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streamlit_app.py

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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()