Update app.py

app.py

@@ -10,6 +10,32 @@ client = AsyncOpenAI(
 
 assistantID = "asst_pMk1lyBSaVZPulq44RvIJUNe"
 
+mytitle = "<h1 align=center>Wat hunn d'Lëtzebuerger an de leschte Jore kommentéiert ?</h1>"
+
+mydescription="""
+<h3 align='center'>Wat fir een Thema interesséiert Dech : 👍 👎 🤛 ☝️ </h3>
+<table width=100%>
+  <tr>
+    <th width=50% bgcolor="Moccasin">Stell deng Fro op englesch, fir einfach Froe versteet d'AI och Lëtzebuergesch !</th>
+    <th bgcolor="Khaki">Äntwert vum OpenAI Code-Interpreter Assistent :</th>
+  </tr>
+</table>
+"""
+
+myarticle ="""
+<h3>Hannergrënn :</h3>
+<p>Dës HuggingFace Space Demo gouf vum <a href="https://github.com/mbarnig">Marco Barnig</a> realiséiert. Als kënstlech Intelligenz gëtt, mëttels API, den <a href="https://platform.openai.com/docs/models">OpenAI Modell</a> gpt-4o-mini-2024-07-18 benotzt, deen als Kontext bis 128.000 Tokens ka benotzen, eng Äntwert op eng Fro vu maximal 16.384 Tokens ka ginn a bis zu 200.000 Tokens pro Minutt (TPM) ka beaarbechten. Fir dës Demo goufen nëmmen eng News-JSON-Datei mat enger Gréisst vun 30 MB benotzt. Et ass méiglech bis zu 20 Dateien op en OpenAI Code-Interpreter Assistent opzelueden. D'Äntwerte vun de Beispiller sinn am Cache gespäichert a ginn duerfir ouni Delai ugewise.</p>
+"""
+
+myinput = gr.Textbox(lines=3, label="Wat interesséiert Dech ?")
+
+myexamples = [
+  "Wat fir ee Kommentar krut déi meescht 👍 ?",
+  "Wat fir ee Kommentar krut déi meescht 👎 ?",
+  "Show me a random comment !",
+  "Please show a comment with 2 👍 and 2 👎 !"
+]
+
 class EventHandler(AsyncAssistantEventHandler):
     def __init__(self) -> None:
         super().__init__()
@@ -70,7 +96,8 @@ async def generate_response(user_input):
     async with client.beta.threads.runs.stream(
         thread_id=thread_id,
         assistant_id=assistant_id,
-        instructions="""You are a Code Interpreter to analyze JSON files with RTL comments. Here is the format of the files :  
+        instructions="""
+You are a Code Interpreter to analyze JSON files with RTL comments. Here is the format of the JSON files :  
 [
     {
         "context_id": "",
@@ -92,7 +119,9 @@ async def generate_response(user_input):
             },
     }
 ]
-You will search dates ("date_created" of a comment and "date"  of the related thumbs),  calculate the total number of up scores and down scores. You will answer questions about "context_id", "text" and "referers".""",
+You will search dates ("date_created" of a comment and "date" of the related thumbs), calculate the total number of "thumbs":[{"score": "up"},{"score": "down"}] and answer questions about "context_id", "text" and "referers". A 👍 means "thumbs":[{"score": "up"}], a 👎 means "thumbs":[{"score": "down"}]. Please indicate in all responses the number of thumbs. Please provide your answers in luxembourgish language.
+"""
+,
         event_handler=event_handler,
     ) as stream:
         # Yield incremental updates
@@ -120,12 +149,14 @@ async def gradio_chat_interface(user_input):
 # Set up Gradio interface with streaming
 interface = gr.Interface(
     fn=gradio_chat_interface,
-    inputs="text",
+    inputs=myinput,
     outputs="markdown",
-    title="OpenAI Interpreter with Gradio",
-    description="Ask anything and get an AI-generated response in real-time.",
-    live=False,  # Important to allow streaming-like behavior
-    allow_flagging="never"
+    title=mytitle,
+    description=mydescription,
+    article=myarticle,
+    live=False,
+    allow_flagging="never",
+    examples=myexamples
 )
 
 # Launch the Gradio app