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LICENSE

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+Copyright (c) [2023] [andi5986]
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+
+
+
+
+User
+I want to put it in the github 
+ChatGPT
+Great! Here are the steps to add your MIT License to your GitHub repository:
+
+Navigate to your repository on GitHub.
+
+Click on the "Add file" button near the right of the screen, and then click "Create new file."
+
+In the "Name your file" text box, enter "LICENSE".
+
+In the body of the new file, copy and paste the text of the MIT License template provided above, replacing [year] and [fullname] with the current year and your name or your organization's name.
+
+Scroll to the bottom and enter a brief message describing your changes, such as "Added MIT License."
+
+Make sure the "Commit directly to the main branch" option is selected, and then click "Commit new file."
+
+That's it! Your GitHub repository now includes an MIT License.
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+

README.MD

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+Project Title: CSV Searcher
+A simple web application using Streamlit and Pandas, to help you search through a database of talented individuals based on specific attributes.
+
+Table of Contents
+Installation
+Usage
+License
+Installation
+Before starting, ensure you have the following requirements:
+
+Python 3.6 or later
+Streamlit 0.84.0 or later
+Pandas 1.3.0 or later
+You can install these packages using pip:
+
+bash
+Copy code
+pip install streamlit pandas
+Usage
+Start the Streamlit app by navigating to the directory containing the script and running:
+bash
+Copy code
+streamlit run main.py
+Navigate to the URL provided in your terminal to view the app in your web browser.
+
+On the sidebar, upload your CSV file containing data about various talents using the 'Upload your Talent CSV' button.
+
+In the main section of the app, input any attributes you wish to search for in the database.
+
+The application will display the corresponding rows from the CSV file that match your search criteria. The search is case-insensitive and can be performed on both string and numeric columns.
+
+License
+This project is licensed under the MIT License. See the LICENSE file for details.

app.py

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+import streamlit as st
+import pandas as pd
+
+uploaded_file = st.sidebar.file_uploader('Upload your Talent CSV')
+if uploaded_file is not None:
+        df = pd.read_csv(uploaded_file)
+
+def main():
+    st.title('Search for your star')
+
+    search_term = st.text_input('Enter attributes')
+
+    if search_term:
+        # Split the search term into individual words
+        search_terms = search_term.split()
+
+        # Initialize a mask to select all rows
+        mask = pd.Series([True] * len(df))
+
+        for term in search_terms:
+            # Case-insensitive search in all String columns
+            string_cols = df.select_dtypes(include='object')
+            term_mask = string_cols.apply(lambda x: x.str.contains(term, case=False, na=False)).any(axis=1)
+
+            # Case-insensitive search in all numeric columns
+            numeric_cols = df.select_dtypes(include='number')
+            term_mask |= numeric_cols.apply(lambda x: x == pd.to_numeric(term, errors='coerce')).any(axis=1)
+            
+            # Combine the mask for this term with the overall mask
+            mask &= term_mask
+
+        results = df[mask]
+        
+        st.write(results)
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+pandas==2.0.1
+streamlit==1.22.0