Update app.py

app.py

@@ -1,53 +1,53 @@
-import os
-import torch
-import torch.nn as nn
-import pandas as pd
-import torch.nn.functional as F
-from lavis.models.protein_models.protein_function_opt import Blip2ProteinMistral
-from lavis.models.base_model import FAPMConfig
-import spaces
-import gradio as gr
-
-
-# Load the model
-model = Blip2ProteinMistral(config=FAPMConfig(), esm_size='3b')
-model.load_checkpoint("model/checkpoint_mf2.pth")
-model.to('cuda')
-
-
-@spaces.GPU
-def generate_caption(protein, prompt):
-    # Process the image and the prompt
-    with open('data/fasta/example.fasta', 'w') as f:
-        f.write('>{}\n'.format("protein_name"))
-        f.write('{}\n'.format(protein.strip()))
-    os.system("python esm_scripts/extract.py esm2_t36_3B_UR50D data/fasta/example.fasta data/emb_esm2_3b --repr_layers 36 --truncation_seq_length 1024 --include per_tok")
-    esm_emb = torch.load("data/emb_esm2_3b/protein_name.pt")['representations'][36]
-    esm_emb = F.pad(esm_emb.t(), (0, 1024 - len(esm_emb))).t().to('cuda')
-    samples = {'name': ['test_protein'],
-               'image': torch.unsqueeze(esm_emb, dim=0),
-               'text_input': ['none'],
-               'prompt': [prompt]}
-    # Generate the output
-    prediction = model.generate(samples, length_penalty=0., num_beams=15, num_captions=10, temperature=1., repetition_penalty=1.0)
-
-    return prediction
-
-# Define the FAPM interface
-description = """Quick demonstration of the FAPM model for protein function prediction. Upload an protein sequence to generate a function description. Modify the Prompt to provide the taxonomy information.
-
-The model used in this app is available at [Hugging Face Model Hub](https://huggingface.co/wenkai/FAPM) and the source code can be found on [GitHub](https://github.com/xiangwenkai/FAPM/tree/main)."""
-
-iface = gr.Interface(
-    fn=generate_caption,
-    inputs=[gr.Textbox(type="pil", label="Upload sequence"), gr.Textbox(label="Prompt", value="taxonomy prompt")],
-    outputs=gr.Textbox(label="Generated description"),
-    description=description
-)
-
-# Launch the interface
-iface.launch()
-
-
-
-
+import os
+import torch
+import torch.nn as nn
+import pandas as pd
+import torch.nn.functional as F
+from lavis.models.protein_models.protein_function_opt import Blip2ProteinMistral
+from lavis.models.base_model import FAPMConfig
+import spaces
+import gradio as gr
+
+
+# Load the model
+model = Blip2ProteinMistral(config=FAPMConfig(), esm_size='3b')
+model.load_checkpoint("model/checkpoint_mf2.pth")
+model.to('cuda')
+
+
+@spaces.GPU
+def generate_caption(protein, prompt):
+    # Process the image and the prompt
+    with open('data/fasta/example.fasta', 'w') as f:
+        f.write('>{}\n'.format("protein_name"))
+        f.write('{}\n'.format(protein.strip()))
+    os.system("python esm_scripts/extract.py esm2_t36_3B_UR50D data/fasta/example.fasta data/emb_esm2_3b --repr_layers 36 --truncation_seq_length 1024 --include per_tok")
+    esm_emb = torch.load("data/emb_esm2_3b/protein_name.pt")['representations'][36]
+    esm_emb = F.pad(esm_emb.t(), (0, 1024 - len(esm_emb))).t().to('cuda')
+    samples = {'name': ['test_protein'],
+               'image': torch.unsqueeze(esm_emb, dim=0),
+               'text_input': ['none'],
+               'prompt': [prompt]}
+    # Generate the output
+    prediction = model.generate(samples, length_penalty=0., num_beams=15, num_captions=10, temperature=1., repetition_penalty=1.0)
+
+    return prediction
+
+# Define the FAPM interface
+description = """Quick demonstration of the FAPM model for protein function prediction. Upload an protein sequence to generate a function description. Modify the Prompt to provide the taxonomy information.
+
+The model used in this app is available at [Hugging Face Model Hub](https://huggingface.co/wenkai/FAPM) and the source code can be found on [GitHub](https://github.com/xiangwenkai/FAPM/tree/main)."""
+
+iface = gr.Interface(
+    fn=generate_caption,
+    inputs=[gr.Textbox(type="text", label="Upload sequence"), gr.Textbox(type="text", label="Prompt")],
+    outputs=gr.Textbox(label="Generated description"),
+    description=description
+)
+
+# Launch the interface
+iface.launch()
+
+
+
+