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from huggingface_hub import InferenceClient |
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import gradio as gr |
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client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") |
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def format_prompt(message, history, system_prompt=None): |
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prompt = "<s>" |
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for user_prompt, bot_response in history: |
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prompt += f"[INST] {user_prompt} [/INST]" |
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prompt += f" {bot_response}</s> " |
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if system_prompt: |
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prompt += f"[SYS] {system_prompt} [/SYS]" |
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prompt += f"[INST] {message} [/INST]" |
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return prompt |
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def generate( |
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prompt, history, system_prompt=None, temperature=0.2, max_new_tokens=512, top_p=0.95, repetition_penalty=1.0, |
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): |
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temperature = float(temperature) |
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if temperature < 1e-2: |
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temperature = 1e-2 |
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top_p = float(top_p) |
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generate_kwargs = dict( |
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temperature=temperature, |
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max_new_tokens=max_new_tokens, |
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top_p=top_p, |
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repetition_penalty=repetition_penalty, |
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do_sample=True, |
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seed=42, |
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) |
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formatted_prompt = format_prompt(prompt, history, system_prompt) |
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stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) |
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output = "" |
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for response in stream: |
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output += response.token.text |
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yield output |
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return output |
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mychatbot = gr.Chatbot( |
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avatar_images=["./user.png", "./botm.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,) |
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demo = gr.ChatInterface( |
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fn=generate, |
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chatbot=mychatbot, |
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title="Hello! I'm Elisa by SpriFi.👋 How can I help you today?", |
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css="body { background-color: inherit; overflow-x:hidden;}" |
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":root {--color-accent: transparent !important; --color-accent-soft:transparent !important; --code-background-fill:black !important; --body-text-color:white !important;}" |
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"#component-2 {background:#ffffff1a; display:contents;}" |
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"div#component-0 { height: auto !important;}" |
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".gradio-container.gradio-container-4-8-0.svelte-1kyws56.app {max-width: 100% !important;}" |
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"gradio-app {background: linear-gradient(134deg,#00425e 0%,#001a3f 43%,#421438 77%) !important; background-attachment: fixed !important; background-position: top;}" |
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".panel.svelte-vt1mxs {background: transparent; padding:0;}" |
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".block.svelte-90oupt { background: transparent; border-color: transparent;}" |
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".bot.svelte-12dsd9j.svelte-12dsd9j.svelte-12dsd9j { background: #ffffff1a; border-color: transparent; color: white;}" |
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".user.svelte-12dsd9j.svelte-12dsd9j.svelte-12dsd9j { background: #ffffff1a; border-color: transparent; color: white; padding: 10px 18px;}" |
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"div.svelte-iyf88w{ background: #cc98d445; border-color: transparent; border-radius: 25px;}" |
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"textarea.scroll-hide.svelte-1f354aw { background: transparent; color: #fff !important;}" |
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".primary.svelte-cmf5ev { background: transparent; color: white;}" |
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".primary.svelte-cmf5ev:hover { background: transparent; color: white;}" |
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"button#component-8 { display: none; position: absolute; margin-top: 60px; border-radius: 25px;}" |
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"div#component-9 { max-width: fit-content; margin-left: auto; margin-right: auto;}" |
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"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;}" |
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".share-button.svelte-12dsd9j { display: none;}" |
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"footer.svelte-mpyp5e { display: none !important;}" |
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".message-buttons-bubble.svelte-12dsd9j.svelte-12dsd9j.svelte-12dsd9j { border-color: #31546E; background: #31546E;}" |
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".bubble-wrap.svelte-12dsd9j.svelte-12dsd9j.svelte-12dsd9j {padding: 0;}" |
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".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;}" |
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".toast-wrap.svelte-pu0yf1 { display:none !important;}" |
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".scroll-hide { scrollbar-width: auto !important;}" |
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".main svelte-1kyws56 {max-width: 800px; align-self: center;}" |
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"div#component-4 {max-width: 650px; margin-left: auto; margin-right: auto;}" |
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"body::-webkit-scrollbar { display: none;}" |
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) |
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demo.queue().launch(show_api=False) |
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