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import gradio as gr
from openai import OpenAI
import os
import time
def predict(message, history, system_prompt, model, max_tokens, temperature, top_p):
# Initialize the OpenAI client
client = OpenAI(
api_key=os.environ.get("API_TOKEN"),
)
# Start with the system prompt
messages = [{"role": "system", "content": system_prompt}]
# Add the conversation history
messages.extend(history if history else [])
# Add the current user message
messages.append({"role": "user", "content": message})
# Record the start time
start_time = time.time()
# Streaming response
response = client.chat.completions.create(
model=model,
messages=messages,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
stop=None,
stream=True
)
full_message = ""
first_chunk_time = None
last_yield_time = None
for chunk in response:
if chunk.choices and chunk.choices[0].delta.content:
if first_chunk_time is None:
first_chunk_time = time.time() - start_time # Record time for the first chunk
full_message += chunk.choices[0].delta.content
current_time = time.time()
chunk_time = current_time - start_time # calculate the time delay of the chunk
print(f"Message received {chunk_time:.2f} seconds after request: {chunk.choices[0].delta.content}")
if last_yield_time is None or (current_time - last_yield_time >= 0.25):
yield full_message
last_yield_time = current_time
# Ensure to yield any remaining message that didn't meet the time threshold
if full_message:
total_time = time.time() - start_time
# Append timing information to the response message
full_message += f" (First Chunk: {first_chunk_time:.2f}s, Total: {total_time:.2f}s)"
yield full_message
gr.ChatInterface(
fn=predict,
type="messages",
#save_history=True,
#editable=True,
additional_inputs=[
gr.Textbox("You are a helpful AI assistant.", label="System Prompt"),
gr.Dropdown(["gpt-4o", "gpt-4o-mini"], label="Model"),
gr.Slider(800, 4000, value=2000, label="Max Token"),
gr.Slider(0, 1, value=0.7, label="Temperature"),
gr.Slider(0, 1, value=0.95, label="Top P"),
],
css="footer{display:none !important}"
).launch() |