add files

app.py

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+import gradio as gr
+import random
+
+
+# Placeholder prediction function
+def predict_affinity(smiles, sequence):
+    # In practice, you'd call your ML model here
+    # For now, we just return a random score between 0 and 1
+    score = round(random.uniform(0, 1), 4)
+    return f"Predicted Affinity Score: {score}"
+
+
+with gr.Blocks(title="Molecule-Protein Affinity Predictor") as demo:
+    gr.Markdown("## Molecule–Protein Affinity Prediction")
+    gr.Markdown(
+        "Enter a **Molecule SMILES string** and a **Protein amino acid sequence** "
+        "then click **Predict** to get the affinity score."
+    )
+
+    with gr.Row():
+        smiles_input = gr.Textbox(
+            label="Molecule SMILES",
+            placeholder="e.g. CC(=O)OC1=CC=CC=C1C(=O)O"
+        )
+        sequence_input = gr.Textbox(
+            label="Protein Sequence",
+            placeholder="e.g. MVLSPADKTNVKAA..."
+        )
+
+    predict_button = gr.Button("Predict")
+    output = gr.Textbox(label="Affinity Score", interactive=False)
+
+    predict_button.click(
+        fn=predict_affinity,
+        inputs=[smiles_input, sequence_input],
+        outputs=output
+    )
+
+demo.launch()

requirements.txt

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+gradio