Update app.py

app.py

@@ -5,28 +5,44 @@ from huggingface_hub import InferenceClient
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 # Define how the chatbot responds
-def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
-    messages = [{"role": "system", "content": system_message}]
+shop_floor_data = {
+    "Machine A": {"Status": "Running", "Efficiency": "92%", "Output": "200 units/day"},
+    "Machine B": {"Status": "Idle", "Efficiency": "N/A", "Output": "0 units/day"},
+    "Assembly Line 1": {"Status": "Running", "Output": "450 units/day"},
+    "Welding Robot": {"Status": "Under maintenance", "Next Check": "April 10, 2025"},
+}
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    message_lower = message.lower()
     
+    if "status" in message_lower or "machine" in message_lower or "output" in message_lower:
+        response = "📊 **Shop Floor Status**:\n\n"
+        for name, info in shop_floor_data.items():
+            response += f"**{name}**\n"
+            for key, value in info.items():
+                response += f"  - {key}: {value}\n"
+            response += "\n"
+        yield response
+        return
+
+    # Fallback to the LLM response
+    messages = [{"role": "system", "content": system_message}]
     for val in history:
         if val[0]:
             messages.append({"role": "user", "content": val[0]})
         if val[1]:
             messages.append({"role": "assistant", "content": val[1]})
-
     messages.append({"role": "user", "content": message})
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-        response += token
-        yield response
+
+    response
+
 
 # Build the UI using Gradio Blocks
 with gr.Blocks() as demo: