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import gradio as gr
import os
import subprocess
import uuid
import pandas as pd
# Helper to save SMILES string to a temporary CSV
def save_smiles_to_csv(smiles: str, temp_dir="temp_inputs"):
os.makedirs(temp_dir, exist_ok=True)
file_path = os.path.join(temp_dir, f"{uuid.uuid4().hex}.csv")
df = pd.DataFrame({"smiles": [smiles]})
df.to_csv(file_path, index=False)
return file_path
# Core prediction logic
def predict(model_version, dataset_name, input_type, file=None, smiles=None):
# Set paths
if model_version == "Vanilla Chemprop":
model_dir = "chemprop"
model_path = f"model_weight/{dataset_name}/best_unbalanced.pt"
script_path = "chemprop/chemprop/cli/predict.py"
else:
model_dir = "chemprop_update"
model_path = f"model_weight/{dataset_name}/best_bert_fusion.pt"
script_path = "chemprop_update/chemprop/cli/predict.py"
# Prepare input file
if input_type == "Upload CSV":
if file is None:
return "Please upload a CSV file."
input_path = file.name
else:
if not smiles:
return "Please enter a SMILES string."
input_path = save_smiles_to_csv(smiles)
# Run prediction command
cmd = [
"python", script_path,
"--test-path", input_path,
"--model-paths", model_path,
"--smiles-columns", "smiles"
]
try:
result = subprocess.run(cmd, capture_output=True, text=True)
if result.returncode != 0:
return f"Error:\n{result.stderr}"
return f"Prediction Output:\n{result.stdout}"
except Exception as e:
return f"Execution Failed: {str(e)}"
# Gradio UI setup
with gr.Blocks() as demo:
gr.Markdown("## 🧪 Molecular Property Prediction using Chemprop and Transformers")
model_version = gr.Radio(
["Vanilla Chemprop", "Updated Fusion Model"],
label="Select Model Version"
)
dataset_name = gr.Radio(["BBBP", "ClinTox"], label="Select Dataset")
input_type = gr.Radio(["Upload CSV", "Single SMILES"], label="Input Type")
file_input = gr.File(file_types=[".csv"], label="Upload CSV", visible=True)
smiles_input = gr.Textbox(label="Enter SMILES string", visible=False)
def toggle_inputs(choice):
return {
file_input: gr.update(visible=(choice == "Upload CSV")),
smiles_input: gr.update(visible=(choice == "Single SMILES"))
}
input_type.change(toggle_inputs, input_type, [file_input, smiles_input])
predict_button = gr.Button("Predict")
output = gr.Textbox(label="Output")
predict_button.click(
fn=predict,
inputs=[model_version, dataset_name, input_type, file_input, smiles_input],
outputs=output
)
demo.launch()
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