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import gradio as gr
from huggingface_hub import InferenceClient
# Step 1: Read your background info
with open("BACKGROUND.md", "r", encoding="utf-8") as f:
background_text = f.read()
# Step 2: Set up your InferenceClient (same as before)
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 msg in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = msg.choices[0].delta.content
response += token
# 'yield' returns partial responses for streaming
yield response
# Step 3: Build a Gradio Blocks interface with two Tabs
with gr.Blocks() as demo:
# (A) First Tab: Chat Interface
with gr.Tab("GPT Chat Agent"):
gr.Markdown("## Welcome to Varun's GPT Agent")
gr.Markdown("Feel free to ask questions about Varun’s journey, skills, and more!")
chat = 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)"),
],
)
# (B) Second Tab: Background Document
with gr.Tab("Varun's Background"):
gr.Markdown("# About Varun")
gr.Markdown(background_text)
# Step 4: Launch
if __name__ == "__main__":
demo.launch()
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