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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()
# import gradio as gr
# from huggingface_hub import InferenceClient
# hf_token = "HF_TOKEN"
# # Ensure token is available
# if hf_token is None:
# raise ValueError("HUGGINGFACEHUB_API_TOKEN is not set in .env file or environment.")
# # Instantiate Hugging Face Inference Client with token
# client = InferenceClient(
# model="Qwen/Qwen2.5-Coder-32B-Instruct",
# token=hf_token
# )
# 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 conversation history
# 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 = ""
# # Stream the response from the model
# 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,
# ):
# token = chunk.choices[0].delta.content or ""
# response += token
# yield response
# # Gradio UI
# demo = gr.ChatInterface(respond, type="messages")
# if __name__ == "__main__":
# demo.launch()
# import gradio as gr
# from transformers import AutoTokenizer, AutoModelForCausalLM
# import torch
# # Load once globally
# tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-32B-Instruct")
# model = AutoModelForCausalLM.from_pretrained(
# "Qwen/Qwen2.5-Coder-32B-Instruct",
# device_map="auto",
# torch_dtype=torch.float16,
# )
# def respond(message, history):
# system_prompt = (
# "You are a helpful coding assistant specialized in web development. "
# "Provide complete code snippets for HTML, CSS, JS, Flask, Node.js etc."
# )
# # Build input prompt including chat history
# chat_history = ""
# for user_msg, bot_msg in history:
# chat_history += f"User: {user_msg}\nAssistant: {bot_msg}\n"
# prompt = f"{system_prompt}\n{chat_history}User: {message}\nAssistant:"
# inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
# outputs = model.generate(
# **inputs,
# max_new_tokens=512,
# temperature=0.7,
# do_sample=True,
# top_p=0.95,
# eos_token_id=tokenizer.eos_token_id,
# )
# generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
# # Extract only the new response part after the prompt
# response = generated_text[len(prompt):].strip()
# # Append current Q/A to history
# history.append((message, response))
# return "", history
# demo = gr.ChatInterface(respond, type="messages")
# if __name__ == "__main__":
# demo.launch()
import os
import gradio as gr
from huggingface_hub import InferenceClient
from dotenv import load_dotenv
# Load .env variables (make sure to have HF_TOKEN in .env or set as env var)
load_dotenv()
HF_TOKEN = os.getenv("HF_TOKEN") # or directly assign your token here as string
# Initialize InferenceClient with Hugging Face API token
client = InferenceClient(
model="Qwen/Qwen2.5-Coder-32B-Instruct",
token=HF_TOKEN
)
def respond(message, history):
"""
Chat response generator function streaming from Hugging Face Inference API.
"""
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
# Prepare messages in OpenAI chat 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 = ""
# Stream response tokens from Hugging Face Inference API
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,
):
token = chunk.choices[0].delta.get("content", "")
response += token
yield response
# Create Gradio chat interface
demo = gr.ChatInterface(fn=respond, title="Website Building Assistant")
if __name__ == "__main__":
demo.launch()
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