app.py

import streamlit as st
import pandas as pd
import prediction as pred_module  # Import the prediction module
import os

def extract_windows(sequence, window_size=55):
    """
    Extract windows of a given size centered around each 'K' residue in the sequence.

    Parameters:
    sequence (str): The amino acid sequence.
    window_size (int): The size of the window to extract (must be an odd number).

    Returns:
    list: A list of subsequences of the specified window size centered on 'K'.
    """
    half_window = window_size // 2
    windows = []

    for i in range(len(sequence)):
        if sequence[i] == 'K':
            start = i - half_window
            end = i + half_window + 1

            if start >= 0 and end <= len(sequence):
                window_sequence = sequence[start:end]
                windows.append((i, window_sequence))

    return windows

def main():
    st.title("Ubiquitinated Protein Sequence Predictor")

    st.write("""
    This app predicts whether a given protein sequence contains ubiquitination sites.
    Enter a protein sequence and the app will analyze windows size 'n-1/2' residues centered around each 'K' residue.
    If any window is predicted positive, the sequence is considered to contain ubiquitination sites.
    """)

    sequence_input = st.text_area("Enter protein sequence:")

    if st.button("Predict"):
        if len(sequence_input) < 55:
            st.error("Sequence length must be at least 55 residues.")
        else:
            windows = extract_windows(sequence_input)
            final_prediction = 0  # Initialize final prediction as negative

            for i, window_sequence in windows:
                pred_result = pred_module.predict_sequence_label(window_sequence)
                if pred_result == 1:
                    final_prediction = 1  # If any window is positive, set final prediction to positive
                    break

            if final_prediction == 1:
                st.success("The sequence contains ubiquitination sites.")
            else:
                st.success("The sequence does not contain ubiquitination sites.")

            # Collect user feedback
            st.write("Provide your feedback on the prediction:")
            feedback = st.radio(
                "Was the prediction correct?",
                ("Yes", "No"),
                key="feedback"
            )

            if st.button("Submit Feedback"):
                feedback_data = {
                    "sequence": [sequence_input],
                    "predicted_label": [final_prediction],
                    "feedback": [feedback]
                }
                feedback_df = pd.DataFrame(feedback_data)
                
                # Check if feedback.csv exists
                if not os.path.isfile("feedback.csv"):
                    feedback_df.to_csv("feedback.csv", index=False)
                else:
                    try:
                        existing_feedback = pd.read_csv("feedback.csv")
                        feedback_df = pd.concat([existing_feedback, feedback_df], ignore_index=True)
                        feedback_df.to_csv("feedback.csv", index=False)
                    except pd.errors.EmptyDataError:
                        feedback_df.to_csv("feedback.csv", index=False)
                
                st.success("Feedback submitted successfully!")

            # Display feedback table
            st.write("User Feedback Table (Last 10 Entries):")
            try:
                feedback_df = pd.read_csv("feedback.csv")
                last_10_feedback = feedback_df.tail(10)
                st.dataframe(last_10_feedback)
            except FileNotFoundError:
                st.write("No feedback available yet.")
            except pd.errors.EmptyDataError:
                st.write("Feedback file is empty. No feedback available yet.")

if __name__ == "__main__":
    main()
# Add a footer
    st.markdown(
        """
        <style>
        .footer {
            position: fixed;
            left: 0;
            bottom: 0;
            width: 100%;
            background-color: #f1f1f1;
            color: black;
            text-align: center;
            padding: 10px;
        }
        .footer p {
            font-size: 1.2em;
            font-weight: bold;
        }
        </style>
        <div class="footer">
        <p>© Shubham Kale and Prof.N.Arul.Murugan, IIIT Delhi</p>
        </div>
        """, unsafe_allow_html=True
    )