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import requests |
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import streamlit as st |
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@st.cache_data(ttl=3600) |
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def fetch_readme_content(): |
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try: |
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url = "https://raw.githubusercontent.com/EthicalML/awesome-production-machine-learning/master/README.md" |
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response = requests.get(url, timeout=30) |
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response.raise_for_status() |
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return response.text |
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except requests.exceptions.RequestException as e: |
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st.error(f"Failed to fetch README.md content: {e}") |
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return "" |
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class SearchApplication: |
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def __init__(self): |
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self.title = "Production Machine Learning Search" |
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self.set_page_config() |
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st.header(self.title) |
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self.query = st.text_input("Search", value="") |
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st.caption( |
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"This search toolkit is a user-friendly platform that enables efficient exploration and filtering of the comprehensive [Awesome Production Machine Learning](https://github.com/EthicalML/awesome-production-machine-learning) list, which includes over 500 machine learning SDKs, making it an indispensable resource for researchers, developers, and enthusiasts in the field." |
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) |
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st.write("#") |
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self.show_search_results() |
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def set_page_config(self): |
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st.set_page_config( |
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page_title=self.title, |
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page_icon="😎", |
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layout="centered", |
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) |
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def show_search_results(self): |
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if self.query: |
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st.write("#") |
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readme_content = fetch_readme_content() |
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if readme_content: |
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search_results = [] |
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lines = readme_content.split("\n") |
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for line in lines: |
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if self.query.lower() in line.lower(): |
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search_results.append(line) |
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num_search_results = len(search_results) |
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st.write(f"A total of {num_search_results} matches found.") |
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if num_search_results > 0: |
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for result in search_results: |
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st.write(result) |
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else: |
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st.write("No matches found.") |
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if __name__ == "__main__": |
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SearchApplication() |
