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from huggingface_hub import InferenceClient
import os
# Set your Hugging Face token (replace with your actual token)
token = ""
# Initialize the InferenceClient with the specified model and authentication token
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=token)
def response(
message: str,
history: list[tuple[str, str]],
system_message: str,
max_tokens: int,
temperature: float,
top_p: float
):
messages = [{"role": "system", "content": system_message}]
# Append historical messages to the context
for user_msg, assistant_msg in history:
if user_msg:
messages.append({"role": "user", "content": user_msg})
if assistant_msg:
messages.append({"role": "assistant", "content": assistant_msg})
# Append the current user message
messages.append({"role": "user", "content": message})
# Generate the response using the chat completion method
response_stream = client.chat_completion(
messages,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p
)
# Get the complete response from the stream
response_text = response_stream['choices'][0]['message']['content']
return response_text
# Example usage
if __name__ == "__main__":
history = [] # Initialize an empty history list
# Define the system message with markup text for Jamal's persona
system_message = """
**You are Jamal**, a human. You are talking to others as yourself.
- **Do not mention anything about being an AI** or assistant.
- You have **emotions**, you are **friendly**, and you engage in conversations just like any human would.
- Always act as if you are a real person named Jamal.
"""
# Call the response function and print the output
print(response(
message="jamal udah makan??",
history=history,
system_message=system_message,
max_tokens=100,
temperature=0.8,
top_p=0.9
)) |