Update Dockerfile

Dockerfile

@@ -1,29 +1,37 @@
-# Use lightweight Python base image
+# Use minimal Python base image
 FROM python:3.9-slim
 
-# Setup environment for Hugging Face
+# Set Hugging Face home for caching
 ENV HF_HOME=/app/.cache/huggingface
-RUN mkdir -p $HF_HOME && chmod -R 777 $HF_HOME
 
-# Allow passing Hugging Face token securely
-ARG HF_TOKEN
-ENV HF_TOKEN=${HF_TOKEN}
+# Ensure permissions for cache directory
+RUN mkdir -p $HF_HOME && chmod -R 777 $HF_HOME
 
+# Set working directory
 WORKDIR /app
 
-# Install dependencies
+# Copy requirements and install dependencies
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 
-# Copy app source
+# Copy all project files
 COPY . .
 
-# ✅ Authenticate and pre-download the gated model with token during build
-RUN python3 -c "from transformers import pipeline; pipeline('summarization', model='mistralai/Mistral-7B-Instruct-v0.1', tokenizer='mistralai/Mistral-7B-Instruct-v0.1', use_auth_token='${HF_TOKEN}')" && \
-    python3 -c "from transformers import pipeline; pipeline('sentiment-analysis', model='distilbert-base-uncased-finetuned-sst-2-english')"
+# Preload the gated model using HF_TOKEN (already injected in Spaces)
+RUN python3 -c "\
+import os; \
+from transformers import pipeline; \
+pipe = pipeline('summarization', model='mistralai/Mistral-7B-Instruct-v0.1', \
+tokenizer='mistralai/Mistral-7B-Instruct-v0.1', \
+use_auth_token=os.environ['HF_TOKEN'])"
+
+# Also preload another model (not gated)
+RUN python3 -c "\
+from transformers import pipeline; \
+pipeline('sentiment-analysis', model='distilbert-base-uncased-finetuned-sst-2-english')"
 
 # Expose Streamlit default port
 EXPOSE 7860
 
-# Run the app
+# Launch Streamlit app
 CMD ["streamlit", "run", "src/streamlit_app.py", "--server.port=7860", "--server.address=0.0.0.0"]