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app.py

@@ -13,8 +13,13 @@ KNOWHOW = ('SJSU CardioLab: '
     'Equipment: Heska HT5 hematology analyzer time-resolved PIV Tygon tubing Arduino Uno.')
 
 CSS = (
+    '.gradio-container { background: #0a0f1e !important; color: white !important; max-width: 100% !important; }'
     'body { background: #0a0f1e !important; }'
-    '.gradio-container { background: #0a0f1e !important; color: white !important; }'
+    '.gr-button-primary { background: linear-gradient(135deg, #e63946, #c1121f) !important; border: none !important; color: white !important; border-radius: 8px !important; }'
+    '.gr-button-secondary { background: #1d3461 !important; border: 1px solid #4361ee !important; color: white !important; border-radius: 8px !important; }'
+    'textarea, input { background: #1a2744 !important; color: white !important; border: 1px solid #2d4a8a !important; border-radius: 8px !important; }'
+    '.gr-box { background: #0d1b3e !important; border: 1px solid #2d4a8a !important; border-radius: 12px !important; }'
+    'h1 { color: #e63946 !important; font-size: 2.5em !important; font-weight: 900 !important; text-align: center !important; padding: 20px !important; }'
 )
 
 def get_pubmed(query, n=5):
@@ -24,8 +29,7 @@ def get_pubmed(query, n=5):
             'retmax':n,'retmode':'json','sort':'date'},timeout=10)
         ids = r.json()['esearchresult']['idlist']
         if not ids: return ''
-        links = ['https://pubmed.ncbi.nlm.nih.gov/'+i for i in ids]
-        return 'PubMed: ' + ' | '.join(links)
+        return chr(10).join(['https://pubmed.ncbi.nlm.nih.gov/'+i for i in ids])
     except: return ''
 
 def get_scholar(query, n=5):
@@ -40,17 +44,17 @@ def get_scholar(query, n=5):
             url = p.get('url','')
             citations = str(p.get('citationCount',0))
             if url:
-                out.append(title[:80]+' ('+year+') '+citations+' citations - '+url)
-        return chr(10).join(out)
+                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 = 'RESEARCH PAPERS FOR: ' + query + chr(10) + chr(10)
-    result += 'PUBMED (verified links):' + chr(10) + pubmed + chr(10) + chr(10)
-    result += 'SEMANTIC SCHOLAR:' + chr(10) + scholar
+    result = 'PAPERS FOR: ' + query + chr(10)+chr(10)
+    result += 'PUBMED (click links to open):'+chr(10)+pubmed+chr(10)+chr(10)
+    result += 'SEMANTIC SCHOLAR:'+chr(10)+scholar
     return result
 
 def research_chat(message, history):
@@ -61,8 +65,7 @@ def research_chat(message, history):
     try:
         client = Groq(api_key=GROQ_KEY)
         pubmed = get_pubmed(message, n=3)
-        scholar = get_scholar(message, n=3)
-        system_msg = 'You are CardioLab AI from SJSU Biomedical Engineering built on Biomni Stanford SNAP Lab. Expert in MHV MCL PIV TGT uPAD CKD FSI. Remember full conversation. Never invent URLs. ' + KNOWHOW
+        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):
@@ -70,8 +73,7 @@ def research_chat(message, history):
         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)+'VERIFIED PUBMED: '+pubmed
-        if scholar: answer += chr(10)+chr(10)+'SEMANTIC SCHOLAR: '+chr(10)+scholar
+        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
@@ -82,37 +84,30 @@ def research_chat(message, history):
 
 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'
+    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 'PIV ANALYSIS RESULTS'+chr(10)+'Velocity: '+str(velocity)+' m/s - '+v+chr(10)+'Shear: '+str(shear)+' Pa - '+s+chr(10)+'Heart Rate: '+str(hr)+' bpm - '+hr_s
+    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'
-    t_s='HIGH' if float(tat)>15 else 'NORMAL'
-    p_s='HIGH' if float(pf12)>2.0 else 'NORMAL'
-    h_s='HIGH' if float(hemo)>50 else 'NORMAL'
-    pl_s='LOW' if float(platelets)<150 else 'NORMAL'
-    return 'TGT BLOOD ANALYSIS'+chr(10)+'Time: '+str(time)+' min'+chr(10)+'TAT: '+str(tat)+' - '+t_s+chr(10)+'PF1.2: '+str(pf12)+' - '+p_s+chr(10)+'Hemoglobin: '+str(hemo)+' - '+h_s+chr(10)+'Platelets: '+str(platelets)+' - '+pl_s+chr(10)+'Overall: '+r
+    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'
-    elif c<1.5: s='Borderline'
+    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'
-    return 'uPAD CKD ANALYSIS'+chr(10)+'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'
+    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 SJSU', css=CSS) as demo:
-    gr.Markdown('# CardioLab AI Agent')
-    gr.Markdown('### SJSU Biomedical Engineering | Biomni Stanford + Llama 70B + PubMed Live Search')
-    gr.Markdown('github.com/pranatechsol/Cardio-Lab-Ai | huggingface.co/Saicharan21')
+with gr.Blocks(title='CardioLab AI', css=CSS) as demo:
+    gr.Markdown('# CardioLab AI')
 
     with gr.Tabs():
-        with gr.Tab('Research Chat'):
-            gr.Markdown('### Chat with memory like ChatGPT - searches PubMed for every question')
-            chatbot = gr.Chatbot(label='CardioLab AI', height=500)
+        with gr.Tab('Chat'):
+            chatbot = gr.Chatbot(label='', height=500)
             with gr.Row():
                 msg_box = gr.Textbox(placeholder='Ask anything about CardioLab research...', label='', lines=2, scale=4)
                 with gr.Column(scale=1):
@@ -123,47 +118,35 @@ with gr.Blocks(title='CardioLab AI SJSU', css=CSS) as demo:
             clear_btn.click(lambda: ([], ''), outputs=[chatbot, msg_box])
 
         with gr.Tab('Paper Search'):
-            gr.Markdown('### Search latest research papers with verified working links')
-            gr.Markdown('Searches PubMed + Semantic Scholar - only real verified URLs sorted by most recent')
-            search_input = gr.Textbox(placeholder='e.g. mechanical heart valve thrombogenicity 2024', label='Research topic')
+            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='Research Papers Found', lines=20)
+            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)
-            gr.Markdown('Suggested: mechanical heart valve PIV | creatinine uPAD microfluidic | bileaflet MHV thrombogenicity | CKD point-of-care')
 
         with gr.Tab('PIV Analysis'):
-            gr.Markdown('### Analyze PIV flow data from Mock Circulatory Loop')
             with gr.Row():
                 with gr.Column():
-                    v=gr.Number(label='Max Velocity m/s', value=1.8, info='Normal: 0.5-2.0 m/s')
-                    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 bpm')
-                    gr.Button('Analyze PIV', variant='primary').click(piv_tool,inputs=[v,s,h],outputs=gr.Textbox(label='Result',lines=6))
+                    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'):
-            gr.Markdown('### Interpret Thrombogenicity Tester blood results')
-            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')
+            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)
+            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'):
-            gr.Markdown('### Analyze uPAD colorimetric result - Jaffe Reaction')
-            r=gr.Number(label='R value', value=210, info='Range 0-255')
-            g=gr.Number(label='G value', value=140, info='Range 0-255')
-            b=gr.Number(label='B value', value=80, info='Range 0-255')
-            out3=gr.Textbox(label='CKD Result',lines=6)
+            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)
 
-        with gr.Tab('About'):
-            gr.Markdown('## CardioLab AI Agent - SJSU Biomedical Engineering')
-            gr.Markdown('Built on Biomni Stanford SNAP Lab | Apache 2.0')
-            gr.Markdown('Live: https://huggingface.co/spaces/Saicharan21/CardioLab-AI')
-            gr.Markdown('Code: https://github.com/pranatechsol/Cardio-Lab-Ai')
-            gr.Markdown('Brain: Llama 3.3 70B via Groq free | Search: PubMed + Semantic Scholar free')
-
 demo.launch()