test

Dockerfile

@@ -33,27 +33,8 @@ RUN pip install --no-cache-dir -r requirements.txt
 # Create a directory for pre-downloaded models
 RUN mkdir -p /app/models
 
-# Pre-download models using a Python script during build
-RUN python -c "\
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, AutoProcessor, AutoModel; \
-from transformers import Gemma3ForConditionalGeneration; \
-import os; \
-models = { \
-    'llm_model': ('google/gemma-3-4b-it', Gemma3ForConditionalGeneration, AutoProcessor), \
-    'tts_model': ('ai4bharat/IndicF5', AutoModel, None), \
-    'asr_model': ('ai4bharat/indic-conformer-600m-multilingual', AutoModel, None), \
-    'trans_en_indic': ('ai4bharat/indictrans2-en-indic-dist-200M', AutoModelForSeq2SeqLM, AutoTokenizer), \
-    'trans_indic_en': ('ai4bharat/indictrans2-indic-en-dist-200M', AutoModelForSeq2SeqLM, AutoTokenizer), \
-    'trans_indic_indic': ('ai4bharat/indictrans2-indic-indic-dist-320M', AutoModelForSeq2SeqLM, AutoTokenizer), \
-}; \
-for name, (model_name, model_class, processor_class) in models.items(): \
-    print(f'Downloading {model_name}...'); \
-    model = model_class.from_pretrained(model_name, trust_remote_code=True); \
-    model.save_pretrained(f'/app/models/{name}'); \
-    if processor_class: \
-        processor = processor_class.from_pretrained(model_name, trust_remote_code=True); \
-        processor.save_pretrained(f'/app/models/{name}'); \
-"
+# Pre-download models using a single-line Python script
+RUN python -c "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, AutoProcessor, AutoModel; from transformers import Gemma3ForConditionalGeneration; import os; models = {'llm_model': ('google/gemma-3-4b-it', Gemma3ForConditionalGeneration, AutoProcessor), 'tts_model': ('ai4bharat/IndicF5', AutoModel, None), 'asr_model': ('ai4bharat/indic-conformer-600m-multilingual', AutoModel, None), 'trans_en_indic': ('ai4bharat/indictrans2-en-indic-dist-200M', AutoModelForSeq2SeqLM, AutoTokenizer), 'trans_indic_en': ('ai4bharat/indictrans2-indic-en-dist-200M', AutoModelForSeq2SeqLM, AutoTokenizer), 'trans_indic_indic': ('ai4bharat/indictrans2-indic-indic-dist-320M', AutoModelForSeq2SeqLM, AutoTokenizer)}; for name, (model_name, model_class, processor_class) in models.items(): print(f'Downloading {model_name}...'); model = model_class.from_pretrained(model_name, trust_remote_code=True); model.save_pretrained(f'/app/models/{name}'); if processor_class: processor = processor_class.from_pretrained(model_name, trust_remote_code=True); processor.save_pretrained(f'/app/models/{name}');"
 
 # Copy application code
 COPY . .