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import gradio as gr
from gradio import ChatMessage
import time
import gradio as gr
from gradio import ChatMessage
import time
sleep_time = 0.1
long_sleep_time = 1
def generate_response(history):
history.append(
ChatMessage(
role="user", content="What is the weather in San Francisco right now?"
)
)
yield history
time.sleep(sleep_time)
history.append(
ChatMessage(
role="assistant",
content="In order to find the current weather in San Francisco, I will need to use my weather tool.",
)
)
yield history
time.sleep(sleep_time)
history.append(
ChatMessage(
role="assistant",
content="",
metadata={"title": "Gathering Weather Websites", "id": 1},
)
)
yield history
time.sleep(long_sleep_time)
history[-1].content = "Will check: weather.com and sunny.org"
yield history
time.sleep(sleep_time)
history.append(
ChatMessage(
role="assistant",
content="Received weather from weather.com.",
metadata={"title": "API Success ✅", "parent_id": 1, "id": 2},
)
)
yield history
time.sleep(sleep_time)
history.append(
ChatMessage(
role="assistant",
content="API Error when connecting to sunny.org.",
metadata={"title": "API Error 💥 ", "parent_id": 1, "id": 3},
)
)
yield history
time.sleep(sleep_time)
history.append(
ChatMessage(
role="assistant",
content="I will try again",
)
)
yield history
def simulate_thinking_chat(message: str, history: list):
"""Mimicking thinking process and response"""
# Add initial empty thinking message to chat history
history.append( # Adds new message to the chat history list
ChatMessage( # Creates a new chat message
role="assistant", # Specifies this is from the assistant
content="", # Initially empty content
metadata={"title": "Thinking Process 💭"} # Setting a thinking header here
)
)
time.sleep(0.5)
yield history # Returns current state of chat history
# Define the thoughts that LLM will "think" through
thoughts = [
"First, I need to understand the core aspects of the query...",
"Now, considering the broader context and implications...",
"Analyzing potential approaches to formulate a comprehensive answer...",
"Finally, structuring the response for clarity and completeness..."
]
# Variable to store all thoughts as they accumulate
accumulated_thoughts = ""
# Loop through each thought
for thought in thoughts:
time.sleep(0.5) # Add a samll delay for realism
# Add new thought to accumulated thoughts with markdown bullet point
accumulated_thoughts += f"- {thought}\n\n" # \n\n creates line breaks
# Update the thinking message with all thoughts so far
history[-1] = ChatMessage( # Updates last message in history
role="assistant",
content=accumulated_thoughts.strip(), # Remove extra whitespace
metadata={"title": "💭 Thinking Process"} # Shows thinking header
)
yield history # Returns updated chat history
# After thinking is complete, adding the final response
history.append(
ChatMessage(
role="assistant",
content="Based on my thoughts and analysis above, my response is: This dummy repro shows how thoughts of a thinking LLM can be progressively shown before providing its final answer."
)
)
yield history # Returns final state of chat history
# Gradio blocks with gr.chatbot
with gr.Blocks() as demo1:
gr.Markdown("# Thinking LLM Demo 🤔")
chatbot = gr.Chatbot(type="messages", render_markdown=True)
msg = gr.Textbox(placeholder="Type your message...")
msg.submit(
lambda m, h: (m, h + [ChatMessage(role="user", content=m)]),
[msg, chatbot],
[msg, chatbot]
).then(
simulate_thinking_chat,
[msg, chatbot],
chatbot
)
with gr.Blocks(theme="ocean") as demo2:
chatbot = gr.Chatbot(type="messages", height=500, show_copy_button=True)
demo2.load(generate_response, chatbot, chatbot)
demo_tabbed = gr.TabbedInterface([demo1, demo2], ["First tab", "Second tab"])
if __name__ == "__main__":
demo_tabbed.launch() |