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app.py

@@ -1,6 +1,7 @@
 import gradio as gr
-import os, requests
+import os, requests, json
 from groq import Groq
+from datetime import datetime
 
 GROQ_KEY = os.environ.get("GROQ_API_KEY","")
 client = Groq(api_key=GROQ_KEY)
@@ -14,9 +15,10 @@ 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=5):
+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)
@@ -39,13 +41,13 @@ def search_pubmed(query, n=5):
                 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  URL: " + real_url)
-                context += "[PubMed PMID:" + pmid + "] " + title + ". " + str(abstract)[:300] + "\n\n"
+                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=5):
+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)
@@ -58,50 +60,64 @@ def search_scholar(query, n=5):
             year = str(p.get("year",""))
             url = p.get("url","")
             if url:
-                real_links.append("- " + title[:100] + " (" + year + ")\n  URL: " + url)
-                context += "[Scholar " + year + "] " + title + ". " + abstract + "\n\n"
+                real_links.append("- " + title[:100] + " (" + year + ")\n  " + url)
+                context += "[Scholar " + year + "] " + title + ". " + abstract + "\n"
         return real_links, context
     except: return [], ""
 
-def ask_agent(question):
+def ask_with_memory(message, history):
     if not GROQ_KEY:
-        return "Error: GROQ_API_KEY not set in Space secrets."
+        return "Error: GROQ_API_KEY not set."
 
-    cardio_query = question + " mechanical heart valve OR microfluidic creatinine OR PIV hemodynamics OR thrombogenicity"
-
-    pubmed_links, pubmed_context = search_pubmed(cardio_query, n=5)
-    scholar_links, scholar_context = search_scholar(question + " biomedical", n=5)
+    # Build full conversation 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.
+Always be helpful, detailed and accurate.
 
-    all_context = pubmed_context + scholar_context
+CARDIOLAB KNOW-HOW:
+""" + KNOWHOW
+        }
+    ]
+
+    # Add full chat history so it remembers previous messages
+    for human_msg, ai_msg in history:
+        messages.append({"role": "user", "content": human_msg})
+        messages.append({"role": "assistant", "content": ai_msg})
+
+    # Search for relevant 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
+
+    # Add current question with search results
+    messages.append({
+        "role": "user",
+        "content": message + "\n\nReal papers found (ONLY use these, do not invent):\n" + sources[:3000]
+    })
 
     response = client.chat.completions.create(
         model="llama-3.3-70b-versatile",
-        messages=[
-            {"role":"system","content":"""You are CardioLab AI built on Biomni from Stanford SNAP Lab.
-Expert in SJSU Biomedical Engineering research.
-IMPORTANT RULES:
-1. NEVER invent or generate paper titles or URLs
-2. ONLY refer to papers provided in the context below
-3. Always say which source you are using
-4. If you do not know something say so clearly
-
-CARDIOLAB KNOW-HOW:
-""" + KNOWHOW},
-            {"role":"user","content":"Research question: " + question + "\n\nReal papers found (use ONLY these):\n" + all_context[:4000] + "\n\nAnswer the question using only the above sources."}
-        ],
-        max_tokens=600
+        messages=messages,
+        max_tokens=800
     )
 
     answer = response.choices[0].message.content
 
-    # Add REAL links section — only verified URLs
-    real_links_section = ""
+    # Add verified links
+    links = ""
     if pubmed_links:
-        real_links_section += "\n\n📚 VERIFIED PUBMED LINKS:\n" + "\n".join(pubmed_links[:5])
+        links += "\n\n📚 VERIFIED PUBMED LINKS:\n" + "\n".join(pubmed_links[:3])
     if scholar_links:
-        real_links_section += "\n\n🎓 VERIFIED SCHOLAR LINKS:\n" + "\n".join(scholar_links[:5])
+        links += "\n\n🎓 VERIFIED SCHOLAR LINKS:\n" + "\n".join(scholar_links[:3])
 
-    return answer + real_links_section
+    return answer + links
 
 def piv_tool(velocity, shear, hr):
     v = "HIGH - stenosis risk" if float(velocity)>2.0 else "NORMAL"
@@ -111,7 +127,7 @@ def piv_tool(velocity, shear, hr):
 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
+    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))
@@ -120,13 +136,34 @@ def upad_tool(r, g, b):
 
 with gr.Blocks(title="CardioLab AI - SJSU") as demo:
     gr.Markdown("# CardioLab AI Agent")
-    gr.Markdown("### SJSU Biomedical Engineering | Biomni + Llama 70B + PubMed + Semantic Scholar")
-    gr.Markdown("**All paper links are verified real URLs only** | GitHub: github.com/pranatechsol/Cardio-Lab-Ai")
-    with gr.Tab("Research Assistant"):
-        gr.Markdown("### Searches PubMed + Semantic Scholar — only real verified links")
-        q = gr.Textbox(label="Research question", placeholder="e.g. Methods for MHV thrombogenicity detection")
-        a = gr.Textbox(label="Answer with verified citations", lines=14)
-        gr.Button("Search & Answer").click(ask_agent, inputs=q, outputs=a)
+    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,
+            show_label=True
+        )
+        msg = gr.Textbox(
+            label="Your message",
+            placeholder="Ask anything about CardioLab research... I remember our full conversation!",
+            lines=2
+        )
+        with gr.Row():
+            send = gr.Button("Send", variant="primary")
+            clear = gr.Button("Clear Chat")
+
+        def respond(message, chat_history):
+            bot_message = ask_with_memory(message, chat_history)
+            chat_history.append((message, bot_message))
+            return "", chat_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)
@@ -134,6 +171,7 @@ with gr.Blocks(title="CardioLab AI - SJSU") as demo:
         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)
@@ -143,6 +181,7 @@ with gr.Blocks(title="CardioLab AI - SJSU") as demo:
         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)