milestone one and two AI BRIAN AND MULTILINGUA commit

Dockerfile

@@ -1,4 +1,4 @@
-
+# Base Image
 FROM python:3.10-slim
 
 
@@ -9,7 +9,7 @@ ENV DEBIAN_FRONTEND=noninteractive \
 
 WORKDIR /code
 
-# Install system dependencies
+# System Dependencies
 RUN apt-get update && apt-get install -y --no-install-recommends \
     build-essential \
     git \
@@ -20,38 +20,37 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 
 
 COPY requirements.txt .
-
-
 RUN pip install --no-cache-dir -r requirements.txt
 
-
+# Hugging Face + model tools
 RUN pip install --no-cache-dir huggingface-hub sentencepiece accelerate fasttext
 
+# Hugging Face cache environment
+ENV HF_HOME=/models/huggingface \
+    TRANSFORMERS_CACHE=/models/huggingface \
+    HUGGINGFACE_HUB_CACHE=/models/huggingface \
+    HF_HUB_CACHE=/models/huggingface
 
-ENV HF_HOME=/models/huggingface
-ENV TRANSFORMERS_CACHE=/models/huggingface
-ENV HUGGINGFACE_HUB_CACHE=/models/huggingface
-ENV HF_HUB_CACHE=/models/huggingface
+# Created cache dir and set permissions
+RUN mkdir -p /models/huggingface && chmod -R 777 /models/huggingface
 
-# Create cache dir
-RUN mkdir -p /models/huggingface
-
-# Pre-download models at build time (Qwen + SentenceTransformer + FastText + NLLB finetuned)
+# Pre-download models at build time
 RUN python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='Qwen/Qwen3-4B-Instruct-2507')" \
  && python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')" \
  && python -c "from huggingface_hub import hf_hub_download; hf_hub_download(repo_id='facebook/fasttext-language-identification', filename='model.bin')" \
- && python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='drrobot9/nllb-ig-yo-ha-finetuned')"
+ && python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='drrobot9/nllb-ig-yo-ha-finetuned')" \
+ && find /models/huggingface -name '*.lock' -delete
 
 # Preload tokenizers (avoid runtime delays)
 RUN python -c "from transformers import AutoTokenizer; AutoTokenizer.from_pretrained('Qwen/Qwen3-4B-Instruct-2507', use_fast=True)" \
  && python -c "from transformers import AutoTokenizer; AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2', use_fast=True)" \
  && python -c "from transformers import AutoTokenizer; AutoTokenizer.from_pretrained('drrobot9/nllb-ig-yo-ha-finetuned', use_fast=True)"
 
-
+# Copy project files
 COPY . .
 
-
+# Expose FastAPI port
 EXPOSE 7860
 
-# Run FastAPI app with uvicorn (2 workers for better concurrency)
-CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860", "--workers", "2"]
+# Run FastAPI app with uvicorn (1 workers for concurrency)
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860", "--workers", "1"]