{
  "id": 10653230,
  "title": "Building a Customer Support UI with Streamlit",
  "url": "https://urgent.news/2026/09/29/building-a-customer-support-ui-with-streamlit",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-09-29T08:56:37.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/heena_meheraj_66/building-a-customer-support-ui-with-streamlit-2277"
  },
  "original_language": "en",
  "account": "Building a Customer Support User Interface with Streamlit\n\nIntroducing a Support Interface\nCustomer support systems must deliver clear, useful responses. When an AI system uses customer-specific memory, the interface should indicate which customer asked, what the AI generated, and what information was recalled. For the ResolveIQ Lite project, the author worked on the Streamlit UI layer to make this memory feature visible.\n\nDesigning the Streamlit Application\nThe main application resides in app.py and uses Streamlit to create the user interface. The page layout is kept simple with `st.set_page_config(page_title=\"ResolveIQ Lite\", layout=\"wide\")`. A wide layout allows the three sections to display side by side.\n\nCustomer Selection and Memory Toggle\nThe sidebar contains all the controls. Customers are selected from available data. The interface includes:\n- Customer selection\n- Memory toggle (Hindsight memory ON/OFF)\n- Start new session button to clear the current screen/session history while keeping the stored memory\n- Customer message input\n- Send button\n- Current query display\n- Generated response display\n- Recalled memory display\n- Session history\n\nConnecting UI with Backend\nInitially, stand-in functions allowed testing the interface before completing backend components. Later, real functions from memory.py and agent.py replaced the temporary functions. The final application imports:\n- from memory import retain_incident, recall_incidents\n- from agent import generate_reply\n\nThe Streamlit UI communicates with the memory and AI layers. The flow is:\nCustomer ↓ Streamlit UI ↓ Recall Customer Memory ↓ Generate AI Response ↓ Display Response + Memory ↓ Store Interaction\n\nDemonstrating Memory Impact\nTo show the difference between memory-enabled and memory-disabled responses, the author used two scenarios:\n1. Memory ON\n- Customer query: \"I still haven't received my replacement.\"\n- Recalled memory: \"Customer's order A104 arrived damaged and they requested a replacement.\"\n- Generated response incorporates the recalled information.\n2. Memory OFF\n- Same query is sent again.\n- UI displays: \"Memory is OFF for this message\"\n- Response becomes more generic as it doesn't receive the previous customer-specific memory.\n\nTesting and Integration\nThe application was tested using fictional customer data. Initial integration issues included:\n- Hindsight configuration errors due to empty environment variables\n- Signature mismatch between the temporary retain_incident() function and the actual function in memory.py\n\nAfter resolving these, the UI effectively demonstrated how memory affects AI responses.",
  "summary": "Introduction Customer support systems need to provide responses that are not only useful but also easy to understand. When an AI system uses customer-specific memory, the interface should make it clear what the customer asked, what the AI generated, and what information was recalled. For our ResolveIQ Lite project, I worked on the UI layer using Streamlit. My main responsibility was to build an…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}