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app.py

@@ -1,7 +1,6 @@
 import gradio as gr
-import os, requests, json
+import os, requests
 from groq import Groq
-from datetime import datetime
 
 GROQ_KEY = os.environ.get("GROQ_API_KEY","")
 client = Groq(api_key=GROQ_KEY)
@@ -67,9 +66,9 @@ def search_scholar(query, n=3):
 
 def ask_with_memory(message, history):
     if not GROQ_KEY:
-        return "Error: GROQ_API_KEY not set."
+        return "Error: GROQ_API_KEY not set in Space secrets."
 
-    # Build full conversation history for memory
+    # Build messages with full history for memory
     messages = [
         {
             "role": "system",
@@ -78,28 +77,30 @@ 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.
 
 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})
+    # 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 for relevant papers
+    # 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
 
-    # 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]
+        "content": message + "\n\nReal papers (ONLY use these):\n" + sources[:3000]
     })
 
     response = client.chat.completions.create(
@@ -110,7 +111,6 @@ CARDIOLAB KNOW-HOW:
 
     answer = response.choices[0].message.content
 
-    # Add verified links
     links = ""
     if pubmed_links:
         links += "\n\n📚 VERIFIED PUBMED LINKS:\n" + "\n".join(pubmed_links[:3])
@@ -144,21 +144,22 @@ with gr.Blocks(title="CardioLab AI - SJSU") as demo:
         chatbot = gr.Chatbot(
             label="CardioLab AI",
             height=500,
-            show_label=True
+            type="messages"
         )
         msg = gr.Textbox(
             label="Your message",
-            placeholder="Ask anything about CardioLab research... I remember our full conversation!",
+            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, chat_history):
-            bot_message = ask_with_memory(message, chat_history)
-            chat_history.append((message, bot_message))
-            return "", chat_history
+        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])