Create app.py

app.py

@@ -0,0 +1,149 @@
+from openai import AsyncAssistantEventHandler
+from openai import AsyncOpenAI
+import gradio as gr
+import asyncio
+import os
+
+# set the keys
+client = AsyncOpenAI(
+  api_key=os.getenv("OPENAI_API_KEY")
+)
+
+assistantID = os.getenv("OPENAI_ASSISTANT_ID")
+username = os.getenv("YOUR_ID")
+password = os.getenv("YOUR_PASSWORD")
+
+mytitle = "<h1 align=center>RTL AI News Reader : Wat war lass am Land 🇱🇺 an op der Welt 🌎 ?</h1>"
+
+mydescription="""
+<h3 align='center'>Wat fir een Thema interesséiert Iech : 🐶 🏃🏻‍♂️ 🌗 🍇 🌈 🍽️ 🏆 🚘 ✈️ 🩺 </h3>
+<table width=100%>
+  <tr>
+    <th width=50% bgcolor="Moccasin">Stell deng Froen op Lëtzebuergesch, oder an enger anerer Sprooch :</th>
+    <th bgcolor="Khaki">Äntwert vum OpenAI File-Search 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. 
+De ganze lëtzebuergesche Contenu vun RTL.lu vum Ufank (2000, 2012, ???) bis September 2024 gouf a 50 JSON-Dateien opgespléckt an op 
+e Vector Store vum OpenAI File-Search Assistent "RTL News Reader" eropgelueden. 
+All Datei huet manner wéi 5 Milliounen Token, wat eng iewescht Grenz fir den AI Modell ass. 
+Et ass méiglech bis zu 10.000 Dateien op en OpenAI 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 wëllt Der wëssen ?")
+
+myexamples = [
+  "Wat war lass am Juni 2023 ?",
+  "Wat ass gewosst iwwert de SREL ?",
+  "Wat fir eng Katastroph war 2022 zu Lëtzebuerg ?",
+  "Koumen an de leschte Jore gréisser Kriminalfäll viru Geriicht ?"
+]
+
+class EventHandler(AsyncAssistantEventHandler):
+    def __init__(self) -> None:
+        super().__init__()
+        self.response_text = ""
+
+    async def on_text_created(self, text) -> None:
+        self.response_text += str(text)
+
+    async def on_text_delta(self, delta, snapshot):
+        self.response_text += str(delta.value)
+
+    async def on_text_done(self, text):
+        pass
+
+    async def on_tool_call_created(self, tool_call):
+        self.response_text += f"\n[Tool Call]: {str(tool_call.type)}\n"
+
+    async def on_tool_call_delta(self, delta, snapshot):
+        if snapshot.id != getattr(self, "current_tool_call", None):
+            self.current_tool_call = snapshot.id
+            self.response_text += f"\n[Tool Call Delta]: {str(delta.type)}\n"
+
+        if delta.type == 'code_interpreter':
+            if delta.code_interpreter.input:
+                self.response_text += str(delta.code_interpreter.input)
+            if delta.code_interpreter.outputs:
+                self.response_text += "\n\n[Output]:\n"
+                for output in delta.code_interpreter.outputs:
+                    if output.type == "logs":
+                        self.response_text += f"\n{str(output.logs)}"
+
+    async def on_tool_call_done(self, text):
+        pass
+
+# Initialize session variables
+session_data = {"assistant_id": assistantID, "thread_id": None}
+
+async def initialize_thread():
+    # Create a Thread
+    thread = await client.beta.threads.create()
+    # Store thread ID in session_data for later use
+    session_data["thread_id"] = thread.id
+
+async def generate_response(user_input):
+    assistant_id = session_data["assistant_id"]
+    thread_id = session_data["thread_id"]
+
+    # Add a Message to the Thread
+    oai_message = await client.beta.threads.messages.create(
+        thread_id=thread_id,
+        role="user",
+        content=user_input
+    )
+
+    # Create and Stream a Run
+    event_handler = EventHandler()
+
+    async with client.beta.threads.runs.stream(
+        thread_id=thread_id,
+        assistant_id=assistant_id,
+        instructions="Please assist the user with their query.",
+        event_handler=event_handler,
+    ) as stream:
+        # Yield incremental updates
+        async for _ in stream:
+            await asyncio.sleep(0.1)  # Small delay to mimic streaming
+            yield event_handler.response_text
+
+# Gradio interface function (generator)
+async def gradio_chat_interface(user_input):
+    # Create a new event loop if none exists (or if we are in a new thread)
+    try:
+        loop = asyncio.get_running_loop()
+    except RuntimeError:
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+
+    # Initialize the thread if not already done
+    if session_data["thread_id"] is None:
+        await initialize_thread()
+
+    # Generate and yield responses
+    async for response in generate_response(user_input):
+        yield response
+
+# Set up Gradio interface with streaming
+interface = gr.Interface(
+    fn=gradio_chat_interface,
+    inputs=myinput,
+    outputs="markdown",
+    title=mytitle,
+    description=mydescription,
+    article=myarticle,
+    live=False,
+    allow_flagging="never",
+    examples=myexamples
+)
+
+# Launch the Gradio app
+interface.launch(auth=(username, password), auth_message="<h1>RTL AI News Reader</h1><p>Dëse HuggingFace Space ass e Prototyp an nach net zougänglech fir jiddereen. De Projet baséiert op engem OpenAI API File-Search Assistent a benotzt de Modell GPT-4o-mini. Interesséiert KI-Spezialiste kënnen eng ID a Passwuert beim marco.barnig@pt.lu ufroen.</p>")