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Saicharan21 commited on 27 days ago

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1 Parent(s): 9f268b2

Upload app.py with huggingface_hub

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app.py +550 -337

app.py CHANGED Viewed

@@ -9,421 +9,596 @@ from groq import Groq
 from PIL import Image
 from datetime import datetime
 from huggingface_hub import HfApi, hf_hub_download
-from huggingface_hub.utils import EntryNotFoundError
 GROQ_KEY = os.environ.get("GROQ_API_KEY", "")
 HF_TOKEN = os.environ.get("HF_TOKEN", "")
 HISTORY_REPO = "Saicharan21/cardiolab-chat-history"
 KNOWHOW = ("MCL: Sylgard 184 PDMS 10:1 ratio 48hr cure green laser PIV 70bpm 5L/min. "
-    "TGT: Arduino Uno Stepper Motor 150mL blood sampled at 0 20 40 60min measures TAT PF1.2 hemolysis platelets. "
-    "uPAD: Jaffe reaction creatinine plus picric acid gives orange-red color normal 0.6-1.2 mg/dL CKD above 1.5. "
-    "MHV: 27mm SJM Regent bileaflet also trileaflet monoleaflet pediatric. "
-    "Equipment: Heska HT5 hematology analyzer time-resolved PIV Tygon tubing Arduino Uno.")
 CSS = """
-body, .gradio-container { background: #f0f4f8 !important; }
-.tab-nav { background: #ffffff !important; border-bottom: 2px solid #e2e8f0 !important; padding: 4px 5px 0 5px !important; display: flex !important; flex-wrap: wrap !important; gap: 3px !important; }
-.tab-nav button { background: #f7fafc !important; color: #2d3748 !important; border: 1px solid #e2e8f0 !important; border-radius: 6px 6px 0 0 !important; padding: 8px 10px !important; font-weight: 600 !important; font-size: 0.8em !important; white-space: nowrap !important; }
-.tab-nav button:hover { background: #ebf4ff !important; color: #1a237e !important; }
-.tab-nav button.selected { background: linear-gradient(135deg, #e63946, #c1121f) !important; color: #ffffff !important; font-weight: 700 !important; }
-button.primary { background: linear-gradient(135deg, #e63946 0%, #c1121f 100%) !important; color: white !important; border: none !important; border-radius: 8px !important; font-weight: 700 !important; }
-button.secondary { background: #edf2f7 !important; color: #4a5568 !important; border: 1px solid #cbd5e0 !important; border-radius: 8px !important; }
-textarea, input[type=number], input[type=text] { background: #f7fafc !important; color: #1a202c !important; border: 1px solid #cbd5e0 !important; border-radius: 8px !important; }
-.message.user { background: linear-gradient(135deg, #e63946, #c1121f) !important; color: white !important; }
-.message.bot { background: #ebf4ff !important; color: #1a202c !important; border: 1px solid #bee3f8 !important; }
-label span { color: #2b6cb0 !important; font-weight: 600 !important; font-size: 0.85em !important; text-transform: uppercase !important; }
 """
-# ── PERSISTENT HISTORY FUNCTIONS ──────────────────────────────────
-def get_history_api():
-    if not HF_TOKEN: return None
-    return HfApi(token=HF_TOKEN)
 def load_all_sessions():
     if not HF_TOKEN: return {}
     try:
-        api = get_history_api()
-        path = hf_hub_download(
-            repo_id=HISTORY_REPO,
-            filename="chat_history.json",
-            repo_type="dataset",
-            token=HF_TOKEN
-        )
-        with open(path, "r") as f:
-            return json.load(f)
-    except Exception:
-        return {}
 def save_all_sessions(sessions):
     if not HF_TOKEN: return False
     try:
-        api = get_history_api()
-        content = json.dumps(sessions, indent=2)
-        api.upload_file(
-            path_or_fileobj=content.encode(),
-            path_in_repo="chat_history.json",
-            repo_id=HISTORY_REPO,
-            repo_type="dataset",
-            token=HF_TOKEN,
-            commit_message="Update chat history"
-        )
         return True
-    except Exception as e:
-        print("Save error:", e)
-        return False
 def get_session_list():
-    sessions = load_all_sessions()
-    if not sessions:
-        return ["No saved sessions yet"]
-    return list(sessions.keys())
-def load_session(session_name):
-    if not session_name or session_name == "No saved sessions yet":
-        return [], "No session loaded"
     sessions = load_all_sessions()
-    if session_name in sessions:
-        history = sessions[session_name]["messages"]
-        return history, "Loaded: " + session_name + " (" + str(len(history)) + " messages)"
-    return [], "Session not found"
-def save_session(history, session_name):
-    if not history:
-        return "Nothing to save — chat is empty", gr.update()
-    if not session_name.strip():
-        session_name = "Session " + datetime.now().strftime("%Y-%m-%d %H:%M")
     sessions = load_all_sessions()
-    sessions[session_name] = {
-        "messages": history,
-        "saved_at": datetime.now().isoformat(),
-        "message_count": len(history)
-    }
-    success = save_all_sessions(sessions)
-    if success:
-        return "Saved: " + session_name, gr.update(choices=get_session_list(), value=session_name)
-    return "Save failed — check HF_TOKEN in Space secrets", gr.update()
-def delete_session(session_name):
-    if not session_name or session_name == "No saved sessions yet":
-        return "No session selected", gr.update()
     sessions = load_all_sessions()
-    if session_name in sessions:
-        del sessions[session_name]
-        save_all_sessions(sessions)
-        new_list = get_session_list()
-        return "Deleted: " + session_name, gr.update(choices=new_list, value=new_list[0] if new_list else None)
-    return "Session not found", gr.update()
-# ── CHAT FUNCTIONS ────────────────────────────────────────────────
-def get_pubmed(query, n=5):
     try:
         r = requests.get("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi",
-            params={"db":"pubmed","term":query+" AND (mechanical heart valve OR microfluidic OR CKD OR thrombogenicity)","retmax":n,"retmode":"json","sort":"date"},timeout=10)
         ids = r.json()["esearchresult"]["idlist"]
-        if not ids: return ""
-        return chr(10).join(["https://pubmed.ncbi.nlm.nih.gov/"+i for i in ids])
     except: return ""
 def quick_search(query):
     if not query.strip(): return "Please enter a topic."
-    pubmed = get_pubmed(query, n=8)
     try:
-        r = requests.get("https://api.semanticscholar.org/graph/v1/paper/search",
-            params={"query":query+" biomedical","limit":5,"fields":"title,year,url"},timeout=10)
-        papers = r.json().get("data",[])
-        scholar = chr(10).join([p.get("title","")[:80]+" ("+str(p.get("year",""))+")"+chr(10)+"   "+p.get("url","") for p in papers if p.get("url","")])
-    except: scholar = ""
-    return "PUBMED:"+chr(10)+pubmed+chr(10)+chr(10)+"SCHOLAR:"+chr(10)+scholar
-def research_chat(message, history):
     if not GROQ_KEY:
         history.append({"role":"user","content":message})
-        history.append({"role":"assistant","content":"Error: Add GROQ_API_KEY to Space Settings Secrets."})
         return "", history
     try:
         client = Groq(api_key=GROQ_KEY)
-        msgs = [{"role":"system","content":"You are CardioLab AI. Expert in MHV MCL PIV TGT uPAD CKD FSI. Remember full conversation. Never invent URLs. "+KNOWHOW}]
         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="llama-3.3-70b-versatile",messages=msgs,max_tokens=700)
         answer = resp.choices[0].message.content
-        pubmed = get_pubmed(message, n=3)
-        if pubmed: answer += chr(10)+chr(10)+"PUBMED:"+chr(10)+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")
-        msgs = [{"role":"system","content":"You are CardioLab AI. "+KNOWHOW}]
         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
-# ── 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,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 needed."
-        result_img = img.copy()
-        import PIL.ImageDraw as D
-        draw = D.Draw(result_img)
-        draw.rectangle([x1,y1,x2,y2], outline=(0,255,0), width=3)
-        return result_img, ("uPAD ANALYSIS"+chr(10)+"━"*22+chr(10)+
-            "R:"+str(round(R,1))+" G:"+str(round(G,1))+" B:"+str(round(B,1))+chr(10)+
-            "Orange Score: "+str(round(R-B,1))+chr(10)+"━"*22+chr(10)+
-            "CREATININE: "+str(c)+" mg/dL"+chr(10)+"CKD STAGE: "+s+chr(10)+
-            "ACTION: "+a+chr(10)+"Confirm: Heska Element HT5")
-    except Exception as e: return None, "Error: "+str(e)
 def analyze_piv_csv(file, theme="White"):
-    if file is None: return None,None,None,None,"Upload a PIV CSV file 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 found."
-        bg = "#ffffff" 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)
-        sc = 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 mk(fn, title):
-            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()
-            buf2=io.BytesIO(); plt.savefig(buf2,format="png",facecolor=bg,bbox_inches="tight",dpi=130); buf2.seek(0)
-            res=Image.open(buf2).copy(); plt.close(); return res
         def pv(ax):
             if vc:
-                ax.plot(xv,df[vc],color="#e63946",linewidth=2.5,marker="o",markersize=5)
-                ax.fill_between(xv,df[vc],alpha=0.2,color="#e63946")
-                ax.axhline(y=2.0,color="#f59e0b",linestyle="--",linewidth=2,label="Risk: 2.0 m/s")
-                ax.set_ylabel("Velocity (m/s)",color=ac,fontsize=11)
-                ax.set_xlabel(tc or "Sample",color=ac,fontsize=11)
-                ax.legend(fontsize=9,labelcolor=fg,facecolor=pb)
         def ps(ax):
-            if sc:
                 xp = xv.values if tc else x
-                ax.plot(xp,df[sc],color="#4361ee",linewidth=2.5,marker="s",markersize=5)
-                ax.fill_between(xp,df[sc],alpha=0.2,color="#4361ee")
-                ax.axhline(y=5,color="#f59e0b",linestyle="--",linewidth=2,label="Caution: 5 Pa")
-                ax.axhline(y=10,color="#e63946",linestyle="--",linewidth=2,label="High risk: 10 Pa")
-                ax.set_ylabel("Shear Stress (Pa)",color=ac,fontsize=11)
-                ax.set_xlabel(tc or "Sample",color=ac,fontsize=11)
-                ax.legend(fontsize=9,labelcolor=fg,facecolor=pb)
         def psc(ax):
-            if vc and sc:
-                s2 = ax.scatter(df[vc],df[sc],c=x,cmap="RdYlGn_r",s=90,edgecolors=fg,linewidth=0.5,zorder=5)
-                cb=plt.colorbar(s2,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,label="Vel risk")
-                ax.axhline(y=10,color="#e63946",linestyle="--",linewidth=2,label="Shear risk")
-                ax.set_xlabel("Velocity (m/s)",color=ac,fontsize=11)
-                ax.set_ylabel("Shear Stress (Pa)",color=ac,fontsize=11)
-                ax.legend(fontsize=9,labelcolor=fg,facecolor=pb)
         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 (>2.0 m/s)")
-                if "shear" in col and mx>10: risk.append("HIGH SHEAR (>10 Pa)")
-            st+="━"*20+chr(10)
-            if risk:
-                st+="RISK FLAGS:"+chr(10)+"".join(["  ⚠ "+r+chr(10) for r in risk])
-                st+="OVERALL: HIGH RISK"; bc="#e63946"
-            else:
-                st+="OVERALL: LOW RISK"; bc="#2ecc71"
-            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(pv,"Velocity Profile"); i2=mk(ps,"Wall Shear Stress")
-        i3=mk(psc,"Velocity vs Shear"); i4=mk(psum,"Clinical Summary")
-        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. Analyze PIV stats give clinical interpretation."},
-                    {"role":"user","content":"PIV data from 27mm SJM Regent MHV 70bpm 5L/min:"+chr(10)+df.describe().to_string()[:600]}],max_tokens=300)
-                ai=chr(10)+"━"*20+chr(10)+"AI:"+chr(10)+resp.choices[0].message.content
             except: pass
-        return i1,i2,i3,i4,"PIV LOADED: "+str(len(df))+" rows | "+", ".join(df.columns.tolist())+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 a TGT CSV file 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="#ffffff" 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 or "hgb" 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)
-        xv=df[tc] if tc else range(len(df))
-        def mk(dc,color,yl,lim,ll,title,bar=False):
-            fig2,ax=plt.subplots(figsize=(8,5))
-            fig2.patch.set_facecolor(bg); ax.set_facecolor(pb)
-            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=0.2,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,fontsize=11); ax.set_xlabel(tc or "Sample",color=ac,fontsize=11)
-                mv=round(float(np.max(yp)),2); st="HIGH" if mv>lim else "NORMAL"
-                ax.set_title(title+chr(10)+"Max: "+str(mv)+"  Status: "+st,color=fg,fontweight="bold",fontsize=12)
-            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()
-            buf2=io.BytesIO(); plt.savefig(buf2,format="png",facecolor=bg,bbox_inches="tight",dpi=130); buf2.seek(0)
-            res=Image.open(buf2).copy(); plt.close(); return res
-        i1=mk(tatc,"#e63946","TAT (ng/mL)",8,"Normal: 8 ng/mL","Thrombin-Antithrombin TAT")
-        i2=mk(pfc,"#4361ee","PF1.2 (nmol/L)",2.0,"Normal: 2.0","Prothrombin Fragment PF1.2")
-        i3=mk(hc,"#2ecc71","Free Hemoglobin (mg/L)",20,"Normal: 20 mg/L","Free Hemoglobin Hemolysis",bar=True)
-        i4=mk(plc,"#e67e22","Platelet Count (10³/μL)",150,"Normal min: 150","Platelet Count")
-        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 SJSU CardioLab. Analyze TGT data give thrombogenicity risk LOW MODERATE or HIGH. Normal: TAT<8, PF1.2<2.0, Hemo<20, Plt>150."},
-                    {"role":"user","content":"TGT from 27mm SJM Regent MHV:"+chr(10)+df.describe().to_string()[:600]}],max_tokens=300)
-                ai=chr(10)+"━"*20+chr(10)+"AI:"+chr(10)+resp.choices[0].message.content
             except: pass
-        return i1,i2,i3,i4,"TGT LOADED: "+str(len(df))+" rows | "+", ".join(df.columns.tolist())+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 for CardioLab: "+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. Try again.",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)+" - "+vr+chr(10)+"Shear: "+str(s)+" - "+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)+"Time:"+str(tm)+"min"+chr(10)+"RESULT: "+("HIGH RISK" if risk>=3 else "MODERATE" if risk>=2 else "LOW RISK")
-# ── UI ─────────────────────────────────────────────────────────────
-with gr.Blocks(title="CardioLab AI", css=CSS) as demo:
-    gr.HTML('''<div style="background:linear-gradient(135deg,#1a237e,#b71c1c);padding:20px;text-align:center;border-radius:12px 12px 0 0"><div style="font-size:2.5em;font-weight:900;color:#fff;letter-spacing:3px">CardioLab AI</div></div>''')
     with gr.Tabs():
         with gr.Tab("Chat"):
-            gr.Markdown("### Chat with memory — saves conversations like ChatGPT")
             with gr.Row():
-                with gr.Column(scale=3):
-                    chatbot = gr.Chatbot(label="", height=420)
                     with gr.Row():
-                        msg_box = gr.Textbox(placeholder="Ask about CardioLab research...", label="", lines=2, scale=4)
                         with gr.Column(scale=1, min_width=80):
                             send_btn = gr.Button("Send", variant="primary")
                             clear_btn = gr.Button("Clear", variant="secondary")
-                with gr.Column(scale=1, min_width=200):
-                    gr.Markdown("### Saved Sessions")
-                    session_dropdown = gr.Dropdown(
-                        choices=get_session_list(),
-                        label="Load a saved session",
-                        interactive=True
-                    )
-                    load_btn = gr.Button("Load Session", variant="primary")
-                    session_status = gr.Textbox(label="Status", lines=1, interactive=False)
-                    gr.Markdown("### Save Current Chat")
-                    session_name_box = gr.Textbox(label="Session name", placeholder="e.g. TGT Research May 2026")
-                    save_btn = gr.Button("Save Chat", variant="primary")
-                    delete_btn = gr.Button("Delete Session", variant="secondary")
-            send_btn.click(research_chat, inputs=[msg_box, chatbot], outputs=[msg_box, chatbot])
-            msg_box.submit(research_chat, inputs=[msg_box, chatbot], outputs=[msg_box, chatbot])
             clear_btn.click(lambda: ([], ""), outputs=[chatbot, msg_box])
             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=320)
             audio_input = gr.Audio(sources=["microphone"], type="filepath", label="Record Question")
             with gr.Row():
                 voice_btn = gr.Button("Ask by Voice", variant="primary")
@@ -432,93 +607,131 @@ with gr.Blocks(title="CardioLab AI", css=CSS) as demo:
             voice_clear.click(lambda: [], outputs=voice_chatbot)
         with gr.Tab("Papers"):
             with gr.Row():
-                search_input = gr.Textbox(placeholder="e.g. mechanical heart valve thrombogenicity", label="Research Topic", scale=4)
                 search_btn = gr.Button("Search", variant="primary", scale=1)
-            search_output = gr.Textbox(label="Verified Results", lines=18)
             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"):
-            gr.Markdown("### Upload PIV CSV — 4 separate charts + AI analysis")
             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=5)
             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"):
-            gr.Markdown("### Upload TGT CSV — blood biomarker charts + thrombogenicity assessment")
             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=5)
             with gr.Row():
-                tgt_c1 = gr.Image(label="TAT Over Time", type="pil")
-                tgt_c2 = gr.Image(label="PF1.2 Over Time", type="pil")
             with gr.Row():
-                tgt_c3 = gr.Image(label="Free Hemoglobin", type="pil")
-                tgt_c4 = gr.Image(label="Platelet Count", 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 Photo"):
-            gr.Markdown("### Upload uPAD Photo — Instant CKD diagnosis")
             with gr.Row():
                 with gr.Column():
-                    photo_input = gr.Image(label="Upload uPAD Photo", type="numpy", height=280)
-                    analyze_btn = gr.Button("Analyze uPAD", variant="primary")
                 with gr.Column():
-                    photo_img = gr.Image(label="Detection Zone", type="pil", height=280)
-                    photo_text = gr.Textbox(label="CKD Result", lines=10)
             analyze_btn.click(analyze_upad_photo, inputs=photo_input, outputs=[photo_img, photo_text])
         with gr.Tab("AI Image"):
             with gr.Row():
-                img_prompt = gr.Textbox(placeholder="e.g. bileaflet heart valve | uPAD device | Arduino TGT", 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=380)
-            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 Stress 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("uPAD Manual"):
             with gr.Row():
-                with gr.Column():
-                    r=gr.Number(label="R value",value=210)
-                    g=gr.Number(label="G value",value=140)
-                    b=gr.Number(label="B value",value=80)
-                    out3=gr.Textbox(label="Result",lines=4)
-                    gr.Button("Analyze",variant="primary").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 Stage 2+"),
-                        inputs=[r,g,b],outputs=out3)
 demo.launch()

 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.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()