Regulatory ─────────────────────────────────────────
with gr.Tab("Regulatory memo", id=3):
gr.Markdown("### EPA-style regulatory memo (generated by local Llama 3.2 3B)")
memo_md = gr.Markdown(value="Run the pipeline to see the regulatory memo.
")
# ── Tab 5: Export ─────────────────────────────────────────────
with gr.Tab("Export", id=4):
gr.Markdown("### Download design results")
gr.Markdown("Export the top candidates as CSV or the full design result as JSON for downstream analysis.")
csv_text = gr.Textbox(label="CSV (copy below or use the download button)", lines=10, interactive=False)
csv_btn = gr.DownloadButton("Download CSV", value=None)
json_text = gr.Textbox(label="JSON (copy below or use the download button)", lines=15, interactive=False)
json_btn = gr.DownloadButton("Download JSON", value=None)
# ── Tab 6: About ──────────────────────────────────────────────
with gr.Tab("About", id=5):
gr.Markdown("""
### About Biopesticide-AI
**Biopesticide-AI** is an end-to-end pipeline for designing dsRNA biopesticides against agricultural pests. It compresses the traditional 3-6 month wet-lab design loop into a 4-minute computational pipeline that any farmer, agronomist, or cooperative can run from a laptop.
**Pipeline:**
1. Farmer describes pest problem in plain English
2. Local Ollama Llama 3.2 3B parses the report into a structured design spec
3. PyTorch backend tiles pest transcripts into 200-nt dsRNA precursors
4. Dicer-style dicing produces 21-nt siRNAs
5. Dilated CNN (HyenaDNA-inspired) scores each siRNA for efficacy
6. K-mer index checks off-target risk against 14 non-target species
7. Physics-Informed Neural Network predicts environmental half-life
8. Learned ranker combines all scores into a final candidate ranking
9. Llama 3.2 3B generates safety cards + EPA-style regulatory memo
**14-species safety panel** covers pollinators (honeybee, bumblebee, leafcutter bee), beneficial predators (ladybug, lacewing), soil invertebrates (earthworm), aquatic organisms (water flea, zebrafish), livestock (cattle, zebu, chicken, sheep, pig), and human safety.
**7 pest targets** include brown planthopper (rice), fall armyworm (maize), desert locust (wheat), striped stem borer (rice), peach-potato aphid (vegetables), Colorado potato beetle (potato), and tobacco whitefly (tomato).
**Cost: $0 per design.** All compute is local. No cloud API spend.
Built for the AMD Developer Hackathon Unicorn Track. MIT licensed.
""")
# ─── Footer ───────────────────────────────────────────────────────
gr.HTML(
""
)
# ─── Wire up ──────────────────────────────────────────────────────
run_btn.click(
design_handler,
inputs=[user_text, top_k],
outputs=[
status_box,
candidates_table,
candidates_html,
pest_html,
status_box, # update status after run (same component)
efficacy_img,
offtarget_img,
halflife_img,
safety_md,
memo_md,
csv_text,
json_text,
],
)
return demo
# ─────────────────────────────────────────────────────────────────────────────
# Entry point
# ─────────────────────────────────────────────────────────────────────────────
def main():
parser = argparse.ArgumentParser(description="Biopesticide-AI Gradio UI v2")
parser.add_argument("--host", default="0.0.0.0", help="bind host (default 0.0.0.0)")
parser.add_argument("--port", type=int, default=7860, help="bind port (default 7860)")
parser.add_argument("--share", action="store_true", help="create a public share link")
parser.add_argument("--max-threads", type=int, default=4, help="max concurrent requests")
args = parser.parse_args()
print("[gradio_app] pre-initializing orchestrator...")
get_orchestrator()
demo = build_ui()
print(f"[gradio_app] launching on http://{args.host}:{args.port}")
demo.launch(
server_name=args.host,
server_port=args.port,
share=args.share,
max_threads=args.max_threads,
show_error=True,
css=CUSTOM_CSS,
)
if __name__ == "__main__":
main()