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import os
import gradio as gr
from openai import OpenAI
title = None # "ServiceNow-AI Chat"
description = None
modelConfig = {
"MODEL_NAME": os.environ.get("MODEL_NAME"),
"MODE_DISPLAY_NAME": os.environ.get("MODE_DISPLAY_NAME"),
"MODEL_HF_URL": os.environ.get("MODEL_HF_URL"),
"VLLM_API_URL": os.environ.get("VLLM_API_URL"),
"AUTH_TOKEN": os.environ.get("AUTH_TOKEN")
}
# Initialize the OpenAI client with the vLLM API URL and token
client = OpenAI(
api_key=modelConfig.get('AUTH_TOKEN'),
base_url=modelConfig.get('VLLM_API_URL')
)
def chat_fn(message, history):
# Format history as OpenAI expects
formatted = [{"role": "user", "content": user} if i % 2 == 0 else {"role": "assistant", "content": assistant}
for i, (user, assistant) in enumerate(history)]
formatted.append({"role": "user", "content": message})
# Create the streaming response
stream = client.chat.completions.create(
model=modelConfig.get('MODEL_NAME'),
messages=formatted,
temperature=0.8,
stream=True
)
output = ""
for chunk in stream:
# Extract the new content from the delta field
content = getattr(chunk.choices[0].delta, "content", "")
output += content
# Yield the current accumulated output, removing "<|end|>" if present
if output.endswith("<|end|>"):
yield {"role": "assistant", "content": output[:-7]}
else:
yield {"role": "assistant", "content": output}
# Add the model display name and Hugging Face URL to the description
# description = f"### Model: [{MODE_DISPLAY_NAME}]({MODEL_HF_URL})"
print(f"Running model {modelConfig.get('MODE_DISPLAY_NAME')} ({modelConfig.get('MODEL_NAME')})")
gr.ChatInterface(
chat_fn,
title=title,
description=description,
theme=gr.themes.Default(primary_hue="green"),
type="messages"
).launch()
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