Update app.py

app.py

@@ -24,170 +24,65 @@ def search_pubmed(query, n=3):
         )
         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"])
-                url = "https://pubmed.ncbi.nlm.nih.gov/" + pmid
-                real_links.append("- " + title[:100] + "\n  " + url)
-                context += "[PubMed:" + pmid + "] " + title + ". " + str(abstract)[:300] + "\n"
-            except:
-                continue
-        return real_links, context
+            return ""
+        links = []
+        for pmid in ids[:n]:
+            links.append("https://pubmed.ncbi.nlm.nih.gov/" + pmid)
+        return "PubMed results: " + " | ".join(links)
     except:
-        return [], ""
+        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"},
+            params={"query":query,"limit":n,"fields":"title,year,url"},
             timeout=10
         )
         papers = r.json().get("data",[])
-        real_links = []
-        context = ""
+        out = []
         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
+                out.append(title[:80] + " (" + year + ") - " + url)
+        return "\n".join(out)
     except:
-        return [], ""
+        return ""
 
 def ask_with_memory(message, history):
     if not GROQ_KEY:
         return "Error: GROQ_API_KEY not set in Space secrets."
-
     try:
         from groq import Groq
         client = Groq(api_key=GROQ_KEY)
-    except Exception as e:
-        return "Error loading Groq: " + str(e)
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are CardioLab AI built on Biomni from Stanford SNAP Lab. Expert in SJSU Biomedical Engineering. NEVER invent paper titles or URLs. Only cite papers from search results.\n\nCARDIOLAB KNOW-HOW:\n" + KNOWHOW
-        }
-    ]
-
-    for msg in history:
-        if isinstance(msg, dict):
-            messages.append({"role": msg["role"], "content": msg["content"]})
-
-    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 found (ONLY use these):\n" + sources[:3000]
-    })
-
-    try:
+        messages = [
+            {
+                "role": "system",
+                "content": "You are CardioLab AI built on Biomni from Stanford. Expert in SJSU Biomedical Engineering. Remember the full conversation. NEVER invent paper URLs.\n\n" + KNOWHOW
+            }
+        ]
+        for msg in history:
+            if isinstance(msg, dict):
+                messages.append({"role": msg["role"], "content": msg["content"]})
+        pubmed = search_pubmed(message, n=3)
+        scholar = search_scholar(message + " biomedical", n=3)
+        messages.append({
+            "role": "user",
+            "content": message + "\n\nVerified paper links found:\n" + pubmed + "\n" + scholar
+        })
         response = client.chat.completions.create(
             model="llama-3.3-70b-versatile",
             messages=messages,
-            max_tokens=800
+            max_tokens=600
         )
         answer = response.choices[0].message.content
+        links = ""
+        if pubmed:
+            links += "\n\n📚 VERIFIED PUBMED: " + pubmed
+        if scholar:
+            links += "\n\n🎓 VERIFIED SCHOLAR:\n" + scholar
+        return answer + links
     except Exception as e:
-        return "Error from Groq: " + str(e)
-
-    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"
-
-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
-
-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 — like ChatGPT but for CardioLab research")
-        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")
-        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()
+        return