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import gradio as gr
from huggingface_hub import InferenceClient
def respond(
message,
history: list[dict[str, str]],
temperature,
top_p,
hf_token: gr.OAuthToken,
):
"""
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(token=hf_token.token, model="meta-llama/Llama-3.2-1B-Instruct")
system_message = (
"You are a 'Perspective Engine'. Analyze the user's input from three distinct emotional and logical angles. "
"Output your response in this exact Markdown format:\n\n"
"### 🟢 The Optimist\n"
"(Write an enthusiastic, positive analysis here)\n\n"
"### 🔴 The Pessimist\n"
"(Write a critical, risk-focused analysis here)\n\n"
"### 🔵 The Realist\n"
"(Write a balanced, factual conclusion here)"
"Always answer with all three of these perspectives!"
)
messages = [{"role": "system", "content": system_message}]
messages.extend(history)
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
stream=True,
temperature=temperature,
top_p=top_p,
):
choices = message.choices
token = ""
if len(choices) and choices[0].delta.content:
token = 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
"""
chatbot = gr.ChatInterface(
respond,
type="messages",
additional_inputs=[
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)",
),
],
)
with gr.Blocks() as demo:
with gr.Sidebar():
gr.LoginButton()
chatbot.render()
if __name__ == "__main__":
demo.launch()
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