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"""import gradio as gr
from huggingface_hub import InferenceClient
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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
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
import json
# Retrieve the API token from the environment variable
API_TOKEN = os.getenv("HF_READ_TOKEN")
# Initialize the Hugging Face Inference Client
client = InferenceClient(
"mistralai/Mistral-Nemo-Instruct-2407",
token=API_TOKEN
)
# System prompt to define model behavior
system_prompt = "You are a helpful assistant that provides concise and accurate answers."
# Function to handle the chat completion
def hf_chat(user_input):
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_input}
]
response = ""
try:
# Stream the response
for message in client.chat_completion(
messages=messages,
max_tokens=500,
stream=True,
):
try:
# Parse each part of the response carefully
content = message.choices[0].delta.content
response += content
except (KeyError, json.JSONDecodeError) as e:
# Print error details for debugging
print(f"Error while parsing response: {e}")
continue # Continue receiving the stream
except Exception as e:
# Catch and print any unexpected errors during the stream
return f"Error occurred: {str(e)}"
return response
# Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# Hugging Face Chat Completion")
with gr.Row():
with gr.Column():
user_input = gr.Textbox(
label="Enter your message",
placeholder="Ask me anything..."
)
submit_btn = gr.Button("Submit")
with gr.Column():
output = gr.Textbox(label="Response")
submit_btn.click(fn=hf_chat, inputs=user_input, outputs=output)
# Launch Gradio app
if __name__ == "__main__":
demo.launch(show_api=True, share=False) |