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import streamlit as st
import pandas as pd
import io
from utils import load_csv, get_file_info, filter_dataframe
# Set page config
st.set_page_config(
page_title="CSV Viewer",
page_icon="📊",
layout="wide"
)
# Custom CSS for better styling
st.markdown("""
<style>
.main-header {
font-size: 2.5rem;
font-weight: bold;
color: #1f77b4;
text-align: center;
margin-bottom: 1rem;
}
.sub-header {
font-size: 1.2rem;
color: #666;
text-align: center;
margin-bottom: 2rem;
}
.file-info {
background-color: #f0f2f6;
padding: 1rem;
border-radius: 0.5rem;
margin-bottom: 1rem;
}
.anycoder-link {
position: fixed;
bottom: 10px;
right: 10px;
font-size: 0.8rem;
color: #666;
}
</style>
""", unsafe_allow_html=True)
def main():
# Header
st.markdown('<div class="main-header">CSV Viewer</div>', unsafe_allow_html=True)
st.markdown('<div class="sub-header">Upload and explore your CSV files with ease</div>', unsafe_allow_html=True)
# Anycoder attribution
st.markdown('<div class="anycoder-link">Built with <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank">anycoder</a></div>', unsafe_allow_html=True)
# File uploader
uploaded_file = st.file_uploader(
"Choose a CSV file",
type=["csv"],
help="Upload a CSV file to view and analyze its contents"
)
if uploaded_file is not None:
try:
# Load the CSV file
df = load_csv(uploaded_file)
# Display file info
file_info = get_file_info(df, uploaded_file.name)
st.markdown(f"""
<div class="file-info">
<strong>File:</strong> {file_info['filename']}<br>
<strong>Rows:</strong> {file_info['rows']}<br>
<strong>Columns:</strong> {file_info['columns']}<br>
<strong>Size:</strong> {file_info['size']}
</div>
""", unsafe_allow_html=True)
# Create tabs for different views
tab1, tab2, tab3 = st.tabs(["📊 Data View", "📈 Statistics", "🔍 Filter"])
with tab1:
st.subheader("Data Preview")
# Display options
col1, col2 = st.columns(2)
with col1:
rows_to_show = st.slider(
"Number of rows to display",
min_value=5,
max_value=min(100, len(df)),
value=min(10, len(df))
)
with col2:
show_columns = st.multiselect(
"Select columns to display",
options=df.columns.tolist(),
default=df.columns.tolist()
)
# Display the dataframe
if show_columns:
st.dataframe(
df[show_columns].head(rows_to_show),
use_container_width=True,
height=400
)
else:
st.warning("Please select at least one column to display")
# Download button
csv = df.to_csv(index=False).encode('utf-8')
st.download_button(
label="Download CSV",
data=csv,
file_name="filtered_data.csv",
mime="text/csv"
)
with tab2:
st.subheader("Data Statistics")
if st.checkbox("Show summary statistics"):
st.dataframe(df.describe(), use_container_width=True)
if st.checkbox("Show data types"):
st.write(df.dtypes)
if st.checkbox("Show missing values"):
missing_values = df.isnull().sum()
st.write(missing_values[missing_values > 0])
with tab3:
st.subheader("Filter Data")
st.write("Filter your data based on column values")
# Column selection for filtering
filter_column = st.selectbox(
"Select column to filter",
options=df.columns.tolist()
)
if filter_column:
# Get unique values for the selected column
unique_values = df[filter_column].unique()
if len(unique_values) > 20:
st.warning("This column has many unique values. Consider using a different filter method.")
filter_method = st.radio(
"Filter method",
["Range", "Contains"]
)
if filter_method == "Range":
min_val = st.number_input(
"Minimum value",
value=float(df[filter_column].min())
)
max_val = st.number_input(
"Maximum value",
value=float(df[filter_column].max())
)
if st.button("Apply Filter"):
filtered_df = filter_dataframe(df, filter_column, min_val, max_val)
st.dataframe(filtered_df, use_container_width=True)
else:
search_term = st.text_input("Contains text")
if st.button("Apply Filter"):
filtered_df = df[df[filter_column].astype(str).str.contains(search_term, case=False, na=False)]
st.dataframe(filtered_df, use_container_width=True)
else:
selected_values = st.multiselect(
"Select values to include",
options=unique_values
)
if st.button("Apply Filter"):
filtered_df = df[df[filter_column].isin(selected_values)]
st.dataframe(filtered_df, use_container_width=True)
except Exception as e:
st.error(f"Error processing file: {str(e)}")
if __name__ == "__main__":
main() |