Fix: Add writable cache directory for model downloads

Dockerfile

@@ -9,6 +9,9 @@ RUN apt-get update && apt-get install -y \
     build-essential \
     && rm -rf /var/lib/apt/lists/*
 
+# Create cache directory with proper permissions
+RUN mkdir -p /code/.cache && chmod 777 /code/.cache
+
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 

app.py

@@ -5,37 +5,44 @@ import tempfile
 import os
 from transformers import pipeline
 import logging
+from pathlib import Path
 
 # Set up logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
+# Create cache directory in the workspace
+CACHE_DIR = Path("/code/.cache")
+CACHE_DIR.mkdir(exist_ok=True)
+os.environ["TRANSFORMERS_CACHE"] = str(CACHE_DIR)
+os.environ["HF_HOME"] = str(CACHE_DIR)
+
 app = FastAPI(title="TranscriptoCast AI (Demo)")
 
 # Load models once at startup
 try:
     logger.info("Loading Whisper model...")
-    whisper_model = whisper.load_model("base")
+    whisper_model = whisper.load_model("base", download_root=str(CACHE_DIR))
     logger.info("Whisper model loaded successfully")
 except Exception as e:
     logger.error(f"Error loading Whisper model: {str(e)}")
-    raise HTTPException(status_code=500, detail="Failed to load Whisper model")
+    raise HTTPException(status_code=500, detail=f"Failed to load Whisper model: {str(e)}")
 
 try:
     logger.info("Loading summarization model...")
-    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+    summarizer = pipeline("summarization", model="facebook/bart-large-cnn", cache_dir=str(CACHE_DIR))
     logger.info("Summarization model loaded successfully")
 except Exception as e:
     logger.error(f"Error loading summarization model: {str(e)}")
-    raise HTTPException(status_code=500, detail="Failed to load summarization model")
+    raise HTTPException(status_code=500, detail=f"Failed to load summarization model: {str(e)}")
 
 try:
     logger.info("Loading translation model...")
-    translator = pipeline("translation", model="facebook/mbart-large-50-many-to-many-mmt")
+    translator = pipeline("translation", model="facebook/mbart-large-50-many-to-many-mmt", cache_dir=str(CACHE_DIR))
     logger.info("Translation model loaded successfully")
 except Exception as e:
     logger.error(f"Error loading translation model: {str(e)}")
-    raise HTTPException(status_code=500, detail="Failed to load translation model")
+    raise HTTPException(status_code=500, detail=f"Failed to load translation model: {str(e)}")
 
 @app.post("/transcribe")
 async def transcribe(file: UploadFile = File(...)):