# Install required dependencies if not present
import os
os.system("pip install streamlit pandas xlsxwriter")

import streamlit as st
import pandas as pd
import xlsxwriter
from io import BytesIO
from collections import defaultdict



# Function to find repeated amino acids in the protein sequence
def find_homorepeats(protein):
    n = len(protein)
    freq = defaultdict(int)
    i = 0

    while i < n:
        curr = protein[i]
        repeat = ""
        while i < n and curr == protein[i]:
            repeat += protein[i]
            i += 1

        # Only consider repeats of length > 1
        if len(repeat) > 1:
            freq[repeat] += 1

    return freq

# Function to process a single CSV file and return its analysis
def process_csv(file):
    df = pd.read_csv(file)
    if len(df.columns) < 3:
        st.error(f"Error: The file must have at least three columns: ID, Protein Name, Sequence")
        return None

    # Storing entry ID, protein name, and sequence
    sequences = []
    for _, row in df.iterrows():
        entry_id = str(row[0])
        protein_name = str(row[1])
        sequence = str(row[2]).replace('"', '').replace(' ', '')
        sequences.append((entry_id, protein_name, sequence))

    # Analyzing homorepeats in the sequences
    homorepeats = set()
    sequence_data = []
    for entry_id, protein_name, sequence in sequences:
        freq = find_homorepeats(sequence)
        homorepeats.update(freq.keys())  # Collect unique homorepeats
        sequence_data.append((entry_id, protein_name, freq))

    return homorepeats, sequence_data

import pandas as pd
import streamlit as st
from io import BytesIO
import xlsxwriter

# Function to generate and download Excel workbook with separate sheets for each input file
def create_excel(sequences_data, homorepeats, filenames):
    output = BytesIO()
    workbook = xlsxwriter.Workbook(output, {'in_memory': True})

    # Iterate through sequences data grouped by filenames and create separate sheets
    for file_index, file_data in enumerate(sequences_data):
        filename = filenames[file_index]
        worksheet = workbook.add_worksheet(filename[:31])  # Limit sheet name to 31 characters

        # Write the header for the current file
        worksheet.write(0, 0, "Entry ID")
        worksheet.write(0, 1, "Protein Name")
        col = 2
        for repeat in sorted(homorepeats):
            worksheet.write(0, col, repeat)
            col += 1

        # Write data for each sequence in the current file
        row = 1
        for entry_id, protein_name, freq in file_data:
            worksheet.write(row, 0, entry_id)
            worksheet.write(row, 1, protein_name)
            col = 2
            for repeat in sorted(homorepeats):
                worksheet.write(row, col, freq.get(repeat, 0))
                col += 1
            row += 1

    workbook.close()
    output.seek(0)
    return output

# Streamlit UI components
st.title("Protein Homorepeat Analysis")

# Step 1: Upload Excel Files
uploaded_files = st.file_uploader("Upload Excel files", accept_multiple_files=True, type=["xlsx"])

# Step 2: Process files and display results
if uploaded_files:
    all_homorepeats = set()
    all_sequences_data = []
    filenames = []

    for file in uploaded_files:
        excel_data = pd.ExcelFile(file)
        homorepeats, sequence_data = process_excel(excel_data)  # Modify your process_csv function to process_excel
        if homorepeats is not None:
            all_homorepeats.update(homorepeats)
            all_sequences_data.append(sequence_data)
            filenames.append(file.name)

    if all_sequences_data:
        st.success(f"Processed {len(uploaded_files)} files successfully!")

        # Step 3: Generate and download the Excel report
        excel_file = create_excel(all_sequences_data, all_homorepeats, filenames)

        # Download the Excel file
        st.download_button(
            label="Download Excel file",
            data=excel_file,
            file_name="protein_homorepeat_results.xlsx",
            mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
        )

        # Step 4: Display summary table
        if st.checkbox("Show Results Table"):
            # Convert the sequences data into a DataFrame for easy display
            rows = []
            for file_index, file_data in enumerate(all_sequences_data):
                filename = filenames[file_index]
                for entry_id, protein_name, freq in file_data:
                    row = {"Filename": filename, "Entry ID": entry_id, "Protein Name": protein_name}
                    row.update({repeat: freq.get(repeat, 0) for repeat in sorted(all_homorepeats)})
                    rows.append(row)

            result_df = pd.DataFrame(rows)
            st.dataframe(result_df)

# Function to process the Excel file
def process_excel(excel_data):
    # Custom logic to process each sheet within the Excel file
    homorepeats = set()
    sequence_data = []

    for sheet_name in excel_data.sheet_names:
        df = excel_data.parse(sheet_name)
        for index, row in df.iterrows():
            entry_id = row['Entry ID']
            protein_name = row['Protein Name']
            freq = {repeat: row[repeat] for repeat in df.columns[2:]}  # Assuming repeats start from 3rd column
            sequence_data.append((entry_id, protein_name, freq))
            homorepeats.update(freq.keys())
    
    return homorepeats, sequence_data