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CardioLab-AI

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Saicharan21 commited on 26 days ago

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1992158

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@@ -3,218 +3,425 @@ import os
 import requests
 from groq import Groq
-GROQ_KEY = os.environ.get('GROQ_API_KEY', '')
-KNOWHOW = ('SJSU CardioLab: '
-    '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: #0a0f1e !important; }'
-    '.tab-nav button { color: #a8b2d8 !important; background: transparent !important; font-weight: 600 !important; padding: 10px 20px !important; border: none !important; border-bottom: 3px solid transparent !important; }'
-    '.tab-nav button.selected { color: #e63946 !important; border-bottom: 3px solid #e63946 !important; }'
-    '.tab-nav button:hover { color: #ffffff !important; background: #1a2744 !important; }'
-    'h1 { color: #e63946 !important; font-size: 2.8em !important; font-weight: 900 !important; text-align: center !important; padding: 25px 0 !important; }'
-    'h3, label, p, span, div { color: #ffffff !important; }'
-    '.message { color: #ffffff !important; font-size: 1em !important; }'
-    '.bot { background: #1a2744 !important; color: #ffffff !important; }'
-    '.user { color: #ffffff !important; }'
-    'strong, b { color: #ffd700 !important; }'
-    'textarea, input[type=number], input[type=text] { background: #1a2744 !important; color: #e2e8f0 !important; border: 1px solid #4361ee !important; border-radius: 8px !important; }'
-    'button.primary { background: linear-gradient(135deg, #e63946 0%, #c1121f 100%) !important; color: white !important; border: none !important; border-radius: 8px !important; }'
-    'button.secondary { background: #1d3461 !important; color: #a8b2d8 !important; border: 1px solid #4361ee !important; border-radius: 8px !important; }'
-    '.block { background: #0d1b3e !important; border: 1px solid #1e3a6e !important; border-radius: 12px !important; }'
-)
 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 get_scholar(query, n=5):
     try:
-        r = requests.get('https://api.semanticscholar.org/graph/v1/paper/search',
-            params={'query':query+' biomedical','limit':n,'fields':'title,year,url,citationCount'},timeout=10)
-        papers = r.json().get('data',[])
         out = []
         for p in papers:
-            title = p.get('title','')
-            year = str(p.get('year',''))
-            url = p.get('url','')
-            citations = str(p.get('citationCount',0))
             if url:
-                out.append(title[:80]+' ('+year+') - '+citations+' citations'+chr(10)+'   '+url)
         return chr(10)+chr(10).join(out)
-    except: return ''
 def quick_search(query):
-    if not query.strip(): return 'Please enter a research topic.'
     pubmed = get_pubmed(query, n=8)
     scholar = get_scholar(query, n=5)
-    result = 'PAPERS FOR: ' + query + chr(10)+chr(10)
-    result += 'PUBMED (verified links):'+chr(10)+pubmed+chr(10)+chr(10)
-    result += 'SEMANTIC SCHOLAR:'+chr(10)+scholar
     return result
 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 tab.'})
-        return '', history
     try:
         client = Groq(api_key=GROQ_KEY)
         pubmed = get_pubmed(message, n=3)
-        system_msg = 'You are CardioLab AI assistant. Expert in MHV MCL PIV TGT uPAD CKD FSI. Remember full conversation. Never invent URLs. ' + KNOWHOW
-        msgs = [{'role':'system','content':system_msg}]
         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
-        if pubmed: answer += chr(10)+chr(10)+'PUBMED LINKS:'+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:
             transcription = client.audio.transcriptions.create(
-                file=('audio.wav', f, 'audio/wav'),
-                model='whisper-large-v3'
             )
         text = transcription.text
-        system_msg = 'You are CardioLab AI assistant. Expert in MHV MCL PIV TGT uPAD CKD FSI. ' + KNOWHOW
-        msgs = [{'role':'system','content':system_msg}]
         for item in history:
             if isinstance(item, dict):
-                msgs.append({'role':item['role'],'content':item['content']})
-        msgs.append({'role':'user','content':text})
-        resp = client.chat.completions.create(model='llama-3.3-70b-versatile',messages=msgs,max_tokens=500)
         answer = resp.choices[0].message.content
-        history.append({'role':'user','content':'[Voice]: '+text})
-        history.append({'role':'assistant','content':answer})
         return history
     except Exception as e:
-        history.append({'role':'assistant','content':'Voice error: '+str(e)})
         return history
 def generate_diagram(topic):
-    if not topic.strip(): return 'Please enter a topic.'
     try:
         client = Groq(api_key=GROQ_KEY)
-        msgs = [{'role':'system','content':'You are a biomedical engineering diagram expert for SJSU CardioLab. Generate a detailed ASCII diagram or technical schematic based on the topic. Make it clear, labeled, and useful for researchers.'}]
-        msgs.append({'role':'user','content':'Create a detailed labeled diagram for: '+topic})
-        resp = client.chat.completions.create(model='llama-3.3-70b-versatile',messages=msgs,max_tokens=600)
         return resp.choices[0].message.content
     except Exception as e:
-        return 'Error: '+str(e)
 def piv_tool(velocity, shear, hr):
-    v = 'HIGH - stenosis risk' if float(velocity)>2.0 else 'NORMAL'
-    s = 'HIGH - thrombosis risk' if float(shear)>10 else 'ELEVATED' if float(shear)>5 else 'NORMAL'
-    hr_s = 'ABNORMAL' if float(hr)<60 or float(hr)>100 else 'NORMAL'
-    return 'VELOCITY: '+str(velocity)+' m/s - '+v+chr(10)+'SHEAR: '+str(shear)+' Pa - '+s+chr(10)+'HEART RATE: '+str(hr)+' bpm - '+hr_s
 def tgt_tool(tat,pf12,hemo,platelets,time):
     risk=sum([float(tat)>15,float(pf12)>2.0,float(hemo)>50,float(platelets)<150])
-    r='HIGH THROMBOGENIC RISK' if risk>=3 else 'MODERATE RISK' if risk>=2 else 'LOW RISK'
-    return ('TIME: '+str(time)+' min'+chr(10)+
-        'TAT: '+str(tat)+(' - HIGH' if float(tat)>15 else ' - NORMAL')+chr(10)+
-        'PF1.2: '+str(pf12)+(' - HIGH' if float(pf12)>2.0 else ' - NORMAL')+chr(10)+
-        'HEMOGLOBIN: '+str(hemo)+(' - HIGH' if float(hemo)>50 else ' - NORMAL')+chr(10)+
-        'PLATELETS: '+str(platelets)+(' - LOW' if float(platelets)<150 else ' - NORMAL')+chr(10)+chr(10)+
-        'OVERALL: '+r)
 def upad_tool(r,g,b):
     c=max(0,round(0.02*(float(r)-float(b))-0.5,2))
-    if c<1.2: s='Normal - No CKD'
-    elif c<1.5: s='Borderline - Monitor'
-    elif c<3.0: s='Stage 2 CKD'
-    elif c<6.0: s='Stage 3-4 CKD'
-    else: s='Stage 5 CKD - Kidney Failure'
-    return 'RGB: R='+str(r)+' G='+str(g)+' B='+str(b)+chr(10)+'CREATININE: '+str(c)+' mg/dL'+chr(10)+'CKD STAGE: '+s+chr(10)+'Confirm with: Heska Element HT5'
-with gr.Blocks(title='CardioLab AI', css=CSS) as demo:
-    gr.Markdown('# CardioLab AI')
     with gr.Tabs():
-        with gr.Tab('Chat'):
-            chatbot = gr.Chatbot(label='', height=450)
             with gr.Row():
-                msg_box = gr.Textbox(placeholder='Ask anything about CardioLab research...', label='', lines=2, scale=4)
-                with gr.Column(scale=1):
-                    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])
-        with gr.Tab('Voice'):
-            gr.Markdown('### Speak your question - powered by Groq Whisper')
-            voice_chatbot = gr.Chatbot(label='Voice Chat', height=350)
-            audio_input = gr.Audio(sources=['microphone'], type='filepath', label='Record your 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('Paper Search'):
-            search_input = gr.Textbox(placeholder='e.g. mechanical heart valve thrombogenicity', label='Research topic')
-            search_btn = gr.Button('Search Papers', variant='primary')
-            search_output = gr.Textbox(label='Results', lines=20)
             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('Diagram Generator'):
-            gr.Markdown('### Generate technical diagrams and schematics for CardioLab')
-            gr.Markdown('Examples: MCL setup | TGT circuit | uPAD fabrication flow | PIV system | valve design')
-            diagram_input = gr.Textbox(placeholder='e.g. Mock Circulatory Loop setup with PIV system', label='What to diagram')
-            diagram_btn = gr.Button('Generate Diagram', variant='primary')
-            diagram_output = gr.Textbox(label='Diagram', lines=20)
             diagram_btn.click(generate_diagram, inputs=diagram_input, outputs=diagram_output)
-        with gr.Tab('PIV Analysis'):
             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=5)
-                    gr.Button('Analyze PIV', variant='primary').click(piv_tool,inputs=[v,s,h],outputs=piv_out)
-        with gr.Tab('TGT Results'):
-            t1=gr.Number(label='TAT ng/mL', value=18)
-            t2=gr.Number(label='PF1.2 nmol/L', value=2.5)
-            t3=gr.Number(label='Free Hemoglobin mg/L', value=60)
-            t4=gr.Number(label='Platelet Count', value=140)
-            t5=gr.Number(label='Time minutes', value=40)
-            out2=gr.Textbox(label='Result', lines=8)
-            gr.Button('Analyze TGT', variant='primary').click(tgt_tool,inputs=[t1,t2,t3,t4,t5],outputs=out2)
-        with gr.Tab('uPAD CKD'):
-            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=6)
-            gr.Button('Analyze uPAD', variant='primary').click(upad_tool,inputs=[r,g,b],outputs=out3)
 demo.launch()

 import requests
 from groq import Groq
+GROQ_KEY = os.environ.get("GROQ_API_KEY", "")
+KNOWHOW = ("SJSU CardioLab: "
+    "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 = """
+@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700;900&display=swap');
+* { font-family: 'Inter', sans-serif !important; box-sizing: border-box; }
+body, .gradio-container {
+    background: #050d1a !important;
+    color: #ffffff !important;
+}
+.gradio-container {
+    max-width: 1200px !important;
+    margin: 0 auto !important;
+    padding: 0 !important;
+}
+/* HEADER */
+.main-header {
+    background: linear-gradient(135deg, #0d1b3e 0%, #1a0533 50%, #0d1b3e 100%);
+    border-bottom: 3px solid #e63946;
+    padding: 30px 20px;
+    text-align: center;
+    margin-bottom: 0;
+}
+/* TABS - ALWAYS VISIBLE */
+.tab-nav {
+    background: #0d1b3e !important;
+    border-bottom: 2px solid #1e3a6e !important;
+    padding: 0 10px !important;
+    display: flex !important;
+    gap: 5px !important;
+    overflow-x: auto !important;
+}
+.tab-nav button {
+    background: #132340 !important;
+    color: #a0b3d6 !important;
+    border: 1px solid #1e3a6e !important;
+    border-radius: 8px 8px 0 0 !important;
+    padding: 12px 20px !important;
+    font-size: 0.9em !important;
+    font-weight: 600 !important;
+    cursor: pointer !important;
+    transition: all 0.2s !important;
+    white-space: nowrap !important;
+    margin-top: 8px !important;
+}
+.tab-nav button:hover {
+    background: #1e3a6e !important;
+    color: #ffffff !important;
+    border-color: #4361ee !important;
+}
+.tab-nav button.selected {
+    background: linear-gradient(135deg, #e63946, #c1121f) !important;
+    color: #ffffff !important;
+    border-color: #e63946 !important;
+    font-weight: 700 !important;
+}
+/* CONTENT AREA */
+.tabitem {
+    background: #0a1628 !important;
+    padding: 20px !important;
+    border: 1px solid #1e3a6e !important;
+    border-top: none !important;
+}
+/* INPUTS */
+textarea, input[type=number], input[type=text] {
+    background: #132340 !important;
+    color: #ffffff !important;
+    border: 1px solid #2d4a8a !important;
+    border-radius: 8px !important;
+    font-size: 0.95em !important;
+}
+textarea:focus, input:focus {
+    border-color: #e63946 !important;
+    outline: none !important;
+}
+textarea::placeholder {
+    color: #5a7aaa !important;
+}
+/* BUTTONS */
+button.primary, .btn-primary {
+    background: linear-gradient(135deg, #e63946 0%, #c1121f 100%) !important;
+    color: white !important;
+    border: none !important;
+    border-radius: 8px !important;
+    font-weight: 700 !important;
+    padding: 12px 24px !important;
+    font-size: 0.95em !important;
+    cursor: pointer !important;
+    transition: transform 0.1s !important;
+}
+button.primary:hover {
+    transform: translateY(-1px) !important;
+    box-shadow: 0 4px 15px rgba(230, 57, 70, 0.4) !important;
+}
+button.secondary {
+    background: #1d3461 !important;
+    color: #a0b3d6 !important;
+    border: 1px solid #2d4a8a !important;
+    border-radius: 8px !important;
+    font-weight: 600 !important;
+}
+/* LABELS */
+label span, label, .label-wrap span {
+    color: #7eb8f7 !important;
+    font-weight: 600 !important;
+    font-size: 0.85em !important;
+    text-transform: uppercase !important;
+    letter-spacing: 0.5px !important;
+}
+/* MARKDOWN TEXT */
+.md h1, .md h2, .md h3 { color: #ffffff !important; }
+.md p, .md li { color: #a0b3d6 !important; }
+/* CHATBOT */
+.chatbot {
+    background: #0a1628 !important;
+    border: 1px solid #1e3a6e !important;
+    border-radius: 12px !important;
+}
+.message.user {
+    background: linear-gradient(135deg, #e63946, #c1121f) !important;
+    color: #ffffff !important;
+    border-radius: 12px 12px 4px 12px !important;
+}
+.message.bot {
+    background: #132340 !important;
+    color: #e2e8f0 !important;
+    border: 1px solid #2d4a8a !important;
+    border-radius: 12px 12px 12px 4px !important;
+}
+/* RESULT BOXES */
+.gr-box, .block {
+    background: #0d1b3e !important;
+    border: 1px solid #1e3a6e !important;
+    border-radius: 12px !important;
+}
+/* NUMBERS */
+input[type=number] {
+    color: #7eb8f7 !important;
+    font-size: 1.1em !important;
+    font-weight: 600 !important;
+}
+/* INFO TEXT */
+.info {
+    color: #5a7aaa !important;
+    font-size: 0.8em !important;
+}
+/* SCROLLBAR */
+::-webkit-scrollbar { width: 6px; }
+::-webkit-scrollbar-track { background: #0a1628; }
+::-webkit-scrollbar-thumb { background: #2d4a8a; border-radius: 3px; }
+"""
 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 get_scholar(query, n=5):
     try:
+        r = requests.get("https://api.semanticscholar.org/graph/v1/paper/search",
+            params={"query":query+" biomedical","limit":n,"fields":"title,year,url,citationCount"},timeout=10)
+        papers = r.json().get("data",[])
         out = []
         for p in papers:
+            title = p.get("title","")
+            year = str(p.get("year",""))
+            url = p.get("url","")
+            citations = str(p.get("citationCount",0))
             if url:
+                out.append(title[:80]+" ("+year+") - "+citations+" citations"+chr(10)+"   "+url)
         return chr(10)+chr(10).join(out)
+    except: return ""
 def quick_search(query):
+    if not query.strip(): return "Please enter a research topic."
     pubmed = get_pubmed(query, n=8)
     scholar = get_scholar(query, n=5)
+    result = "PAPERS FOR: " + query + chr(10)+chr(10)
+    result += "PUBMED (verified links):"+chr(10)+pubmed+chr(10)+chr(10)
+    result += "SEMANTIC SCHOLAR:"+chr(10)+scholar
     return result
 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 tab."})
+        return "", history
     try:
         client = Groq(api_key=GROQ_KEY)
         pubmed = get_pubmed(message, n=3)
+        system_msg = "You are CardioLab AI assistant. Expert in MHV MCL PIV TGT uPAD CKD FSI. Remember full conversation. Never invent URLs. " + KNOWHOW
+        msgs = [{"role":"system","content":system_msg}]
         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
+        if pubmed: answer += chr(10)+chr(10)+"PUBMED LINKS:"+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:
             transcription = client.audio.transcriptions.create(
+                file=("audio.wav", f, "audio/wav"),
+                model="whisper-large-v3"
             )
         text = transcription.text
+        msgs = [{"role":"system","content":"You are CardioLab AI assistant. Expert in MHV MCL PIV TGT uPAD CKD FSI. "+KNOWHOW}]
         for item in history:
             if isinstance(item, dict):
+                msgs.append({"role":item["role"],"content":item["content"]})
+        msgs.append({"role":"user","content":text})
+        resp = client.chat.completions.create(model="llama-3.3-70b-versatile",messages=msgs,max_tokens=500)
         answer = resp.choices[0].message.content
+        history.append({"role":"user","content":"[Voice] "+text})
+        history.append({"role":"assistant","content":answer})
         return history
     except Exception as e:
+        history.append({"role":"assistant","content":"Voice error: "+str(e)})
         return history
 def generate_diagram(topic):
+    if not topic.strip(): return "Please enter a topic."
     try:
         client = Groq(api_key=GROQ_KEY)
+        msgs = [{"role":"system","content":"You are a biomedical engineering expert for SJSU CardioLab. Generate a detailed ASCII diagram or technical schematic. Make it clear, labeled, and useful for researchers."}]
+        msgs.append({"role":"user","content":"Create a detailed labeled diagram for: "+topic})
+        resp = client.chat.completions.create(model="llama-3.3-70b-versatile",messages=msgs,max_tokens=600)
         return resp.choices[0].message.content
     except Exception as e:
+        return "Error: "+str(e)
 def piv_tool(velocity, shear, hr):
+    v = "HIGH - stenosis risk" if float(velocity)>2.0 else "NORMAL"
+    s = "HIGH - thrombosis risk" if float(shear)>10 else "ELEVATED - monitor" if float(shear)>5 else "NORMAL"
+    hr_s = "ABNORMAL" if float(hr)<60 or float(hr)>100 else "NORMAL"
+    return ("PIV ANALYSIS RESULTS"+chr(10)+
+        "━━━━━━━━━━━━━━━━━━━━"+chr(10)+
+        "Velocity:   "+str(velocity)+" m/s  →  "+v+chr(10)+
+        "Shear:      "+str(shear)+" Pa   →  "+s+chr(10)+
+        "Heart Rate: "+str(hr)+" bpm →  "+hr_s)
 def tgt_tool(tat,pf12,hemo,platelets,time):
     risk=sum([float(tat)>15,float(pf12)>2.0,float(hemo)>50,float(platelets)<150])
+    r="HIGH THROMBOGENIC RISK" if risk>=3 else "MODERATE RISK" if risk>=2 else "LOW RISK"
+    return ("TGT BLOOD ANALYSIS"+chr(10)+
+        "━━━━━━━━━━━━━━━━━━━━"+chr(10)+
+        "Time:       "+str(time)+" min"+chr(10)+
+        "TAT:        "+str(tat)+"  →  "+("HIGH" if float(tat)>15 else "NORMAL")+chr(10)+
+        "PF1.2:      "+str(pf12)+"  →  "+("HIGH" if float(pf12)>2.0 else "NORMAL")+chr(10)+
+        "Hemoglobin: "+str(hemo)+"  →  "+("HIGH" if float(hemo)>50 else "NORMAL")+chr(10)+
+        "Platelets:  "+str(platelets)+"  →  "+("LOW" if float(platelets)<150 else "NORMAL")+chr(10)+
+        "━━━━━━━━━━━━━━━━━━━━"+chr(10)+
+        "OVERALL: "+r)
 def upad_tool(r,g,b):
     c=max(0,round(0.02*(float(r)-float(b))-0.5,2))
+    if c<1.2: s="Normal - No CKD"
+    elif c<1.5: s="Borderline - Monitor"
+    elif c<3.0: s="Stage 2 CKD"
+    elif c<6.0: s="Stage 3-4 CKD"
+    else: s="Stage 5 CKD - Kidney Failure"
+    return ("uPAD CKD ANALYSIS"+chr(10)+
+        "━━━━━━━━━━━━━━━━━━━━"+chr(10)+
+        "RGB Input:   R="+str(r)+" G="+str(g)+" B="+str(b)+chr(10)+
+        "Orange Score: "+str(int(float(r)-float(b)))+chr(10)+
+        "Creatinine:  "+str(c)+" mg/dL"+chr(10)+
+        "━━━━━━━━━━━━━━━━━━━━"+chr(10)+
+        "CKD STAGE: "+s+chr(10)+
+        "Confirm with: Heska Element HT5")
+with gr.Blocks(title="CardioLab AI", css=CSS) as demo:
+    gr.HTML("""
+    <div class="main-header">
+        <div style="font-size:2.8em; font-weight:900; color:#e63946; letter-spacing:3px; text-shadow: 0 0 30px rgba(230,57,70,0.5);">
+            ❤️ CardioLab AI
+        </div>
+    </div>
+    """)
     with gr.Tabs():
+        with gr.Tab("💬 Chat"):
+            chatbot = gr.Chatbot(label="", height=450)
             with gr.Row():
+                msg_box = gr.Textbox(
+                    placeholder="Ask anything about CardioLab research...",
+                    label="",
+                    lines=2,
+                    scale=4
+                )
+                with gr.Column(scale=1, min_width=100):
+                    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])
+        with gr.Tab("🎙️ Voice"):
+            gr.Markdown("### Speak your research question — powered by Groq Whisper AI")
+            voice_chatbot = gr.Chatbot(label="", height=350)
+            audio_input = gr.Audio(sources=["microphone"], type="filepath", label="Record Question")
             with gr.Row():
+                voice_btn = gr.Button("Ask by Voice ➤", variant="primary")
+                voice_clear = gr.Button("Clear", variant="secondary")
             voice_btn.click(voice_chat, inputs=[audio_input, voice_chatbot], outputs=voice_chatbot)
             voice_clear.click(lambda: [], outputs=voice_chatbot)
+        with gr.Tab("🔍 Papers"):
+            gr.Markdown("### Search latest research papers with verified working links")
+            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="Results — Verified Links Only", lines=18)
             search_btn.click(quick_search, inputs=search_input, outputs=search_output)
             search_input.submit(quick_search, inputs=search_input, outputs=search_output)
+            gr.Markdown("**Try:** `mechanical heart valve PIV` | `creatinine uPAD microfluidic` | `bileaflet MHV thrombogenicity`")
+        with gr.Tab("🎨 Diagrams"):
+            gr.Markdown("### Generate technical diagrams and schematics for CardioLab")
+            with gr.Row():
+                diagram_input = gr.Textbox(
+                    placeholder="e.g. Mock Circulatory Loop with PIV system",
+                    label="What to diagram",
+                    scale=4
+                )
+                diagram_btn = gr.Button("Generate ➤", variant="primary", scale=1)
+            diagram_output = gr.Textbox(label="Technical Diagram", lines=18)
             diagram_btn.click(generate_diagram, inputs=diagram_input, outputs=diagram_output)
+            gr.Markdown("**Try:** `TGT circuit diagram` | `uPAD fabrication steps` | `PIV optical setup` | `MCL flow loop`")
+        with gr.Tab("📊 PIV"):
+            gr.Markdown("### Analyze Particle Image Velocimetry data from Mock Circulatory Loop")
             with gr.Row():
                 with gr.Column():
+                    v=gr.Number(label="Max Velocity (m/s)", value=1.8, info="Normal range: 0.5 - 2.0 m/s")
+                    s=gr.Number(label="Wall Shear Stress (Pa)", value=6.5, info="Normal: below 5 Pa")
+                    h=gr.Number(label="Heart Rate (bpm)", value=72, info="Normal: 60 - 100 bpm")
+                    piv_btn = gr.Button("Analyze PIV Data ➤", variant="primary")
+                with gr.Column():
+                    piv_out=gr.Textbox(label="Analysis Result", lines=8)
+            piv_btn.click(piv_tool, inputs=[v,s,h], outputs=piv_out)
+        with gr.Tab("🩸 TGT"):
+            gr.Markdown("### Interpret Thrombogenicity Tester blood analysis results")
+            with gr.Row():
+                with gr.Column():
+                    t1=gr.Number(label="TAT (ng/mL)", value=18, info="Normal: below 8")
+                    t2=gr.Number(label="PF1.2 (nmol/L)", value=2.5, info="Normal: below 2.0")
+                    t3=gr.Number(label="Free Hemoglobin (mg/L)", value=60, info="Normal: below 20")
+                    t4=gr.Number(label="Platelet Count (10³/μL)", value=140, info="Normal: above 150")
+                    t5=gr.Number(label="Time (minutes)", value=40)
+                    tgt_btn = gr.Button("Analyze TGT Results ➤", variant="primary")
+                with gr.Column():
+                    out2=gr.Textbox(label="Analysis Result", lines=12)
+            tgt_btn.click(tgt_tool, inputs=[t1,t2,t3,t4,t5], outputs=out2)
+        with gr.Tab("🧪 uPAD"):
+            gr.Markdown("### Analyze uPAD colorimetric result using Jaffe Reaction for CKD diagnosis")
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("**Enter RGB values from 64x64 pixel detection zone**")
+                    r=gr.Number(label="R (Red)", value=210, info="Range: 0 - 255")
+                    g=gr.Number(label="G (Green)", value=140, info="Range: 0 - 255")
+                    b=gr.Number(label="B (Blue)", value=80, info="Range: 0 - 255")
+                    upad_btn = gr.Button("Analyze uPAD ➤", variant="primary")
+                with gr.Column():
+                    out3=gr.Textbox(label="CKD Analysis Result", lines=10)
+            upad_btn.click(upad_tool, inputs=[r,g,b], outputs=out3)
 demo.launch()