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import gradio as gr
import torch
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
# Load tokenizer and model
model_id = "HuggingFaceH4/zephyr-7b-beta"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
# Define the Gradio interface
with gr.Blocks(fill_height=True) as demo:
with gr.Sidebar():
gr.Markdown("## Zephyr-7B Unlimited Assistant")
gr.Markdown(
"This assistant is powered by the HuggingFaceH4/zephyr-7b-beta model.\n"
"You can start chatting right away!"
)
login_button = gr.LoginButton("🔐 Sign in to Hugging Face") # Optional UI
chatbot = gr.Chatbot(label="🧠 Zephyr-7B Assistant")
user_input = gr.Textbox(placeholder="Ask anything...", show_label=False)
chat_history = []
def chat(user_msg, history):
# Add system + user messages to chat history
messages = [
{"role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate."}
]
for human, ai in history:
messages.append({"role": "user", "content": human})
messages.append({"role": "assistant", "content": ai})
messages.append({"role": "user", "content": user_msg})
# Format the prompt using the tokenizer's chat template
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
# Generate response
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
response = outputs[0]["generated_text"].split("</s>")[-1].strip()
# Append new interaction
history.append((user_msg, response))
return history, ""
user_input.submit(chat, inputs=[user_input, chatbot], outputs=[chatbot, user_input])
demo.launch() |