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import gradio as gr
from huggingface_hub import InferenceClient
import os
from dotenv import load_dotenv
load_dotenv() # Charge les variables depuis .env
"""
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
"""
# Vérifie si la clé API est définie dans l'environnement (pour local)
api_token = os.getenv("HF_API_TOKEN")
model_id = "mistralai/Mistral-7B-Instruct-v0.3"
#model_id = "HuggingFaceH4/zephyr-7b-beta"
if api_token:
# Local : Utilise la clé API
print("Local key found in .env")
client = InferenceClient(token=api_token, model=model_id)
else:
# Sur Hugging Face Spaces : Pas besoin de clé
print("We are on HF, no need API key")
client = InferenceClient(model=model_id)
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(
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)",
),
],
)
if __name__ == "__main__":
demo.launch() |