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import gradio as gr
from dataclasses import asdict
from transformers import Tool, ReactCodeAgent # type: ignore
from transformers.agents import stream_to_gradio, HfApiEngine # type: ignore
# Import tool from Hub
image_generation_tool = Tool.from_space( # type: ignore
space_id="black-forest-labs/FLUX.1-schnell",
name="image_generator",
description="Generates an image following your prompt. Returns a PIL Image.",
api_name="/infer",
)
llm_engine = HfApiEngine("Qwen/Qwen2.5-Coder-32B-Instruct")
# Initialize the agent with both tools and engine
agent = ReactCodeAgent(tools=[image_generation_tool], llm_engine=llm_engine)
def interact_with_agent(prompt, history):
messages = []
yield messages
for msg in stream_to_gradio(agent, prompt):
messages.append(asdict(msg)) # type: ignore
yield messages
yield messages
demo = gr.ChatInterface(
interact_with_agent,
chatbot= gr.Chatbot(
label="Agent",
type="messages",
avatar_images=(
None,
"https://em-content.zobj.net/source/twitter/53/robot-face_1f916.png",
),
),
examples=[
["Generate an image of an astronaut riding an alligator"],
["I am writing a children's book for my daughter. Can you help me with some illustrations?"],
],
type="messages",
)
if __name__ == "__main__":
demo.launch()
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