| Stell deng Fro op englesch, fir einfach Froe versteet d'AI och Lëtzebuergesch ! | Äntwert vum OpenAI Code-Interpreter Assistent : |
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Dës HuggingFace Space Demo gouf vum Marco Barnig realiséiert. Als kënstlech Intelligenz gëtt, mëttels API, den OpenAI Modell 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.
""" 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__() 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=""" You are a Code Interpreter to analyze JSON files with RTL comments. Here is the format of the JSON files : [ { "context_id": "", "date_created": "", "text": " ", "user_id": "", "referer": "", "status": ", "thumbs": [ { "user_id": "", "score": "up", "date": "" }, { "user_id": "", "score": "down", "date": "" }, } ] 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 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()