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Update app.py
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app.py
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import gradio as gr
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from huggingface_hub import InferenceClient
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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
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"""
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client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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def respond(
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message,
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history: list[tuple[str, str]],
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system_message,
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max_tokens,
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temperature,
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top_p,
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):
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messages = [{"role": "system", "content": system_message}]
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for val in history:
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if val[0]:
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messages.append({"role": "user", "content": val[0]})
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messages.append({"role": "assistant", "content": val[1]})
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messages.append({"role": "user", "content": message})
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response = ""
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for message in client.chat_completion(
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top_p=top_p,
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):
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token = message.choices[0].delta.content
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response += token
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yield response
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title="🤖 Wiser AI Assistant",
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description="Your smart manufacturing assistant powered by Wiser Machines. Ask me anything about automation, productivity, factory operations, or how Wiser can help!",
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additional_inputs=[
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gr.Textbox(value="You are Wiser, an AI assistant specializing in smart manufacturing and factory automation. Respond clearly, concisely, and use real-world manufacturing examples when needed.", label="System message"),
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
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gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
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],
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)
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demo = gr.ChatInterface(..., title="Wiser AI Assistant")
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if __name__ == "__main__":
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with gr.Blocks() as demo:
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gr.Markdown("## Welcome to Wiser AI Assistant")
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gr.Markdown("Ask questions about factory automation, productivity, or how Wiser Machines can help streamline your operations.")
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chat = gr.ChatInterface(
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respond,
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additional_inputs=[
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)
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demo.launch()
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import gradio as gr
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from huggingface_hub import InferenceClient
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# Load the Zephyr model from Hugging Face
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client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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# Define how the chatbot responds
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def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
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messages = [{"role": "system", "content": system_message}]
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for val in history:
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if val[0]:
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messages.append({"role": "user", "content": val[0]})
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messages.append({"role": "assistant", "content": val[1]})
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messages.append({"role": "user", "content": message})
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response = ""
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for message in client.chat_completion(
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top_p=top_p,
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):
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token = message.choices[0].delta.content
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response += token
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yield response
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# Build the UI using Gradio Blocks
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with gr.Blocks() as demo:
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gr.Image("logo.png", height=80, show_label=False, show_download_button=False)
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gr.Markdown("## 🤖 Wiser AI Assistant")
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gr.Markdown(
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"""
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Welcome to **Wiser's AI Assistant**, your smart companion for all things manufacturing.
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Ask questions about:
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- Smart factory operations 🏭
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- Workflow automation ⚙️
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- Efficiency tips 📈
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- Wiser’s AI-powered solutions 🧠
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Learn how Wiser Machines can help transform your production floor!
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"""
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)
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gr.ChatInterface(
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respond,
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additional_inputs=[
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gr.Textbox(
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value="You are Wiser, an expert AI assistant in smart manufacturing. Help users improve factory productivity and explain Wiser’s solutions with confidence.",
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label="System message"
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),
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
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gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
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],
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)
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# Run the app
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if __name__ == "__main__":
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demo.launch()
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