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app.py

@@ -7,7 +7,8 @@ MODEL_ID = "Splashdude/smollm-chatbot"
 SYSTEM_PROMPT = (
     "You are a helpful, friendly AI assistant. "
     "You give clear, accurate, and conversational answers. "
-    "Keep responses concise unless the user asks for detail."
+    "Keep responses concise unless the user asks for detail. "
+    "Remember what the user tells you and refer back to it when asked."
 )
 
 model = None
@@ -26,14 +27,33 @@ def load_model():
     print("Model loaded!")
 
 
+def extract_text(content):
+    """Extract plain text from Gradio content (handles str, list, dict)."""
+    if isinstance(content, str):
+        return content
+    if isinstance(content, list):
+        parts = []
+        for item in content:
+            if isinstance(item, str):
+                parts.append(item)
+            elif isinstance(item, dict):
+                parts.append(item.get("text", item.get("content", str(item))))
+            else:
+                parts.append(str(item))
+        return " ".join(parts)
+    if isinstance(content, dict):
+        return content.get("text", content.get("content", str(content)))
+    return str(content)
+
+
 def build_messages(history, new_message):
     """Convert Gradio history to chat messages list."""
     messages = [{"role": "system", "content": SYSTEM_PROMPT}]
     for msg in history:
-        if msg["role"] == "user":
-            messages.append({"role": "user", "content": msg["content"]})
-        elif msg["role"] == "assistant":
-            messages.append({"role": "assistant", "content": msg["content"]})
+        role = msg.get("role", "")
+        content = extract_text(msg.get("content", ""))
+        if role in ("user", "assistant") and content:
+            messages.append({"role": role, "content": content})
     messages.append({"role": "user", "content": new_message})
     return messages
 
@@ -50,15 +70,14 @@ def generate_response(message, history):
         yield "Please type a message."
         return
 
-    messages = build_messages(history, message)
-
     try:
+        messages = build_messages(history, message)
         text = tokenizer.apply_chat_template(
             messages, tokenize=False, add_generation_prompt=True
         )
         inputs = tokenizer(text, return_tensors="pt")
     except Exception as e:
-        yield f"Error: {e}"
+        yield f"Error building prompt: {e}"
         return
 
     streamer = TextIteratorStreamer(