Create index directory

Dockerfile

@@ -1,29 +1,26 @@
 # Use official lightweight Python image
 FROM python:3.10-slim
 
-RUN useradd -m -u 1000 user
-USER user
-
 # Set environment variables to disable usage stats collection (to prevent write errors)
 ENV STREAMLIT_BROWSER_GATHERUSAGESTATS=false
 ENV STREAMLIT_DISABLE_WATCHDOG_WARNINGS=true
 ENV STREAMLIT_SERVER_HEADLESS=true
 ENV STREAMLIT_SERVER_PORT=7860
 ENV STREAMLIT_SERVER_ADDRESS=0.0.0.0
-ENV HOME=/home/user
-ENV PATH="$PATH:/home/user/.local/bin"
+ENV HOME=/tmp
 
 # Set working directory
-WORKDIR /home/user/app
+WORKDIR /app
+
+# Create directory to store index with correct permissions
+RUN mkdir -p /app/index && chmod -R 777 /app/index
 
 # Copy requirements and install
-COPY --chown=user ./requirements.txt requirements.txt
-RUN pip install --no-cache-dir --upgrade -r requirements.txt
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
 
 # Copy the rest of the code
-COPY --chown=user . /home/user/app
-
-USER user
+COPY . .
 
 # Run the app
-CMD ["streamlit", "run", "streamlit_app.py", "--server.port=7860", "--server.address=0.0.0.0"]
+CMD ["streamlit", "run", "streamlit_app.py", "--server.port=7860", "--server.address=0.0.0.0"]

streamlit_app.py

@@ -76,7 +76,7 @@ def get_text_chunks(text):
 def get_vector_store(text_chunks):
     embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
     vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
-    vector_store.save_local("faiss_index")
+    vector_store.save_local("/app/index/faiss_index")
 
 # ========================
 # 5️⃣ Conversational Chain Setup
@@ -100,7 +100,7 @@ def get_conversational_chain():
 
 def user_input(user_question):
     embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+    new_db = FAISS.load_local("/app/index/faiss_index", embeddings, allow_dangerous_deserialization=True)
     docs = new_db.similarity_search(user_question)