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import gradio as gr
from huggingface_hub import InferenceClient
# Load the Zephyr model from Hugging Face
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# Define how the chatbot responds
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 message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
# Build the UI using Gradio Blocks
with gr.Blocks() as demo:
gr.Markdown("## 🤖 Wiser AI Assistant")
gr.Markdown(
"""
Welcome to **Wiser's AI Assistant**, your smart companion for all things manufacturing.
Ask questions about:
- Smart factory operations 🏭
- Workflow automation ⚙️
- Efficiency tips 📈
- Wiser’s AI-powered solutions 🧠
Learn how Wiser Machines can help transform your production floor!
"""
)
gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(
value="You are Wiser, an expert AI assistant in smart manufacturing. Help users improve factory productivity and explain Wiser’s solutions with confidence.",
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)"),
],
)
# Run the app
if __name__ == "__main__":
demo.launch()
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