Deploy chatbot from GitHub Actions

Dockerfile

@@ -1,33 +1,13 @@
 # Use Python 3.10 slim base image
 FROM python:3.10-slim
-
-# Set working directory
 WORKDIR /app
-
-# Create a writable cache directory
 RUN mkdir -p /app/.cache && chmod -R 777 /app/.cache
-
-# Set environment variables for Hugging Face cache
 ENV HF_HOME=/app/.cache
-ENV TRANSFORMERS_CACHE=/app/.cache
-
-# Copy requirements.txt
 COPY requirements.txt .
-
-# Install dependencies
 RUN pip install --no-cache-dir -r requirements.txt
-
-# Install gunicorn
 RUN pip install --no-cache-dir gunicorn
-
-# Pre-download the sentence-transformers model
-RUN python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')"
-
-# Copy application files
+RUN python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')" \
+    && chmod -R 777 /app/.cache
 COPY GeminiAgent.py main.py routes.py serialization.py tool.py .
-
-# Expose port 7860 (standard for Hugging Face Spaces)
 EXPOSE 7860
-
-# Run the application
 CMD ["gunicorn", "-w", "4", "-k", "uvicorn.workers.UvicornWorker", "main:app", "--bind", "0.0.0.0:7860"]

tool.py

@@ -8,8 +8,7 @@ from typing import List
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 import os
-os.environ["HF_HOME"] = "/app/.cache" 
-os.environ["TRANSFORMERS_CACHE"] = "/app/.cache"
+os.environ["HF_HOME"] = "/app/.cache"
 
 # Initialize embeddings
 embedmodel = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")