app.py

import streamlit as st
import pandas as pd
import primer3
from Bio import SeqIO
import os
from io import StringIO

# Ensure the 'temp' directory exists for saving temporary files
temp_dir = "temp"
os.makedirs(temp_dir, exist_ok=True)

# Streamlit UI setup
st.set_page_config(page_title="PCR Primer Design", page_icon="🧬", layout="wide")
uploaded_file = st.file_uploader("Upload a GenBank file", type=['gb', 'gbk'])

# Custom CSS for styling
st.markdown("""
<style>
h1, h2, h3 {
    color: #1f77b4;
    font-weight: bold;
}
.stProgress {
    margin-top: 20px;
}
</style>
""", unsafe_allow_html=True)

def extract_features_from_genbank(genbank_content, feature_types=['CDS', 'tRNA', 'gene']):
    """Extracts specified features from GenBank content."""
    text_stream = StringIO(genbank_content.decode("utf-8")) if isinstance(genbank_content, bytes) else genbank_content
    record = SeqIO.read(text_stream, "genbank")
    features = {ftype: [] for ftype in feature_types}
    for feature in record.features:
        if feature.type in feature_types:
            features[feature.type].append(feature)
    return features, record

def design_primers_for_region(sequence, product_size_range, num_to_return=10):
    """Design primers for a specific sequence."""
    size_min, size_max = map(int, product_size_range.split('-'))
    return primer3.bindings.designPrimers(
        {
            'SEQUENCE_TEMPLATE': str(sequence),
            'PRIMER_PRODUCT_SIZE_RANGE': [[size_min, size_max]]
        },
        {
            'PRIMER_OPT_SIZE': 20,
            'PRIMER_MIN_SIZE': 18,
            'PRIMER_MAX_SIZE': 23,
            'PRIMER_OPT_TM': 60.0,
            'PRIMER_MIN_TM': 57.0,
            'PRIMER_MAX_TM': 63.0,
            'PRIMER_MIN_GC': 20.0,
            'PRIMER_MAX_GC': 80.0,
            'PRIMER_NUM_RETURN': num_to_return,
        }
    )

if uploaded_file is not None:
    genbank_content = StringIO(uploaded_file.getvalue().decode("utf-8"))
    features, record = extract_features_from_genbank(genbank_content)
    feature_type = st.selectbox('Select feature type:', ['CDS', 'tRNA', 'gene'])

    feature_options = [f"{feature.qualifiers.get('gene', [''])[0]} ({feature.location})" for feature in features[feature_type]]
    selected_index = st.selectbox(f'Select a {feature_type}:', options=range(len(feature_options)), format_func=lambda x: feature_options[x])
    selected_feature = features[feature_type][selected_index]

    feature_sequence = selected_feature.extract(record.seq)
    st.write(f"Selected {feature_type} sequence (length: {len(feature_sequence)} bp):")
    st.code(str(feature_sequence), language="text")  # Display sequence in code format

    product_size_range = st.text_input("Enter desired PCR product size range (e.g., 150-500):", value="150-500")
    min_num_primers = st.number_input("Enter minimum number of primer pairs to return:", min_value=5, value=5, step=1)

    if st.button(f'Design Primers for selected {feature_type}'):
        with st.spinner('Designing primers...'):  # Show a spinner while primers are being designed
            primers = design_primers_for_region(feature_sequence, product_size_range, num_to_return=min_num_primers)

        primer_data = []
        for i in range(min_num_primers):
            left_sequence = primers.get(f'PRIMER_LEFT_{i}_SEQUENCE', 'N/A')
            right_sequence = primers.get(f'PRIMER_RIGHT_{i}_SEQUENCE', 'N/A')
            if left_sequence != 'N/A' and right_sequence != 'N/A':
                primer_info = {
                    'Primer Pair': i + 1,
                    'Left Sequence': left_sequence,
                    'Right Sequence': right_sequence,
                    'Left TM (°C)': primers.get(f'PRIMER_LEFT_{i}_TM', 'N/A'),
                    'Right TM (°C)': primers.get(f'PRIMER_RIGHT_{i}_TM', 'N/A'),
                    'Left Length': len(left_sequence),
                    'Right Length': len(right_sequence),
                    'PCR Product Size (bp)': primers.get(f'PRIMER_PAIR_{i}_PRODUCT_SIZE', 'N/A')
                }
                primer_data.append(primer_info)

        if primer_data:
            st.subheader('Designed Primers')
            primer_df = pd.DataFrame(primer_data)
            st.table(primer_df)  # Use st.table to display the primer data

            csv = primer_df.to_csv(index=False).encode('utf-8')
            st.download_button(
                "Download Primers as CSV",
                csv,
                "primers.csv",
                "text/csv",
                key='download-csv'
            )
        else:
            st.error('No primers were found. Please adjust your parameters and try again.')