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import gradio as gr
from huggingface_hub import InferenceClient
import logging
# Initialize the InferenceClient with the model ID from Hugging Face
client = InferenceClient(model="HuggingFaceH4/zephyr-7b-beta")
# Set up logging
logging.basicConfig(
filename='chatbot_log.log',
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
)
def log_conversation(user_message, bot_response):
"""Logs the conversation between the user and the AI."""
logging.info(f"User: {user_message}")
logging.info(f"Bot: {bot_response}")
def respond(
message: str,
history: list[tuple[str, str]],
system_message: str,
max_tokens: int,
temperature: float,
top_p: float,
stop_sequence: str,
stream_response: bool,
):
"""Generates a response from the AI model based on the user's message and chat history."""
# Prepare the conversation history for the API call
messages = [{"role": "system", "content": system_message}]
for user_input, assistant_response in history:
if user_input:
messages.append({"role": "user", "content": user_input})
if assistant_response:
messages.append({"role": "assistant", "content": assistant_response})
# Add the latest user message to the conversation
messages.append({"role": "user", "content": message})
# Initialize an empty response
response = ""
try:
if stream_response:
# Generate a response from the model with streaming
for message in client.chat_completion(
messages=messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
stop=stop_sequence,
):
token = message.choices[0].delta.get("content", "")
response += token
yield response
else:
# Generate a complete response without streaming
result = client.chat_completion(
messages=messages,
max_tokens=max_tokens,
stream=False,
temperature=temperature,
top_p=top_p,
stop=stop_sequence,
)
response = result.choices[0].message.get("content", "")
log_conversation(message, response)
yield response
except Exception as e:
error_message = f"An error occurred: {str(e)})"
logging.error(error_message)
yield error_message
# Define the ChatInterface with additional input components for user customization
demo = gr.ChatInterface(
fn=respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System Message", lines=2),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max New Tokens"),
gr.Slider(minimum=0.1, maximum=2.0, 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.Textbox(value="", label="Stop Sequence (optional)", lines=1),
gr.Checkbox(label="Stream Response", value=True),
],
title="AI Chatbot Interface",
description="Interact with an AI chatbot powered by Hugging Face's Zephyr-7B model. Customize the chatbot's behavior and response generation settings.",
theme="default",
)
# Launch the Gradio interface
if __name__ == "__main__":
logging.info("Launching the Gradio interface...")
demo.launch()
logging.info("Gradio interface launched successfully.")
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