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import gradio as gr
from huggingface_hub import InferenceClient
# Import the Carmen module. (Ensure that the repository is installed and accessible.)
from carmen.sentience import analyze_sentience
# Initialize the chat client.
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
def chat_and_sentience(message, history, system_message, max_tokens, temperature, top_p):
# Prepare messages for the LLM conversation.
messages = [{"role": "system", "content": system_message}]
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})
messages.append({"role": "user", "content": message})
response = ""
# Generate chat response via streaming.
for chat in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = chat.choices[0].delta.content
response += token
# Update the UI with the intermediate chat history; sentiment analysis hasn't run yet.
yield [history + [(message, response)], None]
# Once the full response is assembled, perform sentience analysis using Carmen.
# The function analyze_sentience is assumed to return a dictionary or list of sentiment scores/labels.
sentiment_results = analyze_sentience(response)
# Format the results for display. Adjust the formatting based on the actual output of analyze_sentience.
if isinstance(sentiment_results, dict):
sentiment_str = "\n".join([f"{k}: {v:.2f}" for k, v in sentiment_results.items()])
elif isinstance(sentiment_results, list):
sentiment_str = "\n".join([f"{item['label']}: {item['score']:.2f}" for item in sentiment_results])
else:
sentiment_str = str(sentiment_results)
# Yield the final state: updated chat history and the sentiment analysis result.
yield [history + [(message, response)], sentiment_str]
# Build the UI with gr.Blocks.
with gr.Blocks() as demo:
with gr.Row():
chatbot = gr.Chatbot(label="Chat")
with gr.Row():
sentiment_box = gr.Textbox(
label="Sentience Moment Scanner",
lines=4,
placeholder="Emotion analysis will appear here..."
)
with gr.Row():
message_input = gr.Textbox(label="Your Message")
with gr.Row():
system_message_input = gr.Textbox(value="You are a friendly Chatbot.", label="System Message")
with gr.Row():
max_tokens_slider = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max New Tokens")
with gr.Row():
temperature_slider = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
with gr.Row():
top_p_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
submit_btn = gr.Button("Send")
# Use a state to track conversation history.
state = gr.State([])
# Wire up the events: on click or pressing enter the chat and sentiment analysis runs.
submit_btn.click(
chat_and_sentience,
inputs=[message_input, state, system_message_input, max_tokens_slider, temperature_slider, top_p_slider],
outputs=[chatbot, sentiment_box],
show_progress=True
)
message_input.submit(
chat_and_sentience,
inputs=[message_input, state, system_message_input, max_tokens_slider, temperature_slider, top_p_slider],
outputs=[chatbot, sentiment_box],
show_progress=True
)
if __name__ == "__main__":
demo.launch()
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