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Sleeping
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import gradio as gr
from huggingface_hub import InferenceClient
import chardet
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token="") #generate Access tokens
file_content = None
def respond(message, history, system_message, max_tokens, temperature, top_p, file=None):
global file_content
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 = ""
if file:
try:
file_content = file.decode("utf-8")
except UnicodeDecodeError:
result = chardet.detect(file)
encoding = result['encoding']
file_content = file.decode(encoding, errors='ignore')
if "contenu du fichier" in message.lower() and file_content:
response += f"Contenu du fichier :\n{file_content}"
yield response
return
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
with gr.Blocks() as demo:
gr.Image(value="logo-gaia.png", label="Logo")
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)"),
gr.File(label="Télécharger un fichier", type="binary"),
],
)
if __name__ == "__main__":
demo.launch()
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