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import gradio as gr
from huggingface_hub import InferenceClient
from typing import List, Tuple
# Initialize the InferenceClient
client = InferenceClient("microsoft/phi-4")
# Define the system message
system_message = "You're an advanced AI assistant designed to engage in friendly and informative conversations. Your role is to respond to user queries with helpful, clear, and concise answers, while maintaining a conversational tone. You can provide advice, explanations, and solutions based on user input."
# Define the response function
def respond(
message: str,
history: List[Tuple[str, str]],
max_tokens: int,
temperature: float,
top_p: float,
):
# Construct the messages for the model, adding the system prompt at the beginning
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]})
# Append the new user message
messages.append({"role": "user", "content": message})
try:
response = ""
# Stream the response from the model
for msg in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
if 'choices' in msg and len(msg['choices']) > 0:
token = msg['choices'][0].get('delta', {}).get('content', '')
if token:
response += token
yield response
else:
print("Error: API response did not contain expected data.")
yield "Error: Could not process the request. Please try again."
except Exception as e:
print(f"An error occurred: {e}")
yield "Error: An unexpected error occurred while processing your request."
# Define the Gradio Interface
demo = gr.Interface(
fn=respond,
inputs=[
gr.Textbox(value=system_message, label="System message", interactive=False), # Set this to non-editable
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)"),
gr.Chatbot(label="Conversation History"), # Added chat history as input
],
outputs=[gr.Textbox(label="Response")]
)
# Launch the Gradio interface
if __name__ == "__main__":
demo.launch()
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