from huggingface_hub import InferenceClient import gradio as gr client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") def format_prompt(message, history, system_prompt=None): prompt = "" for user_prompt, bot_response in history: prompt += f"[INST] {user_prompt} [/INST]" prompt += f" {bot_response} " if system_prompt: prompt += f"[SYS] {system_prompt} [/SYS]" prompt += f"[INST] {message} [/INST]" return prompt def generate( prompt, history, system_prompt="Sou o assistente TSM, pronto para auxiliar em qualquer tarefa, meu idioma principal é o português brasileiro", temperature=0.2, max_new_tokens=8192, top_p=0.95, repetition_penalty=1.0, ): temperature = float(temperature) if temperature < 1e-2: temperature = 1e-2 top_p = float(top_p) generate_kwargs = dict( temperature=temperature, max_new_tokens=max_new_tokens, top_p=top_p, repetition_penalty=repetition_penalty, do_sample=True, seed=42, ) formatted_prompt = format_prompt(prompt, history, system_prompt) stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) output = "" for response in stream: output += response.token.text yield output return output mychatbot = gr.Chatbot( avatar_images=["./user.png", "./botm.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,) demo = gr.ChatInterface( fn=generate, chatbot=mychatbot, title="Olá, eu sou o TSM 3.5 em que posso ajudar?", css="body { background-color: inherit; overflow-x:hidden;}" ":root {--color-accent: transparent !important; --color-accent-soft:transparent !important; --code-background-fill:black !important; --body-text-color:white !important;}" "#component-2 {background:#ffffff1a; display:contents;}" "div#component-0 { height: auto !important;}" ".gradio-container.gradio-container-4-8-0.svelte-1kyws56.app {background: #000000;}gradio-app {background: #333 !important;}" "gradio-app {background: #000000 !important; background-attachment: fixed !important; background-position: top;}" ".panel.svelte-vt1mxs {background: transparent; padding:0;}" ".block.svelte-90oupt { background: transparent; border-color: transparent;}" ".bot.svelte-12dsd9j.svelte-12dsd9j.svelte-12dsd9j { background: #ffffff1a; border-color: transparent; color: white;}" ".user.svelte-12dsd9j.svelte-12dsd9j.svelte-12dsd9j { background: #ffffff1a; border-color: transparent; color: white; padding: 10px 18px;}" "div.svelte-iyf88w{ background: #cc0000; border-color: trasparent; border-radius: 25px;}" "textarea.scroll-hide.svelte-1f354aw {background: #555 !important;}" ".primary.svelte-cmf5ev { background: transparent; color: white;}" ".primary.svelte-cmf5ev:hover { background: transparent; color: white;}" "div#component-9 { max-width: fit-content; margin-left: auto; margin-right: auto;}" "button#component-8, button#component-10, button#component-11, button#component-12 { flex: none; background: #ffffff1a; border: none; color: white; margin-right: auto; margin-left: auto; border-radius: 9px; min-width: fit-content;}" ".share-button.svelte-12dsd9j { display: none;}" "footer.svelte-mpyp5e { display: none !important;}" ".message-buttons-bubble.svelte-12dsd9j.svelte-12dsd9j.svelte-12dsd9j { border-color: #FFFFFF; background: #FFFFFF;}" ".bubble-wrap.svelte-12dsd9j.svelte-12dsd9j.svelte-12dsd9j {padding: 0;}" ".prose h1 { color: white !important; font-size: 16px !important; font-weight: normal !important; background: #ffffff1a; padding: 20px; border-radius: 20px; width: 90%; margin-left: auto !important; margin-right: auto !important;}" ".toast-wrap.svelte-pu0yf1 { display:none !important;}" ".scroll-hide { scrollbar-width: auto !important;}" ".main svelte-1kyws56 {max-width: 800px; align-self: center;}" "div#component-4 {max-width: 650px; margin-left: auto; margin-right: auto;}" "body::-webkit-scrollbar { display: none;}" ) demo.queue().launch(show_api=False)