main.py

import streamlit as st
import pandas as pd
import numpy as np
import os

# ---------------------------
# Function: Interpolate Data
# ---------------------------
def iterative_interpolation(df):
    """
    Interpolates missing values (NaN) using the average of non-diagonal neighbors.
    """
    df = df.copy()
    rows, cols = df.shape
    for i in range(rows):
        for j in range(cols):
            if pd.isna(df.iat[i, j]):
                # Collect neighbors (up, down, left, right)
                neighbors = []
                if i > 0 and not pd.isna(df.iat[i-1, j]):
                    neighbors.append(df.iat[i-1, j])
                if i < rows-1 and not pd.isna(df.iat[i+1, j]):
                    neighbors.append(df.iat[i+1, j])
                if j > 0 and not pd.isna(df.iat[i, j-1]):
                    neighbors.append(df.iat[i, j-1])
                if j < cols-1 and not pd.isna(df.iat[i, j+1]):
                    neighbors.append(df.iat[i, j+1])
                if neighbors:
                    df.iat[i, j] = np.mean(neighbors)
    return df

# ---------------------------
# Streamlit App Layout
# ---------------------------
st.title("CSV Interpolation App")
st.write("""
Upload a CSV file or try the built-in sample dataset.  
This app interpolates missing values using neighboring values, 
previews the results, and lets you download the processed CSV.
""")

# ---------------------------
# File Upload + Sample Data
# ---------------------------
uploaded_file = st.file_uploader("Choose a CSV file", type="csv")

use_sample = st.checkbox("Use sample dataset")

df_input = None
if uploaded_file:
    df_input = pd.read_csv(uploaded_file, header=None)
elif use_sample:
    sample_path = os.path.join(os.path.dirname(__file__), "input_test_data.csv")
    df_input = pd.read_csv(sample_path, header=None)
    st.info("Loaded sample dataset: `input_test_data.csv`")

# ---------------------------
# Display & Interpolation
# ---------------------------
if df_input is not None:
    st.subheader("Original Data")
    st.dataframe(df_input)

    if st.button("Interpolate Missing Values"):
        df_result = iterative_interpolation(df_input)
        st.subheader("Interpolated Data")
        st.dataframe(df_result)

        csv = df_result.to_csv(index=False, header=False).encode("utf-8")
        st.download_button(
            label="Download Interpolated CSV",
            data=csv,
            file_name="interpolated_results.csv",
            mime="text/csv",
        )