Spaces:

Saicharan21
/

CardioLab-AI

Sleeping

App Files Files Community

Saicharan21 commited on Apr 21

Commit

55c23a2

verified ·

1 Parent(s): c1a2885

Upload app.py with huggingface_hub

Browse files

Files changed (1) hide show

app.py +406 -246

app.py CHANGED Viewed

@@ -9,7 +9,6 @@ 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", "")
@@ -22,100 +21,260 @@ KNOWHOW = ("MCL: Sylgard 184 PDMS 10:1 ratio 48hr cure green laser PIV 70bpm 5L/
     "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",
@@ -139,18 +298,18 @@ def quick_search(query):
 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
@@ -172,14 +331,13 @@ def voice_chat(audio, 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:
@@ -195,73 +353,62 @@ def analyze_upad_photo(image):
         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=[]
@@ -269,85 +416,73 @@ def analyze_piv_csv(file, theme="White"):
             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):
@@ -360,7 +495,7 @@ def generate_image(prompt):
                 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()
@@ -377,75 +512,105 @@ def generate_image(prompt):
     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")
-                voice_clear = gr.Button("Clear", variant="secondary")
             voice_btn.click(voice_chat, inputs=[audio_input, voice_chatbot], outputs=voice_chatbot)
             voice_clear.click(lambda: [], outputs=voice_chatbot)
-        with gr.Tab("Papers"):
             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")
@@ -454,71 +619,66 @@ with gr.Blocks(title="CardioLab AI", css=CSS) as demo:
                 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", "")
     "Equipment: Heska HT5 hematology analyzer time-resolved PIV Tygon tubing Arduino Uno.")
 CSS = """
+/* Reset and base */
+body, .gradio-container {
+    background: #f7f7f8 !important;
+    font-family: -apple-system, BlinkMacSystemFont, Segoe UI, sans-serif !important;
+    margin: 0 !important;
+    padding: 0 !important;
+}
+/* Hide default gradio header */
+.gradio-container > .main > .wrap > .panel {
+    border: none !important;
+    box-shadow: none !important;
+}
+/* CHATGPT STYLE SIDEBAR */
+.sidebar {
+    background: #202123 !important;
+    min-height: 100vh !important;
+    padding: 10px !important;
+    border-right: 1px solid #3a3a3a !important;
+}
+.sidebar-title {
+    color: white !important;
+    font-size: 1.1em !important;
+    font-weight: 700 !important;
+    padding: 10px 5px !important;
+    border-bottom: 1px solid #3a3a3a !important;
+    margin-bottom: 10px !important;
+}
+.session-item {
+    background: #2d2d30 !important;
+    color: #ececf1 !important;
+    border-radius: 6px !important;
+    padding: 8px 12px !important;
+    margin-bottom: 4px !important;
+    cursor: pointer !important;
+    font-size: 0.85em !important;
+}
+.session-item:hover {
+    background: #3a3a3c !important;
+}
+/* TABS - TOP NAV STYLE */
+.tab-nav {
+    background: #ffffff !important;
+    border-bottom: 1px solid #e5e7eb !important;
+    padding: 0 16px !important;
+    display: flex !important;
+    flex-wrap: nowrap !important;
+    overflow-x: auto !important;
+    gap: 0 !important;
+}
+.tab-nav button {
+    background: transparent !important;
+    color: #6b7280 !important;
+    border: none !important;
+    border-bottom: 2px solid transparent !important;
+    padding: 12px 16px !important;
+    font-weight: 500 !important;
+    font-size: 0.85em !important;
+    white-space: nowrap !important;
+    border-radius: 0 !important;
+    margin: 0 !important;
+    transition: all 0.15s !important;
+}
+.tab-nav button:hover {
+    color: #111827 !important;
+    background: #f9fafb !important;
+}
+.tab-nav button.selected {
+    color: #e63946 !important;
+    border-bottom: 2px solid #e63946 !important;
+    font-weight: 600 !important;
+    background: transparent !important;
+}
+/* MAIN CHAT AREA */
+.chat-container {
+    background: #ffffff !important;
+    min-height: 80vh !important;
+}
+/* CHATBOT MESSAGES */
+.chatbot {
+    background: #ffffff !important;
+    border: none !important;
+    border-radius: 0 !important;
+}
+.message.user {
+    background: #f7f7f8 !important;
+    color: #1a202c !important;
+    border-radius: 12px !important;
+    padding: 12px 16px !important;
+    margin: 4px 0 !important;
+}
+.message.bot {
+    background: #ffffff !important;
+    color: #1a202c !important;
+    border-radius: 12px !important;
+    padding: 12px 16px !important;
+    margin: 4px 0 !important;
+    border-left: 3px solid #e63946 !important;
+}
+/* INPUT AREA */
+textarea {
+    background: #ffffff !important;
+    color: #1a202c !important;
+    border: 1px solid #d1d5db !important;
+    border-radius: 12px !important;
+    font-size: 0.95em !important;
+    padding: 12px !important;
+    box-shadow: 0 1px 3px rgba(0,0,0,0.1) !important;
+}
+textarea:focus {
+    border-color: #e63946 !important;
+    box-shadow: 0 0 0 2px rgba(230,57,70,0.1) !important;
+    outline: none !important;
+}
+/* BUTTONS */
+button.primary {
+    background: #e63946 !important;
+    color: white !important;
+    border: none !important;
+    border-radius: 8px !important;
+    font-weight: 600 !important;
+    padding: 10px 20px !important;
+    transition: background 0.15s !important;
+}
+button.primary:hover {
+    background: #c1121f !important;
+}
+button.secondary {
+    background: #f3f4f6 !important;
+    color: #374151 !important;
+    border: 1px solid #d1d5db !important;
+    border-radius: 8px !important;
+    font-weight: 500 !important;
+}
+/* NEW CHAT BUTTON */
+.new-chat-btn {
+    background: transparent !important;
+    color: #ececf1 !important;
+    border: 1px solid #3a3a3c !important;
+    border-radius: 6px !important;
+    padding: 8px 12px !important;
+    width: 100% !important;
+    text-align: left !important;
+    margin-bottom: 8px !important;
+    font-size: 0.85em !important;
+}
+/* DROPDOWN */
+select, .gr-dropdown {
+    background: #2d2d30 !important;
+    color: #ececf1 !important;
+    border: 1px solid #3a3a3c !important;
+    border-radius: 6px !important;
+}
+/* INPUT NUMBERS */
+input[type=number] {
+    background: #f9fafb !important;
+    color: #1a202c !important;
+    border: 1px solid #d1d5db !important;
+    border-radius: 8px !important;
+}
+/* LABELS */
+label span {
+    color: #374151 !important;
+    font-weight: 500 !important;
+    font-size: 0.85em !important;
+}
+/* FILE UPLOAD */
+.file-preview {
+    background: #f9fafb !important;
+    border: 2px dashed #d1d5db !important;
+    border-radius: 12px !important;
+}
+/* SCROLLBAR */
+::-webkit-scrollbar { width: 6px; height: 6px; }
+::-webkit-scrollbar-track { background: transparent; }
+::-webkit-scrollbar-thumb { background: #d1d5db; border-radius: 3px; }
+::-webkit-scrollbar-thumb:hover { background: #9ca3af; }
 """
 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, "r") as f: return json.load(f)
+    except: return {}
 def save_all_sessions(sessions):
     if not HF_TOKEN: return False
     try:
+        api = HfApi(token=HF_TOKEN)
+        api.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 chat history")
         return True
+    except: return False
 def get_session_list():
     sessions = load_all_sessions()
+    if not sessions: return ["No saved sessions"]
+    return list(reversed(list(sessions.keys())))
+def save_session(history, session_name):
+    if not history: return "Nothing to save", gr.update()
+    if not session_name or not session_name.strip():
+        session_name = "Chat " + datetime.now().strftime("%b %d %H:%M")
+    sessions = load_all_sessions()
+    sessions[session_name] = {"messages": history, "saved_at": datetime.now().isoformat()}
+    ok = save_all_sessions(sessions)
+    choices = get_session_list()
+    if ok: return "Saved: "+session_name, gr.update(choices=choices, value=session_name)
+    return "Save failed — check HF_TOKEN", gr.update()
 def load_session(session_name):
+    if not session_name or "No saved" in session_name: return [], "Select a session first"
     sessions = load_all_sessions()
     if session_name in sessions:
+        msgs = sessions[session_name]["messages"]
+        return msgs, "Loaded: "+session_name
     return [], "Session not found"
 def delete_session(session_name):
+    if not session_name or "No saved" in session_name: return "Select a session first", gr.update()
     sessions = load_all_sessions()
     if session_name in sessions:
         del sessions[session_name]
         save_all_sessions(sessions)
+        choices = get_session_list()
+        return "Deleted: "+session_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(query, n=5):
     try:
         r = requests.get("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi",
 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."})
         return "", history
     try:
         client = Groq(api_key=GROQ_KEY)
+        msgs = [{"role":"system","content":"You are CardioLab AI assistant for SJSU Biomedical Engineering. 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
             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":"🎙️ "+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 analyze_upad_photo(image):
     if image is None: return None, "Upload a uPAD photo first."
     try:
         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."
+        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)+"━"*22+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 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="#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)
+        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 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=0.15,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=0.15,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 (Pa)",color=ac,fontsize=11)
                 ax.legend(fontsize=9,labelcolor=fg,facecolor=pb)
         def psum(ax):
             ax.axis("off"); risk=[]
             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="#e63946" 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. Analyze stats give clinical interpretation."},
+                    {"role":"user","content":"PIV from 27mm SJM Regent MHV 70bpm:"+chr(10)+df.describe().to_string()[:500]}],max_tokens=250)
+                ai=chr(10)+"━"*20+chr(10)+"AI: "+resp.choices[0].message.content
             except: pass
+        return i1,i2,i3,i4,"PIV: "+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="#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,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)
+            return mk_chart(fn,title,bg,fg,gc,ac,pb)
+        i1=mk2(tatc,"#e63946","TAT (ng/mL)",8,"Normal: 8","TAT Thrombin-Antithrombin")
+        i2=mk2(pfc,"#4361ee","PF1.2 (nmol/L)",2.0,"Normal: 2.0","PF1.2 Prothrombin Fragment")
+        i3=mk2(hc,"#2ecc71","Free Hemoglobin (mg/L)",20,"Normal: 20","Free Hemoglobin",bar=True)
+        i4=mk2(plc,"#e67e22","Platelet Count",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. 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:"+chr(10)+df.describe().to_string()[:500]}],max_tokens=250)
+                ai=chr(10)+"━"*20+chr(10)+"AI: "+resp.choices[0].message.content
             except: pass
+        return i1,i2,i3,i4,"TGT: "+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):
                 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()
     except Exception as e: return None,"Error: "+str(e),""
 def piv_manual(v,s,h):
+    vr="HIGH — stenosis risk" if float(v)>2.0 else "NORMAL"
+    sr="HIGH — thrombosis risk" 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)+"Time:"+str(tm)+" min"+chr(10)+"RESULT: "+("HIGH RISK" if risk>=3 else "MODERATE" if risk>=2 else "LOW RISK")
 with gr.Blocks(title="CardioLab AI", css=CSS) as demo:
+    gr.HTML("""
+    <div style="background:linear-gradient(135deg,#1a237e 0%,#b71c1c 100%);padding:16px 24px;display:flex;align-items:center;gap:16px;">
+        <div style="font-size:1.8em;font-weight:900;color:#fff;letter-spacing:2px;">❤️ CardioLab AI</div>
+        <div style="color:rgba(255,255,255,0.7);font-size:0.85em;">SJSU Biomedical Engineering</div>
+    </div>
+    """)
     with gr.Tabs():
+        with gr.Tab("💬 Chat"):
             with gr.Row():
+                # LEFT SIDEBAR - ChatGPT style
+                with gr.Column(scale=1, min_width=220):
+                    gr.HTML('<div style="background:#202123;padding:12px;border-radius:8px;margin-bottom:8px;"><div style="color:white;font-weight:700;font-size:0.9em;margin-bottom:8px;">💬 Conversations</div></div>')
+                    new_chat_btn = gr.Button("✏️ New Chat", variant="secondary")
+                    gr.HTML('<div style="color:#9ca3af;font-size:0.75em;padding:8px 0 4px 0;">SAVED SESSIONS</div>')
                     session_dropdown = gr.Dropdown(
                         choices=get_session_list(),
+                        label="",
+                        interactive=True,
+                        container=False
                     )
+                    load_btn = gr.Button("📂 Load", variant="primary")
+                    session_name_box = gr.Textbox(
+                        placeholder="Session name...",
+                        label="",
+                        lines=1,
+                        container=False
+                    )
+                    with gr.Row():
+                        save_btn = gr.Button("💾 Save", variant="primary", scale=1)
+                        delete_btn = gr.Button("🗑️", variant="secondary", scale=0)
+                    session_status = gr.Textbox(label="", lines=1, interactive=False, container=False)
+                # RIGHT - Main chat area
+                with gr.Column(scale=4):
+                    chatbot = gr.Chatbot(
+                        label="",
+                        height=520,
+                        show_label=False,
+                        container=False
+                    )
+                    with gr.Row():
+                        msg_box = gr.Textbox(
+                            placeholder="Message CardioLab AI...",
+                            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], 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])
+            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"):
             with gr.Row():
+                with gr.Column():
+                    voice_chatbot = gr.Chatbot(label="", height=400, show_label=False)
+                    audio_input = gr.Audio(sources=["microphone"], type="filepath", label="Record Question")
+                    with gr.Row():
+                        voice_btn = gr.Button("Ask by Voice", variant="primary")
+                        voice_clear = gr.Button("Clear", variant="secondary")
             voice_btn.click(voice_chat, inputs=[audio_input, voice_chatbot], outputs=voice_chatbot)
             voice_clear.click(lambda: [], outputs=voice_chatbot)
+        with gr.Tab("🔍 Papers"):
             with gr.Row():
+                search_input = gr.Textbox(placeholder="e.g. mechanical heart valve thrombogenicity 2024", 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 clinical 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=4)
             with gr.Row():
                 piv_c1 = gr.Image(label="Velocity Profile", type="pil")
                 piv_c2 = gr.Image(label="Shear Stress", 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=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=280)
+                    analyze_btn = gr.Button("Analyze uPAD Photo", variant="primary")
                 with gr.Column():
+                    photo_img = gr.Image(label="Detection Zone (green box)", 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])
+            gr.Markdown("**Manual RGB entry:**")
+            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 the image", lines=2, scale=4)
                 with gr.Column(scale=1):
+                    img_btn = gr.Button("Generate Image", 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=420)
             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,info="Normal: 0.5-2.0")
+                    s=gr.Number(label="Wall Shear Stress Pa",value=6.5,info="Normal: <5 Pa")
+                    h=gr.Number(label="Heart Rate bpm",value=72,info="Normal: 60-100")
                     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,info="Normal: <8")
+                    t2=gr.Number(label="PF1.2 nmol/L",value=2.5,info="Normal: <2.0")
+                    t3=gr.Number(label="Free Hemoglobin mg/L",value=60,info="Normal: <20")
+                    t4=gr.Number(label="Platelet Count",value=140,info="Normal: >150")
+                    t5=gr.Number(label="Time minutes",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)
 demo.launch()