Create app.py

app.py

@@ -0,0 +1,52 @@
+#-------------------------------------------------------libraries------------------------------------------------------------------------------------
+import torch
+import gradio as gr
+import matplotlib.pyplot as plt
+from transformers import AutoTokenizer, EsmModel
+from sklearn.decomposition import PCA
+
+#----------------------------------------------------Analysis------------------------------------------------------------------------------------
+#--load model and tokenizer
+model = EsmModel.from_pretrained("facebook/esm1b_t33_650M_UR50S", output_hidden_states=True)
+tokenizer = AutoTokenizer.from_pretrained("facebook/esm1b_t33_650M_UR50S")
+
+#--task to execute
+def extract_and_plot(seq, layer=-1):
+    #--preprocess sequence
+    inputs = tokenizer(seq, return_tensors="pt")
+
+    #--forward pass
+    with torch.no_grad():
+        outputs = model(**inputs)
+        hidden_states = outputs.hidden_states   #--> tuple: (layer0, ..., layer_final)
+
+    #--select hidden state from specified layer
+    if layer == 1:
+        embedding = hidden_states[-1][0]    #--> (seq_len, hidden_dim)
+    else:
+        embedding = hidden_states[layer][0]
+
+    #--PCA
+    pca = PCA(n_components=2)
+    coords = pca.fit_transform(embedding.numpy())
+
+    #--plot
+    plt.figure(figsize=(6, 4))
+    plt.scatter(coords[:, 0], coords[:, 1])
+    plt.title(f"PCA of esm1b embeddings (layer {layer})")
+    plt.xlabel("PCA1")
+    plt.ylabel("PCA2")
+    plt.tight_layout()
+
+    return plt
+
+demo = gr.Interface(
+    fn=extract_and_plot,
+    inputs=[
+        gr.Textbox(label="Protein Sequence"),
+        gr.Slider(minimum=0, maximum=33, step=1, value=33, label="Layer (-1 = final)")
+    ],
+    outputs=gr.Plot()
+)
+
+demo.launch()