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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("Qwen/Qwen2.5-Coder-32B-Instruct")
# 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=2048, 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 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("Qwen/Qwen2.5-Coder-32B-Instruct")
# def respond(message, history: list[tuple[str, str]]):
# system_message = (
# "You are a helpful and experienced coding assistant specialized in web development. "
# "Help the user by generating complete and functional code for building websites. "
# "You can provide HTML, CSS, JavaScript, and backend code (like Flask, Node.js, etc.) based on their requirements. "
# "Break down the tasks clearly if needed, and be friendly and supportive in your responses.")
# max_tokens = 2048
# temperature = 0.7
# top_p = 0.95
# 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)
# if __name__ == "__main__":
# demo.launch()
# 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("Qwen/Qwen2.5-Coder-32B-Instruct")
# def respond(message, history: list[tuple[str, str]]):
# system_message = (
# "You are a helpful and experienced coding assistant specialized in web development. "
# "Help the user by generating complete and functional code for building websites. "
# "You can provide HTML, CSS, JavaScript, and backend code (like Flask, Node.js, etc.) based on their requirements. "
# "Break down the tasks clearly if needed, and be friendly and supportive in your responses."
# )
# max_tokens = 2048
# temperature = 0.7
# top_p = 0.95
# 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)
# if __name__ == "__main__":
# demo.launch()
import gradio as gr
from huggingface_hub import InferenceClient
# 1. Instantiate with named model param
client = InferenceClient(model="Qwen/Qwen2.5-Coder-32B-Instruct")
def respond(message, history: list[tuple[str, str]]):
system_message = (
"You are a helpful and experienced coding assistant specialized in web development. "
"Help the user by generating complete and functional code for building websites. "
"You can provide HTML, CSS, JavaScript, and backend code (like Flask, Node.js, etc.) "
"based on their requirements."
)
max_tokens = 2048
temperature = 0.7
top_p = 0.95
# Build messages in OpenAI-compatible format
messages = [{"role": "system", "content": system_message}]
for user_msg, assistant_msg in history:
if user_msg:
messages.append({"role": "user", "content": user_msg})
if assistant_msg:
messages.append({"role": "assistant", "content": assistant_msg})
messages.append({"role": "user", "content": message})
response = ""
# 2. Use named parameters and alias if desired
for chunk in client.chat.completions.create(
model="Qwen/Qwen2.5-Coder-32B-Instruct",
messages=messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
# 3. Extract token content
token = chunk.choices[0].delta.content or ""
response += token
yield response
# 4. Wire up Gradio chat interface
demo = gr.ChatInterface(respond, type="messages")
if __name__ == "__main__":
demo.launch()
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