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app.py

@@ -3,97 +3,167 @@ import os
 import requests
 from groq import Groq
 
-GROQ_KEY = os.environ.get("GROQ_API_KEY", "")
+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."""
+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.')
 
-def get_pubmed(query):
+CSS = (
+    'body { background: #0a0f1e !important; }'
+    '.gradio-container { background: #0a0f1e !important; color: white !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 ''
+        links = ['https://pubmed.ncbi.nlm.nih.gov/'+i for i in ids]
+        return 'PubMed: ' + ' | '.join(links)
+    except: return ''
+
+def get_scholar(query, n=5):
     try:
-        r = requests.get("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi",
-            params={"db":"pubmed","term":query,"retmax":3,"retmode":"json","sort":"date"},timeout=8)
-        ids = r.json()["esearchresult"]["idlist"]
-        if not ids: return ""
-        return "\n\nPubMed: "+" | ".join(["https://pubmed.ncbi.nlm.nih.gov/"+i for i in ids])
-    except: return ""
-
-def respond(message, history):
+        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 - '+url)
+        return 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
+    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
+        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)
-        msgs = [{"role":"system","content":"You are CardioLab AI from SJSU Biomedical Engineering built on Biomni Stanford. Expert in MHV MCL PIV TGT uPAD CKD FSI. Remember full conversation. Never invent URLs.\n\n"+KNOWHOW}]
+        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
+        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=600)
-        answer = resp.choices[0].message.content + get_pubmed(message)
-        history.append({"role":"user","content":message})
-        history.append({"role":"assistant","content":answer})
-        return "", history
+                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)+'VERIFIED PUBMED: '+pubmed
+        if scholar: answer += chr(10)+chr(10)+'SEMANTIC SCHOLAR: '+chr(10)+scholar
+        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
+        history.append({'role':'user','content':message})
+        history.append({'role':'assistant','content':'Error: '+str(e)})
+        return '', history
 
 def piv_tool(velocity, shear, hr):
-    v = "HIGH-stenosis" if float(velocity)>2.0 else "NORMAL"
-    s = "HIGH-thrombosis" if float(shear)>10 else "ELEVATED" if float(shear)>5 else "NORMAL"
-    return "Velocity:"+str(velocity)+"m/s - "+v+"\nShear:"+str(shear)+"Pa - "+s+"\nHR:"+str(hr)+"bpm"
+    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)+'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 RISK" if risk>=3 else "MODERATE" if risk>=2 else "LOW RISK"
-    return "TAT:"+str(tat)+" PF1.2:"+str(pf12)+"\nHemo:"+str(hemo)+" Plt:"+str(platelets)+"\nResult:"+r
+    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
 
 def upad_tool(r,g,b):
     c=max(0,round(0.02*(float(r)-float(b))-0.5,2))
-    s="Normal" if c<1.2 else "Borderline" if c<1.5 else "Stage2CKD" if c<3.0 else "Stage3-4" if c<6.0 else "Stage5"
-    return "Creatinine:"+str(c)+"mg/dL\nStage:"+s
-
-with gr.Blocks(title="CardioLab AI SJSU") as demo:
-    gr.Markdown("# CardioLab AI Agent")
-    gr.Markdown("### SJSU Biomedical Engineering | Biomni Stanford + Llama 70B + PubMed")
-    gr.Markdown("github.com/pranatechsol/Cardio-Lab-Ai")
-    with gr.Tab("Research Chat"):
-        gr.Markdown("### Chat like ChatGPT for CardioLab research")
-        chatbot = gr.Chatbot(height=400, label="CardioLab AI")
-        msg_box = gr.Textbox(placeholder="Ask anything about CardioLab...", label="Your message", lines=2)
-        with gr.Row():
-            send_btn = gr.Button("Send", variant="primary", scale=3)
-            clear_btn = gr.Button("Clear", scale=1)
-        send_btn.click(respond, inputs=[msg_box, chatbot], outputs=[msg_box, chatbot])
-        msg_box.submit(respond, inputs=[msg_box, chatbot], outputs=[msg_box, chatbot])
-        clear_btn.click(lambda: ([], ""), outputs=[chatbot, msg_box])
-    with gr.Tab("PIV Analysis"):
-        gr.Markdown("### Analyze PIV flow data from Mock Circulatory Loop")
-        v=gr.Number(label="Max Velocity m/s",value=1.8)
-        s=gr.Number(label="Shear Stress Pa",value=6.5)
-        h=gr.Number(label="Heart Rate bpm",value=72)
-        out=gr.Textbox(label="Result",lines=4)
-        gr.Button("Analyze PIV").click(piv_tool,inputs=[v,s,h],outputs=out)
-    with gr.Tab("TGT Results"):
-        gr.Markdown("### Interpret Thrombogenicity Tester blood results")
-        t1=gr.Number(label="TAT",value=18)
-        t2=gr.Number(label="PF1.2",value=2.5)
-        t3=gr.Number(label="Free Hemoglobin",value=60)
-        t4=gr.Number(label="Platelet Count",value=140)
-        t5=gr.Number(label="Time minutes",value=40)
-        out2=gr.Textbox(label="Result",lines=5)
-        gr.Button("Analyze TGT").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)
-        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 uPAD").click(upad_tool,inputs=[r,g,b],outputs=out3)
+    if c<1.2: s='Normal'
+    elif c<1.5: s='Borderline'
+    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'
+
+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.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.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('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_btn = gr.Button('Search Papers', variant='primary')
+            search_output = gr.Textbox(label='Research Papers Found', 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))
+
+        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')
+            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'):
+            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)
+            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()