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| import os | |
| import gradio as gr | |
| import modelscope_studio as mgr | |
| from modelscope_studio.components.Chatbot.llm_thinking_presets import qwen | |
| def resolve_assets(relative_path): | |
| return os.path.join(os.path.dirname(__file__), "../resources", | |
| relative_path) | |
| conversation = [ | |
| [ | |
| None, { | |
| "text": f""" | |
| Use accordion tag: | |
| <accordion title="Using tool"> | |
| ```json | |
| {{"text": "glorious weather", "resolution": "1024*1024"}} | |
| ``` | |
| </accordion> | |
| Qwen preset: | |
| Action: image_gen | |
| Action Input: {{"text": "glorious weather", "resolution": "1024*1024"}} | |
| Observation: <result>})</result> Based on your description"glorious weather",I generated a picture.}) | |
| Action: 「An arbitrary text representation that will be displayed as the name of the thought chain call」 | |
| Action Input: 「Any json or md content will be displayed in the drop-down box of the calling process」 | |
| Observation: <result>「Any md content will be displayed in the drop-down box when the call is completed」</result> | |
| """, | |
| "flushing": False | |
| } | |
| ], | |
| ] | |
| with gr.Blocks() as demo: | |
| mgr.Chatbot( | |
| value=conversation, | |
| llm_thinking_presets=[qwen()], | |
| height=600, | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue().launch() | |