Fix Zero GPU compatibility and add conditional support

- Add conditional import for spaces module
- Separate implementation function from decorated function
- Apply Zero GPU decorator only when spaces is available
- Add models list to README.md for HF Spaces
- This allows the app to run both with and without GPU support

README.md

@@ -8,6 +8,10 @@ sdk_version: 5.34.2
 app_file: app.py
 pinned: false
 license: mit
+models:
+  - PascalNotin/Tranception_Small
+  - PascalNotin/Tranception_Medium
+  - PascalNotin/Tranception_Large
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -17,7 +17,14 @@ import zipfile
 import shutil
 import uuid
 import gc
-import spaces
+
+# Try to import spaces for Zero GPU support
+try:
+    import spaces
+    SPACES_AVAILABLE = True
+except ImportError:
+    SPACES_AVAILABLE = False
+    print("Warning: spaces module not available. Running without Zero GPU support.")
 
 # Add current directory to path
 sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
@@ -225,8 +232,7 @@ def get_mutated_protein(sequence,mutant):
   mutated_sequence[int(mutant[1:-1])-1]=mutant[-1]
   return ''.join(mutated_sequence)
 
-@spaces.GPU(duration=300)  # Request GPU for up to 5 minutes
-def score_and_create_matrix_all_singles(sequence,mutation_range_start=None,mutation_range_end=None,model_type="Large",scoring_mirror=False,batch_size_inference=20,max_number_positions_per_heatmap=50,num_workers=0,AA_vocab=AA_vocab):
+def score_and_create_matrix_all_singles_impl(sequence,mutation_range_start=None,mutation_range_end=None,model_type="Large",scoring_mirror=False,batch_size_inference=20,max_number_positions_per_heatmap=50,num_workers=0,AA_vocab=AA_vocab):
   # Clean up old files periodically
   cleanup_old_files()
   
@@ -328,6 +334,12 @@ def score_and_create_matrix_all_singles(sequence,mutation_range_start=None,mutat
       if torch.cuda.is_available():
         torch.cuda.empty_cache()
 
+# Apply Zero GPU decorator if available
+if SPACES_AVAILABLE:
+    score_and_create_matrix_all_singles = spaces.GPU(duration=300)(score_and_create_matrix_all_singles_impl)
+else:
+    score_and_create_matrix_all_singles = score_and_create_matrix_all_singles_impl
+
 def extract_sequence(protein_id, taxon, sequence):
   return sequence