Create app.py

app.py

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+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 : What happened in the country 🇱🇺 and in the world 🌎 ?</h1>"
+
+mydescription="""
+<h3 align='center'>Which topic interests you : 🐶 🏃🏻‍♂️ 🌗 🍇 🌈 🍽️ 🏆 🚘 ✈️ 🩺 </h3>
+<table width=100%>
+  <tr>
+    <th width=50% bgcolor="Moccasin">Ask your questions in Luxembourgish or another language :</th>
+    <th bgcolor="Khaki">Response from the OpenAI File-Search Assistant :</th>
+  </tr>
+</table>
+"""
+
+myarticle ="""
+<h3>Background :</h3>
+<p>This HuggingFace Space demo was created by <a href="https://github.com/mbarnig">Marco Barnig</a>.As an artificial intelligence,
+the <a href="https://platform.openai.com/docs/models">OpenAI model</a> gpt-4o-mini-2024-07-18 is used via API,
+which can utilize up to 128,000 tokens as context, provide an answer to a question with a maximum of 16,384 tokens, 
+and process up to 200,000 tokens per minute (TPM). All english content from RTL.lu from the beginning up to September 2024 has been split into 16 JSON files
+and uploaded to a Vector Store by the OpenAI File-Search Assistant "RTL English News Reader." 
+Each file contains fewer than 5 million tokens, which is an upper limit for the AI model. It is possible to upload up to 10,000 files to an OpenAI Assistant.
+The responses of the examples are cached and therefore displayed without delay.</p>
+"""
+
+myinput = gr.Textbox(lines=3, label=" What would you like to know ?")
+
+myexamples = 
+"What happened in 2014 ?"
+]
+
+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>Lecteur de nouvelles IA de RTL</h1><p>Ce HuggingFace