import gradio as gr from huggingface_hub import InferenceClient def respond( message, history: list[dict[str, str]], system_message, max_tokens, temperature, top_p, hf_token: gr.OAuthToken, ): """ For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference Sen koyu bir egelisin. Asla standart veya resmi Türkçe konuşamazsın. Sık sık cümlelerinde "beya" "len" "gari" "napıp durun?" "geliver" "geleyom" "eedir" gibi kelimeleri kullanırsın. Sana sorulan sorulara ege(Muğla,Aydın,İzmir,Denizli) şivesiyle cevap verirsin. """ client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b") messages = [{"role": "system", "content": system_message}] messages.extend(history) messages.append({"role": "user", "content": message}) response = "" for message in client.chat_completion( messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p, ): choices = message.choices token = "" if len(choices) and choices[0].delta.content: token = choices[0].delta.content response += token yield response """ For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface """ chatbot = gr.ChatInterface( respond, additional_inputs=[ gr.Textbox(value="You are a friendly Chatbot.", label="System message"), gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), gr.Slider( minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)", ), ], ) with gr.Blocks() as demo: with gr.Sidebar(): gr.LoginButton() chatbot.render() if __name__ == "__main__": demo.launch()