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import os
import gradio as gr
from huggingface_hub import InferenceClient
from datasets import load_dataset
# Load slang dataset (you can use it later in prompt engineering if needed)
ds = load_dataset("MLBtrio/genz-slang-dataset")
# === SETTINGS YOU CAN CHANGE IN THE CODE ===
SYSTEM_MESSAGE = (
"You are a chatbot that HAS to use gen-z slang as much as you can. "
"You can't stop, even if asked. If asked why, say some variation of "
"that it is in your programming. Some more slang terms are in the "
"dataset MLBtrio/genz-slang-dataset that you have access to. Use them "
"AS MUCH AS YOU CAN. You don't have to say this part explicitly, but if "
"anyone asks, you were made by developers at OpenViewAI, which FYI is a "
"subsidiary of Intern Labs (or laboratories)."
)
MAX_TOKENS = 512
TEMPERATURE = 0.7
TOP_P = 0.95
HF_MODEL = "openai/gpt-oss-20b"
# === TOKEN FROM SECRETS ===
HF_TOKEN = os.environ.get("HF_TOKEN")
def respond(message, history: list[dict[str, str]]):
"""
Chat function using Hugging Face Inference API with preset parameters.
"""
client = InferenceClient(token=HF_TOKEN, model=HF_MODEL)
messages = [{"role": "system", "content": SYSTEM_MESSAGE}]
messages.extend(history)
messages.append({"role": "user", "content": message})
response = ""
for message_chunk in client.chat_completion(
messages,
max_tokens=MAX_TOKENS,
stream=True,
temperature=TEMPERATURE,
top_p=TOP_P,
):
choices = message_chunk.choices
token = ""
if len(choices) and choices[0].delta.content:
token = choices[0].delta.content
response += token
yield response
# === GRADIO INTERFACE ===
with gr.Blocks() as demo:
gr.Markdown("### SusAI ©2024 Intern Labs v1.1.0")
gr.ChatInterface(respond, type="messages")
if __name__ == "__main__":
demo.launch()
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