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