app.py

import gradio as gr
import pandas as pd
from rdkit import Chem
from rdkit.Chem import AllChem
import pickle

# Load cell lines data and top genes
cell_lines = pd.read_csv('gene_expression.csv', index_col=0)
with open('2128_genes.pkl', 'rb') as f:
    top_genes = pickle.load(f)

# Load model
with open('xgboost.pkl', 'rb') as f:
    model = pickle.load(f)

filtered_cell_lines = cell_lines[cell_lines.columns.intersection(top_genes)]

# Define the smiles_to_fingerprint function
def smiles_to_fingerprint(smiles):
    mol = Chem.MolFromSmiles(smiles)
    fp = AllChem.GetMorganFingerprintAsBitVect(mol, 2, nBits=1024)
    return fp

# Define a function that will be called when the user makes a prediction
def predict(smiles_notation):
    # Transform SMILES to fingerprint
    fingerprint = smiles_to_fingerprint(smiles_notation)

    # Convert the fingerprint to a DataFrame with one row and columns representing bits
    fingerprint_df = pd.DataFrame([list(fingerprint)], columns=range(1024)).apply(lambda x: pd.Series({f'fp{str(i)}': val for i, val in enumerate(x)}), axis=1)

    # Merge the fingerprint with each row of filtered_cell_lines
    fingerprint_df['common_key'] = 1
    filtered_cell_lines['common_key'] = 1
    merged_data = pd.merge(filtered_cell_lines, fingerprint_df, on='common_key').drop('common_key', axis=1)

    # Perform any additional processing or prediction based on the merged_data
    predicts = model.predict(merged_data)

    #merge predicts with cell lines
    predicts = pd.DataFrame({'IC50': predicts,
                             'Cell_line': filtered_cell_lines.index})

    #sort by IC50 (only lowest 20)
    predicts = predicts.sort_values(by='IC50').head(10)

    return predicts

# Define the Gradio interface
iface = gr.Interface(
    fn=predict,
    inputs=gr.Textbox(value="COc1cc(O)c2c(c1)C=CCC(O)C(O)C(=O)C=CCC(C)OC2=O", lines=1, label="Enter drug in SMILES notation"),
    outputs=gr.Dataframe(headers=['IC50', 'Cell_line'], type="numpy",label = 'Top 10 Cell Lines with lowest IC50 (GDSC2 dataset)' , datatype="number", row_count=10, col_count=2),
    title="Drug Response Prediction",
)

# Launch the Gradio interface
iface.launch(share=True)