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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")
# ## None type
# def respond(
# message: str,
# history: list[tuple[str, str]], # This will not be used
# system_message: str,
# max_tokens: int,
# temperature: float,
# top_p: float,
# ):
# messages = [{"role": "system", "content": system_message}]
# # Append only the latest user message
# messages.append({"role": "user", "content": message})
# response = ""
# try:
# # Generate response from the model
# for message in client.chat_completion(
# messages,
# max_tokens=max_tokens,
# stream=True,
# temperature=temperature,
# top_p=top_p,
# ):
# if message.choices[0].delta.content is not None:
# token = message.choices[0].delta.content
# response += token
# yield response
# except Exception as e:
# yield f"An error occurred: {e}"
# """
# 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()
import os
import gradio as gr
from huggingface_hub import InferenceClient
# Initialize the Hugging Face Inference Client
client = InferenceClient(
"meta-llama/Meta-Llama-3.1-8B-Instruct",
token= os.getenv("HF_API_TOKEN"),# Replace with your actual token
)
# Define a function to handle the chat input and get a response from the model
def chat_with_model(user_input):
# Call the client to get the model's response
response = ""
for message in client.chat_completion(
messages=[{"role": "user", "content": user_input}],
max_tokens=500,
stream=True,
):
response += message.choices[0].delta.content
return response
# Create a Gradio interface with a chat component
with gr.Blocks() as demo:
chatbot = gr.Chatbot()
with gr.Row():
txt = gr.Textbox(show_label=False, placeholder="Type your message here...")
txt.submit(chat_with_model, inputs=txt, outputs=chatbot)
demo.launch()
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