The Upload page provides a header and a file uploader widget that restricts uploads to CSV files and opens a file picker. When a file is chosen, the UploadedFile object is passed to a load_uploaded_csv helper that wraps pandas.read_csv to isolate I/O logic. The app shows a success message with row and column counts, renders a dataframe preview of the first rows, and offers an optional checkbox to display summary statistics via dataframe_profile. If no file is uploaded, an informational prompt appears. The loader can be extended to handle encodings, delimiters, or large files.
"To learn how to upload, explore, visualize, and export data seamlessly with Streamlit, just keep reading. The Upload page transforms your app from a demo into a real tool. It lets users bring their own CSV files, instantly view the data, and even generate quick summary statistics, all without touching the underlying code."
"elif page == "Upload": st.header("Upload Your Own CSV") uploaded = st.file_uploader("Choose a CSV file", type="csv") if uploaded: user_df = load_uploaded_csv(uploaded) st.success(f"Loaded {user_df.shape[0]} rows × {user_df.shape[1]} columns") st.dataframe(user_df.head()) if st.checkbox("Show summary stats"): st.dataframe(dataframe_profile(user_df)) else: st.info("Upload a CSV to inspect it.")"
"st.header("Upload Your Own CSV") This visually separates the Upload section and sets context immediately. uploaded = st.file_uploader("Choose a CSV file", type="csv") The st.file_uploader() widget opens a file picker and restricts uploads to CSVs only, preventing invalid file types. When a user selects a file, uploaded becomes a Streamlit UploadedFile object (file-like); otherwise, it remains None. if uploaded: user_df = load_uploaded_csv(uploaded) The helper load_uploaded_csv() from data_loader.py wraps pandas.read_csv(), cleanly isolating I/O logic. This function can later evolve to handle different encodings, delimiters, or larger"
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