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import gradio as gr |
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import requests |
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import huggingface_hub |
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client = huggingface_hub.InferenceClient(model="mistralai/Mixtral-8x7B-Instruct-v0.1") |
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PROMPT = """ |
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Write a short, imperative description of the provided app's purpose. It MUST ALWAYS be under 80 characters and a single-sentence. You can mention some technology names that you extract from the source code. |
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Example descriptions: "Remove background from images.", "Generate captions for images using ViT and GPT2.", "Predict the nutritional value of food based on an image of the food." |
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The provided app.py file: |
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""" |
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def generate(spaces): |
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output = "" |
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space_ids = [ |
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str.removeprefix("https://huggingface.co/spaces/") |
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for str in spaces.split() |
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if len(str) > 0 |
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] |
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print(space_ids) |
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for space_id in space_ids: |
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app_file = huggingface_hub.SpaceCard.load(space_id).data.get("app_file", "app.py") |
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with open(huggingface_hub.hf_hub_download(space_id, repo_type="space", filename=app_file)) as app_file_path: |
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app_py = app_file_path.read() |
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input = PROMPT + f"```py{app_py}```" |
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chat_completion = client.chat_completion( |
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messages=[ |
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{"role": "user", "content": input}, |
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], |
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max_tokens=500, |
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) |
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output += chat_completion.choices[0].message.content.strip().strip('"') + "\n" |
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yield output |
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iface = gr.Interface( |
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description=""" |
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## Generate description for a space using a LLM |
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Uses mixtral, feel free to duplicate to tweak stuff. |
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""", |
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fn=generate, |
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inputs=gr.Textbox( |
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label="list of Spaces to generate a description for", |
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value=""" |
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https://huggingface.co/spaces/julien-c/coqui |
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https://huggingface.co/spaces/TTS-AGI/TTS-Arena |
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https://huggingface.co/spaces/playgroundai/playground-v2.5 |
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https://huggingface.co/spaces/amirgame197/Remove-Video-Background |
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard |
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""".strip(), |
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), |
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outputs=gr.Textbox(label="descriptions", lines=4), |
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) |
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iface.queue(default_concurrency_limit=20).launch() |
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