app.py

import gradio as gr
import os, requests, io, json
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from groq import Groq
from PIL import Image
from datetime import datetime
from huggingface_hub import HfApi, hf_hub_download

GROQ_KEY = os.environ.get("GROQ_API_KEY", "")
HF_TOKEN = os.environ.get("HF_TOKEN", "")
HISTORY_REPO = "Saicharan21/cardiolab-chat-history"
PAPERS_DB_REPO = "Saicharan21/cardiolab-papers-db"
CARDIOLAB_MODEL_REPO = "Saicharan21/CardioLab-AI-Model"

CHAT_MODELS = {
    "Llama 3.3 70B (Best)": "llama-3.3-70b-versatile",
    "Llama 3.1 8B (Fast)": "llama-3.1-8b-instant",
    "Llama 4 Scout": "meta-llama/llama-4-scout-17b-16e-instruct",
    "Llama 4 Maverick": "meta-llama/llama-4-maverick-17b-128e-instruct",
}

KNOWHOW = ("MCL: Sylgard 184 PDMS 10:1 ratio 48hr cure green laser PIV 70bpm 5L/min. "
    "TGT: Arduino Uno Stepper Motor 150mL blood 0 20 40 60min TAT PF1.2 hemolysis platelets. "
    "NORMAL: TAT below 8. PF1.2 below 2.0. Hemo below 20. Plt above 150. "
    "uPAD: Jaffe reaction creatinine picric acid orange-red. Normal 0.6-1.2 mg/dL. CKD above 1.5. "
    "MHV: 27mm SJM Regent bileaflet trileaflet monoleaflet pediatric. "
    "PIV: green laser 532nm. Normal velocity 0.5-2.0 m/s. Shear below 5 Pa. Risk above 10 Pa. "
    "Equipment: Heska HT5 analyzer PIV Tygon tubing Arduino Uno.")

CSS = """
@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap');
body, .gradio-container { background: #f8fafc !important; font-family: Inter, sans-serif !important; }
.tab-nav { background: #fff !important; border-bottom: 1px solid #e2e8f0 !important; padding: 0 8px !important; display: flex !important; flex-wrap: wrap !important; }
.tab-nav button { background: transparent !important; color: #64748b !important; border: none !important; border-bottom: 2px solid transparent !important; border-radius: 0 !important; padding: 10px 12px !important; font-weight: 500 !important; font-size: 0.8em !important; white-space: nowrap !important; margin-bottom: -1px !important; }
.tab-nav button:hover { color: #c1121f !important; background: #fff5f5 !important; }
.tab-nav button.selected { color: #c1121f !important; border-bottom: 2px solid #c1121f !important; font-weight: 700 !important; background: transparent !important; }
.message.user { background: linear-gradient(135deg, #c1121f, #e63946) !important; color: white !important; border-radius: 14px 14px 4px 14px !important; padding: 12px 16px !important; }
.message.bot { background: #ffffff !important; color: #1a202c !important; border: 1px solid #e2e8f0 !important; border-left: 3px solid #c1121f !important; border-radius: 4px 14px 14px 14px !important; padding: 12px 16px !important; }
textarea { background: #fff !important; color: #1a202c !important; border: 1px solid #e2e8f0 !important; border-radius: 10px !important; }
textarea:focus { border-color: #c1121f !important; outline: none !important; box-shadow: 0 0 0 2px rgba(193,18,31,0.1) !important; }
button.primary { background: #c1121f !important; color: white !important; border: none !important; border-radius: 8px !important; font-weight: 600 !important; }
button.primary:hover { background: #a00e18 !important; transform: translateY(-1px) !important; }
button.secondary { background: #f1f5f9 !important; color: #475569 !important; border: 1px solid #e2e8f0 !important; border-radius: 8px !important; }
input[type=number] { background: #fff !important; color: #1a202c !important; border: 1px solid #e2e8f0 !important; border-radius: 8px !important; }
label span { color: #475569 !important; font-weight: 500 !important; font-size: 0.82em !important; }
::-webkit-scrollbar { width: 5px; }
::-webkit-scrollbar-thumb { background: #c1121f; border-radius: 4px; }
"""

HEADER = """
<style>
@keyframes hb{0%,100%{transform:scale(1)}15%{transform:scale(1.14)}30%{transform:scale(1)}45%{transform:scale(1.08)}60%{transform:scale(1)}}
@keyframes ecg{from{stroke-dashoffset:400}to{stroke-dashoffset:0}}
@keyframes fadeD{from{opacity:0;transform:translateY(-8px)}to{opacity:1;transform:translateY(0)}}
</style>
<div style="background:#fff;border-bottom:2px solid #c1121f;padding:14px 24px;display:flex;align-items:center;justify-content:space-between;box-shadow:0 1px 8px rgba(0,0,0,0.06);animation:fadeD 0.4s ease;">
  <div style="display:flex;align-items:center;gap:10px;">
    <div style="background:#eff6ff;border:1px solid #bfdbfe;border-radius:10px;padding:7px 12px;display:flex;align-items:center;gap:8px;">
      <svg width="20" height="20" viewBox="0 0 100 100">
        <circle cx="50" cy="35" r="28" fill="#0057a8"/><ellipse cx="50" cy="14" rx="18" ry="8" fill="#0057a8"/>
        <polygon points="35,12 37,5 40,12" fill="#e8a020"/><polygon points="40,11 43,4 46,11" fill="#e8a020"/>
        <polygon points="46,11 49,4 52,11" fill="#e8a020"/><polygon points="52,11 55,4 58,11" fill="#e8a020"/>
        <polygon points="58,12 61,5 64,12" fill="#e8a020"/>
        <rect x="38" y="30" width="24" height="18" rx="3" fill="#0057a8"/>
        <rect x="42" y="34" width="6" height="10" rx="2" fill="#e8a020"/>
        <rect x="36" y="46" width="28" height="6" rx="3" fill="#0057a8"/>
      </svg>
      <div>
        <div style="color:#1d4ed8;font-size:0.68em;font-weight:700;line-height:1.2;">SJSU</div>
        <div style="color:#374151;font-size:0.6em;line-height:1.2;">Biomedical Eng.</div>
      </div>
    </div>
  </div>
  <div style="display:flex;align-items:center;gap:14px;">
    <svg width="80" height="22" viewBox="0 0 100 22">
      <polyline points="0,11 18,11 23,3 27,19 31,1 35,17 39,11 100,11" fill="none" stroke="#c1121f" stroke-width="2" stroke-linecap="round" stroke-dasharray="400" style="animation:ecg 1.5s ease forwards;"/>
    </svg>
    <div style="display:flex;align-items:center;gap:12px;">
      <div style="animation:hb 1.4s ease infinite;">
        <svg width="34" height="30" viewBox="0 0 100 90">
          <defs><radialGradient id="hg" cx="50%" cy="35%"><stop offset="0%" stop-color="#e63946"/><stop offset="100%" stop-color="#9b0a14"/></radialGradient></defs>
          <path d="M50 85 C50 85 5 55 5 30 C5 15 18 5 30 5 C38 5 45 9 50 15 C55 9 62 5 70 5 C82 5 95 15 95 30 C95 55 50 85 50 85Z" fill="url(#hg)"/>
          <polyline points="22,46 30,46 34,35 38,57 42,28 46,51 52,46 78,46" fill="none" stroke="white" stroke-width="3.5" stroke-linecap="round" opacity="0.95"/>
        </svg>
      </div>
      <div>
        <div style="font-size:1.6em;font-weight:700;color:#111;letter-spacing:-0.5px;line-height:1.1;">Cardio<span style="color:#c1121f;">Lab</span> AI</div>
        <div style="font-size:0.6em;color:#9ca3af;margin-top:1px;">SJSU Biomedical Engineering</div>
      </div>
    </div>
    <svg width="80" height="22" viewBox="0 0 100 22" style="transform:scaleX(-1);">
      <polyline points="0,11 18,11 23,3 27,19 31,1 35,17 39,11 100,11" fill="none" stroke="#c1121f" stroke-width="2" stroke-linecap="round" stroke-dasharray="400" style="animation:ecg 1.8s ease forwards;"/>
    </svg>
  </div>
  <div style="display:flex;gap:6px;align-items:center;">
    <span style="background:#fef2f2;border:1px solid #fecaca;color:#c1121f;padding:3px 10px;border-radius:20px;font-size:0.65em;font-weight:600;">RAG Active</span>
    <span style="background:#eff6ff;border:1px solid #bfdbfe;color:#1d4ed8;padding:3px 10px;border-radius:20px;font-size:0.65em;font-weight:600;">4 Models</span>
    <span style="background:#f0fdf4;border:1px solid #bbf7d0;color:#15803d;padding:3px 10px;border-radius:20px;font-size:0.65em;font-weight:600;">16 Papers</span>
  </div>
</div>
"""

# ── PAPER DATABASE ─────────────────────────────────────────
CHUNKS = []
METADATA = []
EMBEDDINGS = None
PAPERS_LOADED = False
EMBEDDER = None

def load_papers():
    global CHUNKS, METADATA, EMBEDDINGS, PAPERS_LOADED, EMBEDDER
    try:
        from sentence_transformers import SentenceTransformer
        chunks_path = hf_hub_download(repo_id=PAPERS_DB_REPO, filename="chunks.json", repo_type="dataset", token=HF_TOKEN)
        meta_path = hf_hub_download(repo_id=PAPERS_DB_REPO, filename="metadata.json", repo_type="dataset", token=HF_TOKEN)
        emb_path = hf_hub_download(repo_id=PAPERS_DB_REPO, filename="embeddings.npy", repo_type="dataset", token=HF_TOKEN)
        with open(chunks_path) as f: CHUNKS = json.load(f)
        with open(meta_path) as f: METADATA = json.load(f)
        EMBEDDINGS = np.load(emb_path)
        EMBEDDER = SentenceTransformer("all-MiniLM-L6-v2")
        PAPERS_LOADED = True
        print("Papers loaded: " + str(len(CHUNKS)) + " chunks")
    except Exception as e:
        print("Paper load error: " + str(e))

load_papers()

def search_papers(query, n=4):
    if not PAPERS_LOADED or EMBEDDINGS is None or EMBEDDER is None: return "", []
    try:
        q_emb = EMBEDDER.encode([query])
        norms = np.linalg.norm(EMBEDDINGS, axis=1, keepdims=True)
        emb_norm = EMBEDDINGS / (norms + 1e-10)
        q_norm = q_emb / (np.linalg.norm(q_emb) + 1e-10)
        scores = (emb_norm @ q_norm.T).flatten()
        top_idx = np.argsort(scores)[::-1][:n]
        context = ""; results = []; seen = set()
        for idx in top_idx:
            chunk = CHUNKS[idx]; meta = METADATA[idx]; score = float(scores[idx])
            if score > 0.25:
                results.append({"chunk": chunk, "paper": meta["paper"], "score": score})
                if meta["paper"] not in seen:
                    context += chr(10) + "=== FROM: " + meta["paper"] + " ===" + chr(10)
                    seen.add(meta["paper"])
                context += chunk[:500] + chr(10)
        return context, results
    except: return "", []

# ── SESSION MANAGEMENT ─────────────────────────────────────
def load_all_sessions():
    if not HF_TOKEN: return {}
    try:
        path = hf_hub_download(repo_id=HISTORY_REPO, filename="chat_history.json", repo_type="dataset", token=HF_TOKEN)
        with open(path) as f: return json.load(f)
    except: return {}

def save_all_sessions(sessions):
    if not HF_TOKEN: return False
    try:
        api2 = HfApi(token=HF_TOKEN)
        api2.upload_file(path_or_fileobj=json.dumps(sessions, indent=2).encode(),
            path_in_repo="chat_history.json", repo_id=HISTORY_REPO,
            repo_type="dataset", token=HF_TOKEN, commit_message="Update")
        return True
    except: return False

def get_session_list():
    s = load_all_sessions()
    return list(reversed(list(s.keys()))) if s else ["No saved sessions"]

def save_session(history, name):
    if not history: return "Nothing to save", gr.update()
    if not name or not name.strip(): name = "Chat " + datetime.now().strftime("%b %d %H:%M")
    sessions = load_all_sessions()
    sessions[name] = {"messages": history, "saved_at": datetime.now().isoformat()}
    ok = save_all_sessions(sessions)
    choices = get_session_list()
    return ("Saved: " + name if ok else "Save failed"), gr.update(choices=choices, value=name)

def load_session(name):
    if not name or "No saved" in name: return [], "Select a session"
    sessions = load_all_sessions()
    return (sessions[name]["messages"], "Loaded: " + name) if name in sessions else ([], "Not found")

def delete_session(name):
    if not name or "No saved" in name: return "Select a session", gr.update()
    sessions = load_all_sessions()
    if name in sessions:
        del sessions[name]; save_all_sessions(sessions)
        choices = get_session_list()
        return "Deleted: " + name, gr.update(choices=choices, value=choices[0] if choices else None)
    return "Not found", gr.update()

def new_chat(): return [], "", "New chat"

# ── SEARCH ─────────────────────────────────────────────────
def get_pubmed(query, n=3):
    try:
        r = requests.get("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi",
            params={"db":"pubmed","term":query+" AND (heart valve OR hemodynamics OR microfluidic OR thrombogen OR creatinine OR CKD)","retmax":n,"retmode":"json","sort":"date","field":"tiab"},timeout=10)
        ids = r.json()["esearchresult"]["idlist"]
        return chr(10).join(["https://pubmed.ncbi.nlm.nih.gov/"+i for i in ids]) if ids else ""
    except: return ""

def quick_search(query):
    if not query.strip(): return "Please enter a topic."
    try:
        expanded = query
        if GROQ_KEY:
            try:
                client = Groq(api_key=GROQ_KEY)
                resp = client.chat.completions.create(model="llama-3.1-8b-instant",
                    messages=[{"role":"system","content":"Biomedical PubMed expert. Convert to MeSH terms. Return ONLY terms."},
                    {"role":"user","content":"Optimize: " + query}], max_tokens=60)
                expanded = resp.choices[0].message.content.strip() or query
            except: pass
        forced = expanded + " AND (heart valve OR hemodynamics OR microfluidic OR thrombogen OR creatinine OR PIV OR CKD)"
        r = requests.get("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi",
            params={"db":"pubmed","term":forced,"retmax":8,"retmode":"json","sort":"date","field":"tiab"},timeout=12)
        ids = r.json()["esearchresult"]["idlist"]
        out = "QUERY: " + query + chr(10) + "="*40 + chr(10) + chr(10)
        if ids:
            r2 = requests.get("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi",
                params={"db":"pubmed","id":",".join(ids),"retmode":"xml","rettype":"abstract"},timeout=12)
            import xml.etree.ElementTree as ET
            root = ET.fromstring(r2.content)
            out += "PUBMED:" + chr(10)
            for article in root.findall(".//PubmedArticle"):
                try:
                    title = article.find(".//ArticleTitle").text or "No title"
                    pmid = article.find(".//PMID").text or ""
                    year_el = article.find(".//PubDate/Year")
                    year = year_el.text if year_el is not None else ""
                    out += str(title)[:85] + " (" + year + ")" + chr(10)
                    out += "  https://pubmed.ncbi.nlm.nih.gov/" + pmid + chr(10) + chr(10)
                except: continue
        try:
            r3 = requests.get("https://api.semanticscholar.org/graph/v1/paper/search",
                params={"query":expanded,"limit":5,"fields":"title,year,url,citationCount"},timeout=12)
            papers = r3.json().get("data",[])
            out += "SEMANTIC SCHOLAR:" + chr(10)
            for p in papers:
                year = p.get("year",0) or 0
                if int(year) >= 2015:
                    out += p.get("title","")[:85] + " (" + str(year) + ")"
                    cites = p.get("citationCount",0)
                    if cites: out += " | " + str(cites) + " citations"
                    out += chr(10) + "  " + p.get("url","") + chr(10) + chr(10)
        except: pass
        out += "SJSU SCHOLARWORKS:" + chr(10)
        out += "  https://scholarworks.sjsu.edu/do/search/?q=" + requests.utils.quote(query) + "&context=6781027"
        return out
    except Exception as e:
        return "Search error: " + str(e)

# ── CHAT ───────────────────────────────────────────────────
def research_chat(message, history, chat_model):
    if not message.strip(): return "", history
    if not GROQ_KEY:
        history.append({"role":"user","content":message})
        history.append({"role":"assistant","content":"Error: Add GROQ_API_KEY to Space Settings."})
        return "", history
    try:
        model_id = CHAT_MODELS.get(chat_model, "llama-3.3-70b-versatile")
        client = Groq(api_key=GROQ_KEY)
        paper_context, paper_results = search_papers(message, n=4)
        if paper_context:
            system_prompt = ("You are CardioLab AI for SJSU Biomedical Engineering. "
                "Answer using SJSU CardioLab research papers below. Cite paper names." +
                chr(10) + "SJSU PAPERS:" + chr(10) + paper_context + chr(10) + "KNOWLEDGE: " + KNOWHOW)
        else:
            system_prompt = "You are CardioLab AI for SJSU Biomedical Engineering. " + KNOWHOW
        msgs = [{"role":"system","content":system_prompt}]
        for item in history:
            if isinstance(item, dict): msgs.append({"role":item["role"],"content":item["content"]})
        msgs.append({"role":"user","content":message})
        resp = client.chat.completions.create(model=model_id, messages=msgs, max_tokens=800)
        answer = resp.choices[0].message.content
        if paper_results:
            unique_papers = list(dict.fromkeys([r["paper"] for r in paper_results]))
            answer += chr(10) + chr(10) + "Sources:"
            for p in unique_papers[:3]:
                answer += chr(10) + "  - " + p.replace(".pdf","").replace("_"," ")
        pubmed = get_pubmed(message, n=2)
        if pubmed: answer += chr(10) + "PubMed: " + pubmed
        history.append({"role":"user","content":message})
        history.append({"role":"assistant","content":answer})
        return "", history
    except Exception as e:
        history.append({"role":"user","content":message})
        history.append({"role":"assistant","content":"Error: " + str(e)})
        return "", history

def voice_chat(audio, history):
    if audio is None:
        history.append({"role":"assistant","content":"Please record first."})
        return history
    try:
        client = Groq(api_key=GROQ_KEY)
        with open(audio, "rb") as f:
            tx = client.audio.transcriptions.create(file=("audio.wav", f, "audio/wav"), model="whisper-large-v3")
        paper_context, _ = search_papers(tx.text, n=3)
        system = "You are CardioLab AI. " + KNOWHOW
        if paper_context: system = "You are CardioLab AI. Use these SJSU papers:" + chr(10) + paper_context + chr(10) + KNOWHOW
        msgs = [{"role":"system","content":system}]
        for item in history:
            if isinstance(item, dict): msgs.append({"role":item["role"],"content":item["content"]})
        msgs.append({"role":"user","content":tx.text})
        resp = client.chat.completions.create(model="llama-3.3-70b-versatile", messages=msgs, max_tokens=500)
        history.append({"role":"user","content":"Voice: " + tx.text})
        history.append({"role":"assistant","content":resp.choices[0].message.content})
        return history
    except Exception as e:
        history.append({"role":"assistant","content":"Voice error: " + str(e)})
        return history

# ── PHASE D ────────────────────────────────────────────────
def generate_protocol(experiment_type, specific_params):
    if not GROQ_KEY: return "Error: Add GROQ_API_KEY"
    if not experiment_type: return "Select experiment type"
    try:
        client = Groq(api_key=GROQ_KEY)
        paper_context, _ = search_papers(experiment_type, n=4)
        lab_ctx = {
            "MCL": "Sylgard 184 PDMS 10:1 ratio 48hr cure. Tygon tubing. 70bpm 5L/min.",
            "PIV": "Green laser 532nm. Normal velocity 0.5-2.0 m/s. Shear below 5 Pa.",
            "Thrombogenicity": "Arduino Uno stepper motor 48V. 150mL fresh blood. Sample 0 20 40 60 min. Heska HT5. TAT below 8 ng/mL. PF1.2 below 2.0 nmol/L.",
            "uPAD": "Whatman filter paper. Wax printer 120C. Jaffe reaction picric acid.",
            "FSI": "COMSOL ALE mesh. Blood 1060 kg/m3 0.0035 Pa.s.",
        }
        extra = next((v for k, v in lab_ctx.items() if k.lower() in experiment_type.lower()), "")
        system_msg = ("You are CardioLab AI protocol generator for SJSU. Generate COMPLETE protocol with: "
            "1.OBJECTIVE 2.MATERIALS AND EQUIPMENT 3.SAFETY 4.PROCEDURE 5.DATA COLLECTION "
            "6.ANALYSIS 7.EXPECTED RESULTS with normal ranges 8.TROUBLESHOOTING. "
            "Use exact SJSU CardioLab values.")
        user_msg = "Generate protocol for: " + experiment_type
        if specific_params and specific_params.strip(): user_msg += chr(10) + "Parameters: " + specific_params
        if extra: user_msg += chr(10) + "Context: " + extra
        if paper_context: user_msg += chr(10) + "SJSU papers: " + paper_context[:600]
        resp = client.chat.completions.create(model="llama-3.3-70b-versatile",
            messages=[{"role":"system","content":system_msg},{"role":"user","content":user_msg}], max_tokens=1200)
        return resp.choices[0].message.content
    except Exception as e: return "Error: " + str(e)

def generate_report(data_description, experiment_type, results):
    if not GROQ_KEY: return "Error: Add GROQ_API_KEY"
    try:
        client = Groq(api_key=GROQ_KEY)
        paper_context, _ = search_papers(experiment_type, n=3)
        system_msg = ("You are CardioLab AI report writer for SJSU. Generate professional research report with: "
            "1.ABSTRACT 2.INTRODUCTION 3.MATERIALS AND METHODS 4.RESULTS AND DISCUSSION "
            "5.CONCLUSION 6.RECOMMENDATIONS 7.REFERENCES. Academic style.")
        user_msg = "Write report for: " + experiment_type
        if data_description and data_description.strip(): user_msg += chr(10) + "Description: " + data_description
        if results and results.strip(): user_msg += chr(10) + "Results: " + results
        if paper_context: user_msg += chr(10) + "SJSU papers: " + paper_context[:600]
        resp = client.chat.completions.create(model="llama-3.3-70b-versatile",
            messages=[{"role":"system","content":system_msg},{"role":"user","content":user_msg}], max_tokens=1500)
        return resp.choices[0].message.content
    except Exception as e: return "Error: " + str(e)

def generate_hypothesis(research_area, current_findings):
    if not GROQ_KEY: return "Error: Add GROQ_API_KEY"
    try:
        client = Groq(api_key=GROQ_KEY)
        paper_context, _ = search_papers(research_area, n=3)
        system_msg = ("You are CardioLab AI research assistant for SJSU. Generate 3 testable hypotheses. "
            "For each: H0 null, H1 alternative, rationale, suggested experiment, expected outcome.")
        user_msg = "Hypotheses for: " + research_area
        if current_findings and current_findings.strip(): user_msg += chr(10) + "Findings: " + current_findings
        if paper_context: user_msg += chr(10) + "SJSU papers: " + paper_context[:500]
        resp = client.chat.completions.create(model="llama-3.3-70b-versatile",
            messages=[{"role":"system","content":system_msg},{"role":"user","content":user_msg}], max_tokens=1000)
        return resp.choices[0].message.content
    except Exception as e: return "Error: " + str(e)

# ── ANALYSIS TOOLS ─────────────────────────────────────────
def analyze_upad_photo(image):
    if image is None: return None, "Upload a uPAD photo first."
    try:
        img = Image.fromarray(image) if not isinstance(image, Image.Image) else image
        arr = np.array(img); h, w = arr.shape[:2]
        y1, y2, x1, x2 = int(h*0.35), int(h*0.65), int(w*0.35), int(w*0.65)
        zone = arr[y1:y2, x1:x2]
        R = float(np.mean(zone[:,:,0])); G = float(np.mean(zone[:,:,1])); B = float(np.mean(zone[:,:,2]))
        c = max(0, round(0.018*(R-B)-0.3, 2))
        if c < 1.2: s, a = "Normal", "Monitor annually."
        elif c < 1.5: s, a = "Borderline", "Repeat in 3 months."
        elif c < 3.0: s, a = "Stage 2 CKD", "Consult nephrologist."
        elif c < 6.0: s, a = "Stage 3-4 CKD", "Immediate consultation."
        else: s, a = "Stage 5 CKD", "Emergency care."
        ri = img.copy()
        import PIL.ImageDraw as D; D.Draw(ri).rectangle([x1, y1, x2, y2], outline=(0,255,0), width=3)
        return ri, ("R:" + str(round(R,1)) + " G:" + str(round(G,1)) + " B:" + str(round(B,1)) + chr(10) +
            "Creatinine: " + str(c) + " mg/dL" + chr(10) + "Stage: " + s + chr(10) + "Action: " + a)
    except Exception as e: return None, "Error: " + str(e)

def mk_chart(fn, title, bg, fg, gc, ac, pb):
    fig2, ax = plt.subplots(figsize=(8,5)); fig2.patch.set_facecolor(bg); ax.set_facecolor(pb)
    fn(ax); ax.set_title(title, color=fg, fontweight="bold", fontsize=13, pad=8)
    ax.tick_params(colors=ac, labelsize=10); ax.grid(True, alpha=0.3, color=gc, linestyle="--")
    for sp in ["top","right"]: ax.spines[sp].set_visible(False)
    for sp in ["bottom","left"]: ax.spines[sp].set_color(gc)
    plt.tight_layout(); buf = io.BytesIO()
    plt.savefig(buf, format="png", facecolor=bg, bbox_inches="tight", dpi=130); buf.seek(0)
    res = Image.open(buf).copy(); plt.close(); return res

def analyze_piv_csv(file, theme="White"):
    if file is None: return None, None, None, None, "Upload PIV CSV first."
    try:
        df = pd.read_csv(file.name); cols = [c.lower().strip() for c in df.columns]; df.columns = cols
        num_cols = df.select_dtypes(include=[np.number]).columns.tolist()
        if not num_cols: return None, None, None, None, "No numeric columns."
        bg = "#fff" if theme=="White" else "#0a1628"; fg = "#1a202c" if theme=="White" else "white"
        gc = "#e2e8f0" if theme=="White" else "#2d4a8a"; ac = "#4a5568" if theme=="White" else "#a8b2d8"
        pb = "#f7fafc" if theme=="White" else "#132340"
        x = np.arange(len(df))
        vc = next((c for c in cols if any(k in c for k in ["vel","speed","v_mag"])), num_cols[0] if num_cols else None)
        sc2 = next((c for c in cols if any(k in c for k in ["shear","stress","tau","wss"])), num_cols[1] if len(num_cols)>1 else None)
        tc = next((c for c in cols if "time" in c or "frame" in c), None); xv = df[tc] if tc else x
        def pv(ax):
            if vc:
                ax.plot(xv, df[vc], color="#c1121f", linewidth=2.5, marker="o", markersize=5)
                ax.fill_between(xv, df[vc], alpha=0.15, color="#c1121f")
                ax.axhline(y=2.0, color="#f59e0b", linestyle="--", linewidth=2, label="Risk 2.0 m/s")
                ax.set_ylabel("Velocity (m/s)", color=ac); ax.legend(fontsize=9, labelcolor=fg, facecolor=pb)
        def ps(ax):
            if sc2:
                xp = xv.values if tc else x
                ax.plot(xp, df[sc2], color="#0057a8", linewidth=2.5, marker="s", markersize=5)
                ax.fill_between(xp, df[sc2], alpha=0.15, color="#0057a8")
                ax.axhline(y=5, color="#f59e0b", linestyle="--", linewidth=2, label="Caution 5 Pa")
                ax.axhline(y=10, color="#c1121f", linestyle="--", linewidth=2, label="Risk 10 Pa")
                ax.set_ylabel("Shear (Pa)", color=ac); ax.legend(fontsize=9, labelcolor=fg, facecolor=pb)
        def psc(ax):
            if vc and sc2:
                s3 = ax.scatter(df[vc], df[sc2], c=x, cmap="RdYlGn_r", s=90, edgecolors=fg, linewidth=0.5, zorder=5)
                cb = plt.colorbar(s3, ax=ax, label="Time"); cb.ax.yaxis.label.set_color(fg); cb.ax.tick_params(colors=ac)
                ax.axvline(x=2.0, color="#f59e0b", linestyle="--", linewidth=2); ax.axhline(y=10, color="#c1121f", linestyle="--", linewidth=2)
                ax.set_xlabel("Velocity (m/s)", color=ac); ax.set_ylabel("Shear (Pa)", color=ac)
        def psum(ax):
            ax.axis("off"); risk = []
            st = "CLINICAL SUMMARY" + chr(10) + "="*20 + chr(10) + chr(10)
            for col in num_cols[:3]:
                mn = round(df[col].mean(), 3); mx = round(df[col].max(), 3)
                st += col[:14] + ":" + chr(10) + "  Mean: " + str(mn) + chr(10) + "  Max:  " + str(mx) + chr(10) + chr(10)
                if "vel" in col and mx > 2.0: risk.append("HIGH VELOCITY")
                if "shear" in col and mx > 10: risk.append("HIGH SHEAR")
            bc = "#c1121f" if risk else "#2ecc71"
            st += "="*20 + chr(10) + ("OVERALL: HIGH RISK" if risk else "OVERALL: LOW RISK")
            ax.text(0.05, 0.97, st, transform=ax.transAxes, color=fg, fontsize=10, va="top",
                fontfamily="monospace", bbox=dict(boxstyle="round,pad=0.8", facecolor=pb, edgecolor=bc, linewidth=2.5))
        i1 = mk_chart(pv, "Velocity Profile", bg, fg, gc, ac, pb)
        i2 = mk_chart(ps, "Wall Shear Stress", bg, fg, gc, ac, pb)
        i3 = mk_chart(psc, "Velocity vs Shear", bg, fg, gc, ac, pb)
        i4 = mk_chart(psum, "Clinical Summary", bg, fg, gc, ac, pb)
        ai = ""
        if GROQ_KEY:
            try:
                client = Groq(api_key=GROQ_KEY)
                resp = client.chat.completions.create(model="llama-3.3-70b-versatile",
                    messages=[{"role":"system","content":"PIV expert SJSU CardioLab."},
                    {"role":"user","content":"PIV from 27mm SJM Regent:" + chr(10) + df.describe().to_string()[:500]}], max_tokens=250)
                ai = chr(10) + "AI: " + resp.choices[0].message.content
            except: pass
        return i1, i2, i3, i4, "PIV: " + str(len(df)) + " rows" + ai
    except Exception as e: return None, None, None, None, "Error: " + str(e)

def analyze_tgt_csv(file, theme="White"):
    if file is None: return None, None, None, None, "Upload TGT CSV first."
    try:
        df = pd.read_csv(file.name); cols = [c.lower().strip() for c in df.columns]; df.columns = cols
        num_cols = df.select_dtypes(include=[np.number]).columns.tolist()
        bg = "#fff" if theme=="White" else "#0a1628"; fg = "#1a202c" if theme=="White" else "white"
        gc = "#e2e8f0" if theme=="White" else "#2d4a8a"; ac = "#4a5568" if theme=="White" else "#a8b2d8"
        pb = "#f7fafc" if theme=="White" else "#132340"
        tc = next((c for c in cols if "time" in c or "min" in c), None)
        tatc = next((c for c in cols if "tat" in c), num_cols[0] if num_cols else None)
        pfc = next((c for c in cols if "pf" in c), num_cols[1] if len(num_cols)>1 else None)
        hc = next((c for c in cols if "hemo" in c), num_cols[2] if len(num_cols)>2 else None)
        plc = next((c for c in cols if "platelet" in c or "plt" in c), num_cols[3] if len(num_cols)>3 else None)
        def mk2(dc, color, yl, lim, ll, title, bar=False):
            def fn(ax):
                if dc and dc in df.columns:
                    xp = df[tc].values if tc else range(len(df)); yp = df[dc].values
                    if bar:
                        bs = ax.bar(range(len(yp)), yp, color=color, alpha=0.85, edgecolor=bg, width=0.6)
                        for b, v in zip(bs, yp): ax.text(b.get_x()+b.get_width()/2, b.get_height()+0.5, str(round(v,1)), ha="center", va="bottom", color=fg, fontsize=10, fontweight="bold")
                    else:
                        ax.plot(xp, yp, color=color, linewidth=3, marker="o", markersize=8)
                        ax.fill_between(xp, yp, alpha=0.15, color=color)
                        for xi, yi in zip(xp, yp): ax.annotate(str(round(yi,1)), (xi, yi), textcoords="offset points", xytext=(0,10), ha="center", color=fg, fontsize=10, fontweight="bold")
                    ax.axhline(y=lim, color="#f59e0b", linestyle="--", linewidth=2.5, label=ll)
                    ax.legend(fontsize=10, labelcolor=fg, facecolor=pb); ax.set_ylabel(yl, color=ac)
                    mv = round(float(np.max(yp)), 2)
                    ax.set_title(title + chr(10) + "Max: " + str(mv) + " - " + ("HIGH" if mv>lim else "NORMAL"), color=fg, fontweight="bold", fontsize=12)
            return mk_chart(fn, title, bg, fg, gc, ac, pb)
        i1 = mk2(tatc, "#c1121f", "TAT (ng/mL)", 8, "Normal: 8", "TAT")
        i2 = mk2(pfc, "#0057a8", "PF1.2", 2.0, "Normal: 2.0", "PF1.2")
        i3 = mk2(hc, "#2ecc71", "Free Hgb (mg/L)", 20, "Normal: 20", "Free Hemoglobin", bar=True)
        i4 = mk2(plc, "#e8a020", "Platelets", 150, "Normal>150", "Platelets")
        ai = ""
        if GROQ_KEY:
            try:
                client = Groq(api_key=GROQ_KEY)
                resp = client.chat.completions.create(model="llama-3.3-70b-versatile",
                    messages=[{"role":"system","content":"Hematology expert. Thrombogenicity risk."},
                    {"role":"user","content":"TGT:" + chr(10) + df.describe().to_string()[:500]}], max_tokens=250)
                ai = chr(10) + "AI: " + resp.choices[0].message.content
            except: pass
        return i1, i2, i3, i4, "TGT: " + str(len(df)) + " rows" + ai
    except Exception as e: return None, None, None, None, "Error: " + str(e)

def generate_image(prompt):
    if not prompt.strip(): return None, "Enter description.", ""
    if not HF_TOKEN: return None, "Add HF_TOKEN.", ""
    try:
        enhanced, desc = prompt, ""
        if GROQ_KEY:
            try:
                client = Groq(api_key=GROQ_KEY)
                resp = client.chat.completions.create(model="llama-3.3-70b-versatile",
                    messages=[{"role":"system","content":"Format: DESCRIPTION: [2 sentences] PROMPT: [detailed image prompt]"},
                    {"role":"user","content":"Biomedical image: " + prompt}], max_tokens=200)
                full = resp.choices[0].message.content
                if "DESCRIPTION:" in full and "PROMPT:" in full:
                    desc = full.split("DESCRIPTION:")[1].split("PROMPT:")[0].strip()
                    enhanced = full.split("PROMPT:")[1].strip()
            except: pass
        headers = {"Authorization": "Bearer " + HF_TOKEN, "Content-Type": "application/json"}
        for url in ["https://router.huggingface.co/hf-inference/models/black-forest-labs/FLUX.1-schnell",
                    "https://router.huggingface.co/hf-inference/models/stabilityai/stable-diffusion-xl-base-1.0"]:
            try:
                r = requests.post(url, headers=headers, json={"inputs":enhanced,"parameters":{"num_inference_steps":8}}, timeout=60)
                if r.status_code == 200: return Image.open(io.BytesIO(r.content)), "Generated!", desc
            except: continue
        return None, "Models busy.", desc
    except Exception as e: return None, "Error: " + str(e), ""

def piv_manual(v, s, h):
    vr = "HIGH-stenosis" if float(v)>2.0 else "NORMAL"
    sr = "HIGH-thrombosis" if float(s)>10 else "ELEVATED" if float(s)>5 else "NORMAL"
    return "Velocity: " + str(v) + " m/s - " + vr + chr(10) + "Shear: " + str(s) + " Pa - " + sr + chr(10) + "HR: " + str(h) + " bpm"

def tgt_manual(t, p, h, pl, tm):
    risk = sum([float(t)>15, float(p)>2.0, float(h)>50, float(pl)<150])
    return "TAT:" + str(t) + " PF1.2:" + str(p) + chr(10) + "Hemo:" + str(h) + " Plt:" + str(pl) + chr(10) + ("HIGH RISK" if risk>=3 else "MODERATE" if risk>=2 else "LOW RISK")

# ── UI ─────────────────────────────────────────────────────
with gr.Blocks(title="CardioLab AI - SJSU", css=CSS) as demo:
    gr.HTML(HEADER)
    gr.HTML("""<div style="background:#f0fdf4;border:1px solid #bbf7d0;border-radius:8px;padding:8px 16px;margin:6px 0;text-align:center;">
    <span style="color:#166534;font-size:0.8em;font-weight:500;">RAG Active: 417 chunks from 16 SJSU papers &nbsp;·&nbsp; Fine-tuned Model &nbsp;·&nbsp; Select model using radio buttons in Chat tab</span></div>""")

    with gr.Tabs():

        with gr.Tab("Chat"):
            with gr.Row():
                with gr.Column(scale=1, min_width=200):
                    gr.HTML("""<div style="background:#fef2f2;border:1px solid #fecaca;border-radius:10px;padding:12px;margin-bottom:8px;">
                    <div style="display:flex;align-items:center;gap:6px;margin-bottom:3px;">
                    <svg width="12" height="11" viewBox="0 0 100 90"><path d="M50 85 C50 85 5 55 5 30 C5 15 18 5 30 5 C38 5 45 9 50 15 C55 9 62 5 70 5 C82 5 95 15 95 30 C95 55 50 85 50 85Z" fill="#c1121f"/></svg>
                    <span style="color:#c1121f;font-weight:700;font-size:0.82em;">CardioLab</span></div>
                    <div style="color:#9ca3af;font-size:0.7em;">Conversations</div></div>""")
                    new_chat_btn = gr.Button("+ New Chat", variant="secondary")
                    session_dropdown = gr.Dropdown(choices=get_session_list(), label="Saved Sessions", interactive=True)
                    load_btn = gr.Button("Load Session", variant="primary")
                    session_name_box = gr.Textbox(placeholder="Name this session...", label="Session Name", lines=1)
                    with gr.Row():
                        save_btn = gr.Button("Save", variant="primary", scale=2)
                        delete_btn = gr.Button("Del", variant="secondary", scale=1)
                    session_status = gr.Textbox(label="", lines=1, interactive=False, container=False)

                with gr.Column(scale=4):
                    chat_model_radio = gr.Radio(
                        choices=list(CHAT_MODELS.keys()),
                        value="Llama 3.3 70B (Best)",
                        label="Select AI Model",
                        container=True
                    )
                    chatbot = gr.Chatbot(label="", height=400, show_label=False, container=False)
                    with gr.Row():
                        msg_box = gr.Textbox(placeholder="Ask anything — AI searches 16 SJSU papers + PubMed...", label="", lines=2, scale=5, container=False)
                        with gr.Column(scale=1, min_width=80):
                            send_btn = gr.Button("Send", variant="primary")
                            clear_btn = gr.Button("Clear", variant="secondary")

            send_btn.click(research_chat, inputs=[msg_box, chatbot, chat_model_radio], outputs=[msg_box, chatbot])
            msg_box.submit(research_chat, inputs=[msg_box, chatbot, chat_model_radio], outputs=[msg_box, chatbot])
            clear_btn.click(lambda: ([], ""), outputs=[chatbot, msg_box])
            new_chat_btn.click(new_chat, outputs=[chatbot, msg_box, session_status])
            save_btn.click(save_session, inputs=[chatbot, session_name_box], outputs=[session_status, session_dropdown])
            load_btn.click(load_session, inputs=session_dropdown, outputs=[chatbot, session_status])
            delete_btn.click(delete_session, inputs=session_dropdown, outputs=[session_status, session_dropdown])

        with gr.Tab("Voice"):
            voice_chatbot = gr.Chatbot(label="", height=360, show_label=False)
            audio_input = gr.Audio(sources=["microphone"], type="filepath", label="Record Question")
            with gr.Row():
                voice_btn = gr.Button("Ask by Voice", variant="primary")
                voice_clear = gr.Button("Clear", variant="secondary")
            voice_btn.click(voice_chat, inputs=[audio_input, voice_chatbot], outputs=voice_chatbot)
            voice_clear.click(lambda: [], outputs=voice_chatbot)

        with gr.Tab("Papers"):
            gr.Markdown("### Search PubMed + Semantic Scholar + SJSU ScholarWorks")
            with gr.Row():
                search_input = gr.Textbox(placeholder="e.g. bileaflet mechanical heart valve thrombogenicity hemodynamics", label="Research Topic", scale=4)
                search_btn = gr.Button("Search", variant="primary", scale=1)
            search_output = gr.Textbox(label="Results", lines=22)
            search_btn.click(quick_search, inputs=search_input, outputs=search_output)
            search_input.submit(quick_search, inputs=search_input, outputs=search_output)

        with gr.Tab("PIV CSV"):
            with gr.Row():
                piv_file = gr.File(label="Upload PIV CSV", file_types=[".csv"], scale=3)
                piv_theme = gr.Radio(["White","Dark"], value="White", label="Theme", scale=1)
            piv_btn = gr.Button("Analyze PIV Data", variant="primary")
            piv_result = gr.Textbox(label="AI Analysis", lines=4)
            with gr.Row():
                piv_c1 = gr.Image(label="Velocity Profile", type="pil")
                piv_c2 = gr.Image(label="Shear Stress", type="pil")
            with gr.Row():
                piv_c3 = gr.Image(label="Velocity vs Shear", type="pil")
                piv_c4 = gr.Image(label="Clinical Summary", type="pil")
            piv_btn.click(analyze_piv_csv, inputs=[piv_file, piv_theme], outputs=[piv_c1, piv_c2, piv_c3, piv_c4, piv_result])

        with gr.Tab("TGT CSV"):
            with gr.Row():
                tgt_file = gr.File(label="Upload TGT CSV", file_types=[".csv"], scale=3)
                tgt_theme = gr.Radio(["White","Dark"], value="White", label="Theme", scale=1)
            tgt_btn = gr.Button("Analyze TGT Data", variant="primary")
            tgt_result = gr.Textbox(label="AI Assessment", lines=4)
            with gr.Row():
                tgt_c1 = gr.Image(label="TAT", type="pil"); tgt_c2 = gr.Image(label="PF1.2", type="pil")
            with gr.Row():
                tgt_c3 = gr.Image(label="Hemoglobin", type="pil"); tgt_c4 = gr.Image(label="Platelets", type="pil")
            tgt_btn.click(analyze_tgt_csv, inputs=[tgt_file, tgt_theme], outputs=[tgt_c1, tgt_c2, tgt_c3, tgt_c4, tgt_result])

        with gr.Tab("uPAD"):
            with gr.Row():
                with gr.Column():
                    photo_input = gr.Image(label="Upload uPAD Photo", type="numpy", height=260)
                    analyze_btn = gr.Button("Analyze uPAD Photo", variant="primary")
                with gr.Column():
                    photo_img = gr.Image(label="Detection Zone", type="pil", height=260)
                    photo_text = gr.Textbox(label="CKD Result", lines=8)
            analyze_btn.click(analyze_upad_photo, inputs=photo_input, outputs=[photo_img, photo_text])
            gr.Markdown("**Manual RGB:**")
            with gr.Row():
                r = gr.Number(label="R", value=210); g = gr.Number(label="G", value=140); b = gr.Number(label="B", value=80)
                out3 = gr.Textbox(label="Result", lines=3)
            gr.Button("Analyze RGB", variant="secondary").click(
                lambda r, g, b: "Creatinine: " + str(max(0,round(0.02*(r-b)-0.5,2))) + " mg/dL" + chr(10) +
                    ("Normal" if max(0,round(0.02*(r-b)-0.5,2)) < 1.2 else "Borderline" if max(0,round(0.02*(r-b)-0.5,2)) < 1.5 else "CKD"),
                inputs=[r, g, b], outputs=out3)

        with gr.Tab("AI Image"):
            with gr.Row():
                img_prompt = gr.Textbox(placeholder="e.g. 27mm bileaflet mechanical heart valve cross section", label="Describe image", lines=2, scale=4)
                with gr.Column(scale=1):
                    img_btn = gr.Button("Generate", variant="primary")
                    img_status = gr.Textbox(label="Status", lines=1)
            img_desc = gr.Textbox(label="AI Description", lines=2, interactive=False)
            img_output = gr.Image(label="Generated Image", type="pil", height=400)
            img_btn.click(generate_image, inputs=img_prompt, outputs=[img_output, img_status, img_desc])

        with gr.Tab("PIV Manual"):
            with gr.Row():
                with gr.Column():
                    v = gr.Number(label="Max Velocity m/s", value=1.8)
                    s = gr.Number(label="Wall Shear Pa", value=6.5)
                    h = gr.Number(label="Heart Rate bpm", value=72)
                    piv_out = gr.Textbox(label="Result", lines=4)
                    gr.Button("Analyze PIV", variant="primary").click(piv_manual, inputs=[v, s, h], outputs=piv_out)

        with gr.Tab("TGT Manual"):
            with gr.Row():
                with gr.Column():
                    t1 = gr.Number(label="TAT ng/mL", value=18); t2 = gr.Number(label="PF1.2", value=2.5)
                    t3 = gr.Number(label="Hemoglobin mg/L", value=60); t4 = gr.Number(label="Platelets", value=140)
                    t5 = gr.Number(label="Time min", value=40); out2 = gr.Textbox(label="Result", lines=6)
                    gr.Button("Analyze TGT", variant="primary").click(tgt_manual, inputs=[t1, t2, t3, t4, t5], outputs=out2)

        with gr.Tab("Protocol Generator"):
            gr.Markdown("### Generate complete lab protocols from SJSU CardioLab knowledge")
            with gr.Row():
                with gr.Column(scale=1):
                    proto_type = gr.Dropdown(
                        choices=["MCL Setup", "PIV Experiment", "Thrombogenicity Tester Blood Clotting Test",
                                 "uPAD Fabrication", "uPAD Creatinine Test", "FSI COMSOL Simulation", "Valve Testing"],
                        value="Thrombogenicity Tester Blood Clotting Test", label="Experiment Type")
                    proto_params = gr.Textbox(placeholder="e.g. 27mm SJM valve 70bpm porcine blood", label="Specific Parameters", lines=2)
                    proto_btn = gr.Button("Generate Protocol", variant="primary")
                with gr.Column(scale=2):
                    proto_output = gr.Textbox(label="Generated Protocol", lines=28)
            proto_btn.click(generate_protocol, inputs=[proto_type, proto_params], outputs=proto_output)

        with gr.Tab("Report Writer"):
            gr.Markdown("### Generate professional research reports")
            with gr.Row():
                with gr.Column(scale=1):
                    report_exp = gr.Dropdown(
                        choices=["MCL PIV Flow Analysis", "TGT Thrombogenicity Study", "uPAD CKD Detection",
                                 "FSI Simulation Study", "Heart Valve Comparison"],
                        value="TGT Thrombogenicity Study", label="Study Type")
                    report_desc = gr.Textbox(placeholder="e.g. TGT with 27mm SJM bileaflet at 70bpm 150mL porcine blood", label="Experiment Description", lines=3)
                    report_results = gr.Textbox(placeholder="e.g. TAT=12.3 PF1.2=2.8 Hemo=45 Plt=142", label="Your Results", lines=2)
                    report_btn = gr.Button("Generate Report", variant="primary")
                with gr.Column(scale=2):
                    report_output = gr.Textbox(label="Generated Report", lines=28)
            report_btn.click(generate_report, inputs=[report_desc, report_exp, report_results], outputs=report_output)

        with gr.Tab("Hypothesis Generator"):
            gr.Markdown("### Generate testable research hypotheses")
            with gr.Row():
                with gr.Column(scale=1):
                    hyp_area = gr.Dropdown(
                        choices=["Bileaflet MHV Thrombogenicity", "uPAD CKD Detection Accuracy",
                                 "PIV Flow Characterization", "FSI Simulation Validation", "Valve Design Comparison"],
                        value="Bileaflet MHV Thrombogenicity", label="Research Area")
                    hyp_findings = gr.Textbox(placeholder="Current observations from your experiments", label="Current Findings", lines=3)
                    hyp_btn = gr.Button("Generate Hypotheses", variant="primary")
                with gr.Column(scale=2):
                    hyp_output = gr.Textbox(label="Research Hypotheses", lines=25)
            hyp_btn.click(generate_hypothesis, inputs=[hyp_area, hyp_findings], outputs=hyp_output)

    gr.HTML("""<div style="text-align:center;padding:12px;border-top:1px solid #e2e8f0;background:#f8fafc;margin-top:8px;">
    <span style="color:#9ca3af;font-size:0.72em;">CardioLab AI v40 &nbsp;·&nbsp; SJSU Biomedical Engineering &nbsp;·&nbsp;
    Inspired by <a href="https://github.com/snap-stanford/Biomni" style="color:#c1121f;text-decoration:none;">Biomni Stanford</a>
    &nbsp;·&nbsp; Apache 2.0 &nbsp;·&nbsp; $0 Cost</span></div>""")

demo.launch()