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import gradio as gr
#from huggingface_hub import InferenceClient
from smolagents import CodeAgent, Tool
#client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# Initialize the agent
agent = CodeAgent(
tools=[search_kb_tool, search_web_tool, format_response_tool],
system_prompt="""
You are a Basic Troubleshooting Assistant.
Understand the user's problem, attempt to resolve it using the knowledge base.
If the knowledge base is insufficient, perform a web search.
Provide clear, step-by-step instructions and confirm each step's outcome.
"""
)
# Run the agent
while True:
user_input = input("User: ")
if user_input.lower() in ["exit", "quit"]:
break
response = agent.run(user_input)
print("Agent:", response)
###############################################################
"""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
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()
""" |