"""
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 started"
def get_pubmed_chat(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 ""
# ── PHASE C: BIOGPT + CLINICALTRIALS + WEEKLY UPDATE ──────────────
def search_biogpt(query):
"""Search BioGPT — trained on 15M PubMed papers via HuggingFace API"""
if not HF_TOKEN: return ""
try:
headers = {"Authorization": "Bearer "+HF_TOKEN}
# Use BioGPT for biomedical question answering
payload = {"inputs": query+" [SEP] Answer based on biomedical literature:"}
r = requests.post(
"https://api-inference.huggingface.co/models/microsoft/BioGPT-Large-PubMedQA",
headers=headers, json=payload, timeout=20
)
if r.status_code == 200:
result = r.json()
if isinstance(result, list) and len(result) > 0:
text = result[0].get("generated_text","")
# Extract just the answer part
if "[SEP]" in text:
text = text.split("[SEP]")[-1].strip()
return text[:400] if text else ""
return ""
except: return ""
def search_clinical_trials(query, n=5):
"""Search ClinicalTrials.gov for heart valve and CKD trials"""
try:
r = requests.get(
"https://clinicaltrials.gov/api/v2/studies",
params={
"query.term": query,
"filter.overallStatus": "RECRUITING|COMPLETED",
"pageSize": n,
"format": "json",
"fields": "NCTId,BriefTitle,OverallStatus,Phase,StartDate,Condition"
},
timeout=12
)
if r.status_code != 200: return []
studies = r.json().get("studies",[])
results = []
for s in studies:
proto = s.get("protocolSection",{})
ident = proto.get("identificationModule",{})
status = proto.get("statusModule",{})
nct = ident.get("nctId","")
title = ident.get("briefTitle","")
phase = status.get("phase","")
overall = status.get("overallStatus","")
if nct and title:
results.append({
"nct": nct,
"title": title,
"status": overall,
"phase": phase,
"url": "https://clinicaltrials.gov/study/"+nct
})
return results
except: return []
def get_weekly_pubmed_update(topics=None):
"""Get papers published in last 7 days on CardioLab topics"""
if topics is None:
topics = [
"mechanical heart valve thrombogenicity",
"microfluidic creatinine CKD diagnosis",
"PIV hemodynamics prosthetic valve",
"Mock Circulatory Loop cardiac",
"bileaflet valve fluid structure interaction"
]
all_new = []
try:
from datetime import datetime, timedelta
week_ago = (datetime.now() - timedelta(days=7)).strftime("%Y/%m/%d")
for topic in topics:
r = requests.get(
"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi",
params={
"db":"pubmed",
"term":topic,
"mindate":week_ago,
"datetype":"pdat",
"retmax":3,
"retmode":"json",
"sort":"date"
},
timeout=10
)
ids = r.json()["esearchresult"]["idlist"]
for pmid in ids:
all_new.append({
"pmid": pmid,
"topic": topic,
"url": "https://pubmed.ncbi.nlm.nih.gov/"+pmid
})
return all_new
except: return []
def full_research_search(query, search_model="Llama 3.3 70B (Best)"):
"""Complete search across ALL sources including Phase C additions"""
if not query.strip(): return "Please enter a research topic."
model_id = CHAT_MODELS.get(search_model, "llama-3.3-70b-versatile")
expanded = expand_query_ai(query, model_id) if GROQ_KEY else query
# All search sources
pubmed = fetch_pubmed(expanded, n=6)
scholar = fetch_scholar(expanded, n=5)
europe = fetch_europe_pmc(expanded, n=4)
trials = search_clinical_trials(query, n=4)
weekly = get_weekly_pubmed_update()
biogpt_answer = search_biogpt(query)
# Format output
out = "QUERY: "+query+chr(10)
out += "AI EXPANDED: "+expanded+chr(10)
out += "SOURCES: PubMed + Scholar + EuropePMC + ClinicalTrials + SJSU + BioGPT"+chr(10)
out += "="*50+chr(10)+chr(10)
# BioGPT answer first
if biogpt_answer:
out += "BIOGPT ANSWER (trained on 15M PubMed papers):"+chr(10)
out += biogpt_answer+chr(10)+chr(10)
out += "="*50+chr(10)+chr(10)
# PubMed results
if pubmed:
out += "PUBMED ("+str(len(pubmed))+" papers):"+chr(10)
for p in pubmed[:6]:
out += p["title"][:85]+" ("+p["year"]+")"+chr(10)
out += " "+p["url"]+chr(10)+chr(10)
# Scholar results
if scholar:
out += "SEMANTIC SCHOLAR ("+str(len(scholar))+" papers):"+chr(10)
for p in scholar[:5]:
out += p["title"][:85]+" ("+p["year"]+")"
if p["citations"] not in ("N/A","","0"): out += " | "+p["citations"]+" citations"
out += chr(10)+" "+p["url"]+chr(10)+chr(10)
# Clinical trials
if trials:
out += "CLINICALTRIALS.GOV ("+str(len(trials))+" trials):"+chr(10)
for t in trials:
out += t["title"][:80]+" | "+t["status"]+" | "+t.get("phase","")+" "+chr(10)
out += " "+t["url"]+chr(10)+chr(10)
# Weekly updates
weekly_relevant = [w for w in weekly if any(
kw in query.lower() for kw in ["valve","heart","ckd","creatinine","piv","tgt","mcl"]
)]
if weekly_relevant:
out += "NEW THIS WEEK (last 7 days):"+chr(10)
for w in weekly_relevant[:5]:
out += " "+w["url"]+" ["+w["topic"][:40]+"]"+chr(10)
# SJSU ScholarWorks
out += chr(10)+"SJSU SCHOLARWORKS:"+chr(10)
out += " https://scholarworks.sjsu.edu/do/search/?q="+requests.utils.quote(query)+"&context=6781027"
return out
def research_chat(message, history, chat_model="Llama 3.3 70B (Best)"):
if not message.strip(): return "", history
paper_context, paper_results = search_papers(message, n=4)
# Use fine-tuned CardioLab model if selected
if chat_model == "CardioLab Fine-tuned (SJSU)" and CARDIOLAB_MODEL_LOADED:
answer = answer_with_cardiolab_model(message, paper_context)
if answer:
if paper_results:
unique_papers = list(dict.fromkeys([r["paper"] for r in paper_results]))
answer += chr(10)+chr(10)+"Sources from SJSU CardioLab papers:"
for p in unique_papers[:3]:
answer += chr(10)+" - "+p.replace('.pdf','').replace('_',' ')
pubmed = get_pubmed_chat(message, n=2)
if pubmed: answer += chr(10)+"PubMed: "+pubmed
history.append({"role":"user","content":message})
history.append({"role":"assistant","content":"[CardioLab Fine-tuned Model] "+answer})
return "", history
# Fall back to Groq models
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)
if paper_context:
system_prompt = ("You are CardioLab AI for SJSU Biomedical Engineering. "
"Answer using SJSU CardioLab research papers below. "
"Always cite the paper name when using specific data."+chr(10)+chr(10)+
"SJSU CARDIOLAB PAPERS:"+chr(10)+paper_context+chr(10)+chr(10)+
"ADDITIONAL KNOWLEDGE: "+KNOWHOW)
else:
system_prompt = "You are CardioLab AI for SJSU Biomedical Engineering. Expert in MHV MCL PIV TGT uPAD CKD FSI. "+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 from SJSU CardioLab papers:"
for p in unique_papers[:3]:
answer += chr(10)+" - "+p.replace('.pdf','').replace('_',' ')
pubmed = get_pubmed_chat(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 your question 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
def expand_query_ai(query):
if not GROQ_KEY: return query
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 for heart valves hemodynamics PIV thrombogenicity FSI microfluidics CKD. Return ONLY terms."},
{"role":"user","content":"Optimize: "+query}],max_tokens=80)
return resp.choices[0].message.content.strip() or query
except: return query
def quick_search(query, search_model="Llama 3.3 70B (Best)"):
if not query.strip(): return "Please enter a topic."
expanded = expand_query_ai(query)
results = []
try:
forced = expanded+" AND (heart valve OR hemodynamics OR microfluidic OR thrombogen OR creatinine OR PIV OR CFD 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"]
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)
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 ""
results.append({"source":"PubMed","title":str(title),"year":year,"url":"https://pubmed.ncbi.nlm.nih.gov/"+pmid,"citations":"N/A"})
except: continue
except: pass
try:
r = requests.get("https://api.semanticscholar.org/graph/v1/paper/search",
params={"query":expanded,"limit":6,"fields":"title,year,url,citationCount"},timeout=12)
for p in r.json().get("data",[]):
year = p.get("year",0) or 0
if int(year) >= 2015:
results.append({"source":"Scholar","title":p.get("title",""),"year":str(year),"url":p.get("url",""),"citations":str(p.get("citationCount",0))})
except: pass
out = "QUERY: "+query+chr(10)+"AI EXPANDED: "+expanded+chr(10)+"="*45+chr(10)+chr(10)
groups = {"PubMed":[],"Scholar":[]}
seen = set()
for r in results:
key = r["title"][:50].lower()
if key not in seen and r["url"]:
seen.add(key); groups[r["source"]].append(r)
for source, papers in groups.items():
if not papers: continue
out += "--- "+source+" ---"+chr(10)
for p in papers[:8]:
out += p["title"][:85]+" ("+p["year"]+")"
if p["citations"] not in ("N/A","","0"): out += " | "+p["citations"]+" citations"
out += chr(10)+" "+p["url"]+chr(10)+chr(10)
out += "--- SJSU ScholarWorks ---"+chr(10)
out += "https://scholarworks.sjsu.edu/do/search/?q="+requests.utils.quote(query)+"&context=6781027"
return out
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,G,B = float(np.mean(zone[:,:,0])),float(np.mean(zone[:,:,1])),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,("uPAD ANALYSIS"+chr(10)+"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="High 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. Give 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 to Space secrets.","";
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)+"RESULT: "+("HIGH RISK" if risk>=3 else "MODERATE" if risk>=2 else "LOW RISK")
with gr.Blocks(title="CardioLab AI - SJSU", css=CSS) as demo:
gr.HTML(HEADER)
papers_count = len(set(m["paper"] for m in METADATA)) if PAPERS_LOADED else 0
model_status = "CardioLab Fine-tuned Model LOADED" if CARDIOLAB_MODEL_LOADED else "Fine-tuned model loading..."
rag_status = f"RAG: {len(CHUNKS)} chunks from {papers_count} SJSU papers" if PAPERS_LOADED else "RAG: loading..."
gr.HTML(f'''
{rag_status} | {model_status} | Select "CardioLab Fine-tuned (SJSU)" in Model dropdown to use your custom model!
''')
with gr.Tabs():
with gr.Tab("Chat"):
with gr.Row():
with gr.Column(scale=1, min_width=200):
gr.HTML('''