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import gradio as gr
from huggingface_hub import InferenceClient
"""
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
"""
client = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
css = """
body, html {
height: 100%;
margin: 0;
font-family: Arial, Helvetica, sans-serif;
position: relative;
}
body::before {
content: "";
background-image: url('./favicon.jpg');
background-size: cover;
background-repeat: no-repeat;
background-attachment: fixed;
opacity: 0.5; /* Ajustez l'opacité ici pour la transparence */
top: 0;
left: 0;
bottom: 0;
right: 0;
position: absolute;
z-index: -1; /* Placez l'image derrière le contenu */
}
h1 {
background: radial-gradient(circle, red, black);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
font-size: 2em;
text-align: center;
margin-top: 0;
}
"""
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
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,
):
token = message.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
"""
demo = gr.ChatInterface(
fn=respond,
css=css,
title="Voici notre Chatbot: Le Spéc'IA'liste du vrac",
examples=[
["Calcul moi ma facture si j'ai 12 pied par 35 pied de gravier 0-3/4 pour un epaisseur de 3 pouces en livraison zone 4"],
["Je veux connaitre les produits de paillis chez le specialiste du vrai"]
],
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 (echantillons nucleus)",
)
]
)
if __name__ == "__main__":
demo.launch(share=True) |