import re import gradio as gr from huggingface_hub import InferenceClient client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") system_instructions = "[SYSTEM] You will be provided with text, and your task is to classify task tasks are (text generation, image generation, tts) answer with only task type that prompt user give, do not say anything else and stop as soon as possible. Example: User- What is friction , BOT - text generation [USER]" def classify_task(prompt): generate_kwargs = dict( temperature=0.5, max_new_tokens=5, top_p=0.7, repetition_penalty=1.2, do_sample=True, seed=42, ) formatted_prompt = system_instructions + prompt + "[BOT]" stream = client.text_generation( formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) output = "" for response in stream: if not response.token.text == "": output += response.token.text return output # Create the Gradio interface with gr.Blocks() as demo: with gr.Row(): text_uesr_input = gr.Textbox(label="Enter text 📚") output = gr.Textbox(label="Translation") with gr.Row(): translate_btn = gr.Button("Translate 🚀") translate_btn.click(fn=classify_task, inputs=text_uesr_input, outputs=output, api_name="translate_text") # Launch the app if __name__ == "__main__": demo.launch()