Create app.py

app.py

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+#@title Lauch the Interface
+#@markdown Run to launch the interface.
+#@markdown 1. Enter the protein sequence.
+#@markdown 2. Specify the start and end index (inclusive) of the domain for which you would like to predict mutations (note that indexing starts at 1).
+#@markdown 3. Select the number of tokens you would like the model to predict for each position.
+#@markdown 4. Click 'Submit'.
+#@markdown 5. Click 'Download Outputs' to download the zip file.
+
+def process_sequence(sequence, domain_bounds, n):
+    start_index = int(domain_bounds['start'][0]) - 1
+    end_index = int(domain_bounds['end'][0])
+
+    top_n_mutations = {}
+    all_logits = []
+
+    for i in range(len(sequence)):
+          if start_index <= i <= (end_index - 1):
+              masked_seq = sequence[:i] + '<mask>' + sequence[i+1:]
+              inputs = tokenizer(masked_seq, return_tensors="pt", padding=True, truncation=True, max_length=2000)
+              inputs = {k: v.to(device) for k, v in inputs.items()}
+              with torch.no_grad():
+                  logits = model(**inputs).logits
+              mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1]
+              mask_token_logits = logits[0, mask_token_index, :]
+
+              # Define amino acid tokens
+              AAs_tokens = ['L', 'A', 'G', 'V', 'S', 'E', 'R', 'T', 'I', 'D', 'P', 'K', 'Q', 'N', 'F', 'Y', 'M', 'H', 'W', 'C']
+              all_tokens_logits = mask_token_logits.squeeze(0)
+              top_tokens_indices = torch.argsort(all_tokens_logits, dim=0, descending=True)
+              top_tokens_logits = all_tokens_logits[top_tokens_indices]
+              mutation = []
+              # make sure we don't include non-AA tokens
+              for token_index in top_tokens_indices:
+                  decoded_token = tokenizer.decode([token_index.item()])
+                  if decoded_token in AAs_tokens:
+                      mutation.append(decoded_token)
+                      if len(mutation) == n:
+                          break
+              top_n_mutations[(sequence[i], i)] = mutation
+
+              # collecting logits for the heatmap
+              logits_array = mask_token_logits.cpu().numpy()
+              # filter out non-amino acid tokens
+              filtered_indices = list(range(4, 23 + 1))
+              filtered_logits = logits_array[:, filtered_indices]
+              all_logits.append(filtered_logits)
+
+
+    token_indices = torch.arange(logits.size(-1))
+    tokens = [tokenizer.decode([idx]) for idx in token_indices]
+    filtered_tokens = [tokens[i] for i in filtered_indices]
+
+    all_logits_array = np.vstack(all_logits)
+    normalized_logits_array = F.softmax(torch.tensor(all_logits_array), dim=-1).numpy()
+    transposed_logits_array = normalized_logits_array.T
+
+   # Plotting the heatmap
+    x_tick_positions = np.arange(start_index, end_index, 10)
+    x_tick_labels = [str(pos + 1) for pos in x_tick_positions]
+
+    plt.figure(figsize=(15, 8))
+    plt.rcParams.update({'font.size': 18})
+
+    sns.heatmap(transposed_logits_array, cmap='plasma', xticklabels=x_tick_labels, yticklabels=filtered_tokens)
+    plt.title('Token Probability Heatmap')
+    plt.ylabel('Token')
+    plt.xlabel('Residue Index')
+    plt.yticks(rotation=0)
+    plt.xticks(x_tick_positions - start_index + 0.5, x_tick_labels, rotation=0)
+
+    # Save the figure to a BytesIO object
+    buf = BytesIO()
+    plt.savefig(buf, format='png', dpi = 300)
+    buf.seek(0)
+    plt.close()
+
+    # Convert BytesIO object to an image
+    img = Image.open(buf)
+
+    original_residues = []
+    mutations = []
+    positions = []
+
+    for key, value in top_n_mutations.items():
+        original_residue, position = key
+        original_residues.append(original_residue)
+        mutations.append(value)
+        positions.append(position + 1)
+
+    df = pd.DataFrame({
+        'Original Residue': original_residues,
+        'Predicted Residues': mutations,
+        'Position': positions
+    })
+    df.to_csv("predicted_tokens.csv", index=False)
+    img.save("heatmap.png", dpi = 300)
+    zip_path = "outputs.zip"
+    with zipfile.ZipFile(zip_path, 'w') as zipf:
+        zipf.write("predicted_tokens.csv")
+        zipf.write("heatmap.png")
+
+
+    return df, img, zip_path
+
+demo = gr.Interface(
+    fn=process_sequence,
+    inputs=[
+        gr.Textbox(label="Sequence", placeholder="Enter the protein sequence here"),
+        gr.Dataframe(
+            headers=["start", "end"],
+            datatype=["number", "number"],
+            row_count=(1, "fixed"),
+            col_count=(2, "fixed"),
+            label="Domain Bounds"
+        ),
+        gr.Dropdown([i for i in range(1, 21)], label="Top N Tokens"),
+    ],
+     outputs=[
+        gr.Dataframe(label="Predicted Tokens (in order of decreasing likelihood)"),
+        gr.Image(type="pil", label="Heatmap"),
+        gr.File(label="Download Outputs"),
+    ],
+)
+if __name__ == "__main__":
+    with suppress_output():
+        demo.launch(show_error=False, debug=False)