Upload hf_app_v7.py

hf_app_v7.py

@@ -0,0 +1,194 @@
+import gradio as gr
+import os, requests
+from groq import Groq
+
+GROQ_KEY = os.environ.get("GROQ_API_KEY","")
+client = Groq(api_key=GROQ_KEY)
+
+KNOWHOW = """
+SJSU CardioLab Know-How:
+MCL: Sylgard 184 PDMS 10:1 ratio 48hr cure, green laser PIV, 70bpm 5L/min flow
+TGT: Arduino Uno + Stepper Motor, 150mL blood, sample 0/20/40/60min, TAT PF1.2 hemolysis platelets
+uPAD: Jaffe reaction creatinine + picric acid = orange-red, normal 0.6-1.2 mg/dL, CKD above 1.5
+FSI: COMSOL ALE mesh, blood 1060 kg/m3, 0.0035 Pa.s, St Jude geometry
+MHV: 27mm SJM Regent, bileaflet trileaflet monoleaflet pediatric
+CKD Stages: 1 below 1.5, 2 1.5-3.0, 3-4 3.0-6.0, 5 above 6.0 mg/dL
+Equipment: Heska HT5, time-resolved PIV, Tygon tubing, Arduino
+13 Projects: MCL/PIV, TGT, FSI simulation, uPAD CKD diagnostics
+"""
+
+def search_pubmed(query, n=3):
+    try:
+        r = requests.get("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi",
+            params={"db":"pubmed","term":query,"retmax":n,"retmode":"json","sort":"date"}, timeout=10)
+        ids = r.json()["esearchresult"]["idlist"]
+        if not ids: return [], ""
+        r2 = requests.get("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi",
+            params={"db":"pubmed","id":",".join(ids),"retmode":"xml","rettype":"abstract"}, timeout=10)
+        import xmltodict
+        data = xmltodict.parse(r2.content)
+        articles = data.get("PubmedArticleSet",{}).get("PubmedArticle",[])
+        if isinstance(articles, dict): articles = [articles]
+        real_links = []
+        context = ""
+        for a in articles[:n]:
+            try:
+                c = a["MedlineCitation"]
+                title = str(c["Article"]["ArticleTitle"])
+                abstract = c["Article"].get("Abstract",{}).get("AbstractText","")
+                if isinstance(abstract, list): abstract = " ".join([str(x) for x in abstract])
+                if isinstance(abstract, dict): abstract = str(abstract.get("#text",""))
+                pmid = str(c["PMID"]["#text"] if isinstance(c["PMID"],dict) else c["PMID"])
+                real_url = "https://pubmed.ncbi.nlm.nih.gov/" + pmid
+                real_links.append("- " + title[:100] + "\n  " + real_url)
+                context += "[PubMed:" + pmid + "] " + title + ". " + str(abstract)[:300] + "\n"
+            except: continue
+        return real_links, context
+    except: return [], ""
+
+def search_scholar(query, n=3):
+    try:
+        r = requests.get("https://api.semanticscholar.org/graph/v1/paper/search",
+            params={"query":query,"limit":n,"fields":"title,abstract,year,url"}, timeout=10)
+        papers = r.json().get("data",[])
+        real_links = []
+        context = ""
+        for p in papers:
+            title = p.get("title","")
+            abstract = (p.get("abstract") or "")[:300]
+            year = str(p.get("year",""))
+            url = p.get("url","")
+            if url:
+                real_links.append("- " + title[:100] + " (" + year + ")\n  " + url)
+                context += "[Scholar " + year + "] " + title + ". " + abstract + "\n"
+        return real_links, context
+    except: return [], ""
+
+def ask_with_memory(message, history):
+    if not GROQ_KEY:
+        return "Error: GROQ_API_KEY not set in Space secrets."
+
+    # Build messages with full history for memory
+    messages = [
+        {
+            "role": "system",
+            "content": """You are CardioLab AI built on Biomni from Stanford SNAP Lab.
+Expert in SJSU Biomedical Engineering research.
+You remember everything said in this conversation.
+NEVER invent paper titles or URLs.
+ONLY cite papers from the search results provided.
+
+CARDIOLAB KNOW-HOW:
+""" + KNOWHOW
+        }
+    ]
+
+    # Add chat history — new Gradio format uses dicts
+    for msg in history:
+        if isinstance(msg, dict):
+            messages.append({"role": msg["role"], "content": msg["content"]})
+        else:
+            # fallback for tuple format
+            messages.append({"role": "user", "content": str(msg[0])})
+            messages.append({"role": "assistant", "content": str(msg[1])})
+
+    # Search papers
+    cardio_query = message + " mechanical heart valve OR microfluidic OR CKD creatinine OR PIV OR thrombogenicity"
+    pubmed_links, pubmed_context = search_pubmed(cardio_query, n=3)
+    scholar_links, scholar_context = search_scholar(message + " biomedical", n=3)
+    sources = pubmed_context + scholar_context
+
+    messages.append({
+        "role": "user",
+        "content": message + "\n\nReal papers (ONLY use these):\n" + sources[:3000]
+    })
+
+    response = client.chat.completions.create(
+        model="llama-3.3-70b-versatile",
+        messages=messages,
+        max_tokens=800
+    )
+
+    answer = response.choices[0].message.content
+
+    links = ""
+    if pubmed_links:
+        links += "\n\n📚 VERIFIED PUBMED LINKS:\n" + "\n".join(pubmed_links[:3])
+    if scholar_links:
+        links += "\n\n🎓 VERIFIED SCHOLAR LINKS:\n" + "\n".join(scholar_links[:3])
+
+    return answer + links
+
+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"
+    return "Velocity: "+str(velocity)+" m/s - "+v+"\nShear: "+str(shear)+" Pa - "+s+"\nHeart Rate: "+str(hr)+" bpm"
+
+def tgt_tool(tat, pf12, hemo, platelets, time):
+    risk = sum([float(tat)>15, float(pf12)>2.0, float(hemo)>50, float(platelets)<150])
+    overall = "HIGH THROMBOGENIC RISK" if risk>=3 else "MODERATE RISK" if risk>=2 else "LOW RISK"
+    return "TAT:"+str(tat)+" PF1.2:"+str(pf12)+" Hemo:"+str(hemo)+" Platelets:"+str(platelets)+"\nTime:"+str(time)+"min\nResult: "+overall
+
+def upad_tool(r, g, b):
+    creatinine = max(0, round(0.02*(float(r)-float(b))-0.5, 2))
+    stage = "Normal" if creatinine<1.2 else "Borderline" if creatinine<1.5 else "Stage 2 CKD" if creatinine<3.0 else "Stage 3-4 CKD" if creatinine<6.0 else "Stage 5 CKD"
+    return "Creatinine: "+str(creatinine)+" mg/dL\nStage: "+stage+"\nConfirm with: Heska Element HT5"
+
+with gr.Blocks(title="CardioLab AI - SJSU") as demo:
+    gr.Markdown("# CardioLab AI Agent")
+    gr.Markdown("### SJSU Biomedical Engineering | Biomni + Llama 70B + Chat Memory + PubMed")
+    gr.Markdown("GitHub: github.com/pranatechsol/Cardio-Lab-Ai")
+
+    with gr.Tab("Research Chat"):
+        gr.Markdown("### Chat with memory — remembers your full conversation like ChatGPT")
+        chatbot = gr.Chatbot(
+            label="CardioLab AI",
+            height=500,
+            type="messages"
+        )
+        msg = gr.Textbox(
+            label="Your message",
+            placeholder="Ask anything about CardioLab research...",
+            lines=2
+        )
+        with gr.Row():
+            send = gr.Button("Send", variant="primary")
+            clear = gr.Button("Clear Chat")
+
+        def respond(message, history):
+            bot_message = ask_with_memory(message, history)
+            history.append({"role": "user", "content": message})
+            history.append({"role": "assistant", "content": bot_message})
+            return "", history
+
+        send.click(respond, inputs=[msg, chatbot], outputs=[msg, chatbot])
+        msg.submit(respond, inputs=[msg, chatbot], outputs=[msg, chatbot])
+        clear.click(lambda: [], None, chatbot)
+
+    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 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=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)
+
+demo.launch()