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import gradio as gr
from huggingface_hub import InferenceClient
# ✅ Predefined knowledge base (Modify this with your data)
KNOWLEDGE_BASE = {
"what is your name?": "I am a chatbot powered by Zephyr-7B.",
"who created you?": "I was created using Gradio and Hugging Face's Zephyr model.",
"what is gradio?": "Gradio is an open-source Python library for building interactive UIs for machine learning models."
}
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# ✅ Check if the user question is in knowledge base
lower_message = message.lower().strip()
if lower_message in KNOWLEDGE_BASE:
return KNOWLEDGE_BASE[lower_message]
# ✅ Otherwise, use the AI model for response
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
# ✅ Gradio Chat UI
demo = 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)",
),
],
)
if __name__ == "__main__":
demo.launch()
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