gradio 4.3

README.md

@@ -4,7 +4,7 @@ emoji: 🐠
 colorFrom: green
 colorTo: red
 sdk: gradio
-sdk_version: 3.49.0
+sdk_version: 4.3.0
 app_file: app.py
 pinned: false
 ---

app.py

@@ -2,6 +2,7 @@ import itertools
 import gradio as gr
 import requests
 import os
+from gradio.themes.utils import sizes
 
 
 def respond(message, history):
@@ -9,10 +10,11 @@ def respond(message, history):
     if len(message.strip()) == 0:
         return "ERROR the question should not be empty"
 
+    local_token = os.getenv('API_TOKEN')
+    local_endpoint = os.getenv('API_ENDPOINT')
 
-    local_token = os.environ['API_TOKEN']
-    local_endpoint = os.environ['API_ENDPOINT']
-
+    if local_token is None or local_endpoint is None:
+        return "ERROR missing env variables"
 
     # Add your API token to the headers
     headers = {
@@ -24,30 +26,38 @@ def respond(message, history):
     prompt.append(message)
     q = {"inputs": [prompt]}
     try:
-        response = requests.post(local_endpoint, json=q, headers=headers, timeout=100)
+        response = requests.post(
+            local_endpoint, json=q, headers=headers, timeout=100)
         response_data = response.json(
         )["predictions"]
-    except:
-        response_data = "ERROR status_code:" + \
-            str(response.status_code) + " response:" + response.text
+    except Exception as error:
+        response_data = f"ERROR status_code: {type(error).__name__}"
+        # + str(response.status_code) + " response:" + response.text
 
-    #print(response.json())
+    # print(response.json())
     return response_data
 
 
+theme = gr.themes.Soft(
+    text_size=sizes.text_sm,radius_size=sizes.radius_sm, spacing_size=sizes.spacing_sm,
+)
+
+
 demo = gr.ChatInterface(
     respond,
-    chatbot=gr.Chatbot(height=400),
+    chatbot=gr.Chatbot(show_label=False, container=False, show_copy_button=True, bubble_full_width=True),
     textbox=gr.Textbox(placeholder="Ask me a question",
                        container=False, scale=7),
     title="Databricks LLM RAG demo - Chat with llama2 Databricks model serving endpoint",
     description="This chatbot is a demo example for the dbdemos llm chatbot. <br>This content is provided as a LLM RAG educational example, without support. It is using llama2, can hallucinate and should not be used as production content.<br>Please review our dbdemos license and terms for more details.",
-    examples=[["How can I start a Databricks cluster?"], ["What is a Databricks Cluster Policy?"]],
+    examples=[["How can I start a Databricks cluster?"],
+              ["What is a Databricks Cluster Policy?"],
+              ["How can I track billing usage on my workspaces?"],],
     cache_examples=False,
-    theme="soft",
+    theme=theme,
     retry_btn=None,
     undo_btn=None,
-    clear_btn="Clear"
+    clear_btn="Clear",
 )
 
 if __name__ == "__main__":