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Update app.py
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app.py
CHANGED
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@@ -14,16 +14,6 @@ import tempfile
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import shutil
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import os
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# Search Terms
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example_search_terms = [
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{"id": "gpt-3", "emoji": "🤖"},
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{"id": "stable-diffusion", "emoji": "🎨"},
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{"id": "whisper", "emoji": "🗣️"},
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{"id": "bert", "emoji": "📖"},
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{"id": "resnet", "emoji": "🖼️"}
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]
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# Initialize HuggingFace with access token
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def init_huggingface(token: str):
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"""Initialize HuggingFace with access token."""
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@@ -88,24 +78,6 @@ def SwarmyTime(data: List[Dict]) -> Dict:
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aggregated["unique_authors"] = len(aggregated["unique_authors"])
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return aggregated
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def search_and_aggregate(query, search_type, token, example_term):
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if example_term:
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query = example_term.split(" ")[1] # Extract the user ID from the button label
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df = search_hub(query, search_type, token)
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data = df.to_dict('records')
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aggregated = SwarmyTime(data)
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html_results = display_results(df)
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return [
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html_results,
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"Status: Ready to download",
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"",
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aggregated,
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search_type,
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data
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]
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def search_hub(query: str, search_type: str, token: str = None) -> pd.DataFrame:
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"""Search the Hugging Face Hub for models, datasets, or spaces."""
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api = HfApi(token=token)
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@@ -279,13 +251,6 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
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Search and download models, datasets, and spaces from Hugging Face.
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""")
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with gr.Row():
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example_search_buttons = gr.Dataset(
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components=[gr.Button(value=f"{term['emoji']} {term['id']}", variant="secondary") for term in example_search_terms],
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label="Example Search Terms",
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samples=[]
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)
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with gr.Row():
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with gr.Column(scale=3):
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hf_token = gr.Textbox(
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@@ -348,49 +313,22 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
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search_type,
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data
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]
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async def handle_readme_download(data, token):
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if data:
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download_link, status = await download_all_readmes(data, token)
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return [f"Status: {status}", download_link]
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else:
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return ["Status: No results to download", ""]
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def handle_repo_download(data, search_type, token):
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if data:
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download_link, status = create_repo_zip(data, search_type, token)
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return [f"Status: {status}", download_link]
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else:
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return ["Status: No results to download", ""]
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# if not data:
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# return ["Status: No results to download", ""]
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# download_link, status = await download_all_readmes(data, token)
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# return [f"Status: {status}", download_link]
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#def handle_repo_download(data, search_type, token):
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# if not data:
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# return ["Status: No results to download", ""]
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# download_link, status = create_repo_zip(data, search_type, token)
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# return [f"Status: {status}", download_link]
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#search_button.click(
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# search_and_aggregate,
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# inputs=[search_query, search_type, hf_token],
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# outputs=[
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# results_html,
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# download_status,
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# download_area,
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# aggregated_output,
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# search_type_state,
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# current_results
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# ]
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#)
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search_button.click(
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search_and_aggregate,
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inputs=[search_query, search_type, hf_token
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outputs=[
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results_html,
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download_status,
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@@ -398,10 +336,9 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
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aggregated_output,
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search_type_state,
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current_results
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]
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call_after=lambda data: [handle_readme_download(data[-1], hf_token.value), handle_repo_download(data[-1], search_type.value, hf_token.value)]
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)
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download_readme_button.click(
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handle_readme_download,
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inputs=[current_results, hf_token],
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import shutil
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import os
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# Initialize HuggingFace with access token
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def init_huggingface(token: str):
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"""Initialize HuggingFace with access token."""
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aggregated["unique_authors"] = len(aggregated["unique_authors"])
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return aggregated
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def search_hub(query: str, search_type: str, token: str = None) -> pd.DataFrame:
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"""Search the Hugging Face Hub for models, datasets, or spaces."""
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api = HfApi(token=token)
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Search and download models, datasets, and spaces from Hugging Face.
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""")
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with gr.Row():
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with gr.Column(scale=3):
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hf_token = gr.Textbox(
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search_type,
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data
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]
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async def handle_readme_download(data, token):
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if not data:
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return ["Status: No results to download", ""]
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download_link, status = await download_all_readmes(data, token)
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return [f"Status: {status}", download_link]
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def handle_repo_download(data, search_type, token):
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if not data:
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return ["Status: No results to download", ""]
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download_link, status = create_repo_zip(data, search_type, token)
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return [f"Status: {status}", download_link]
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search_button.click(
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search_and_aggregate,
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inputs=[search_query, search_type, hf_token],
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outputs=[
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results_html,
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download_status,
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aggregated_output,
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search_type_state,
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current_results
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]
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)
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download_readme_button.click(
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handle_readme_download,
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inputs=[current_results, hf_token],
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