pdate src/app.py to prepend the sqlite:/// prefix to the database path.

Dockerfile

@@ -1,35 +1,39 @@
-# Use the official Python minimal image (Best for CPU Basic tier)
+# Use the official Python minimal image
 FROM python:3.10-slim
 
 # 1. Install system tools
-#    Added 'zstd' which is now REQUIRED by the Ollama installer
+#    CRITICAL: Added 'git-lfs' so we download the REAL database files, not pointers.
+#    Added 'zstd' for Ollama.
 RUN apt-get update && apt-get install -y \
     curl \
     git \
+    git-lfs \
     build-essential \
     zstd \
-    && rm -rf /var/lib/apt/lists/*
+    && rm -rf /var/lib/apt/lists/* \
+    && git lfs install  # <--- Initialize LFS
 
-# 2. Install Ollama (Must be done as ROOT)
+# 2. Install Ollama (Root)
 RUN curl -fsSL https://ollama.com/install.sh | sh
 
-# 3. Setup User (Hugging Face Requirement)
+# 3. Setup User
 RUN useradd -m -u 1000 user
 USER user
 ENV HOME=/home/user \
     PATH=/home/user/.local/bin:$PATH
 
-# 4. Set Working Directory
+# 4. Workdir
 WORKDIR $HOME/app
 
-# 5. Install Python Requirements
+# 5. Requirements
 COPY --chown=user requirements.txt $HOME/app/
 RUN pip install --no-cache-dir -r requirements.txt
 
-# 6. Copy Application Code
+# 6. Copy Code
 COPY --chown=user . $HOME/app
 
-# 7. STARTUP COMMAND
+# 7. Startup
+#    We clone the dataset. git-lfs ensures we get the big files.
 CMD git clone https://huggingface.co/datasets/teofizzy/mshauri-data data_download && \
     mv data_download/mshauri_fedha_v6.db . && \
     mv data_download/mshauri_fedha_chroma_db . && \

src/app.py

@@ -6,13 +6,11 @@ import streamlit as st
 import os
 
 # --- PATH SETUP ---
-# Since we run this from the root via Docker, we need to help python find 'mshauri_demo'
-# We add 'src/load' to the system path.
 current_dir = os.getcwd() # Should be /home/user/app in Docker
 load_dir = os.path.join(current_dir, "src", "load")
 sys.path.append(load_dir)
 
-# Import agent creator
+# Import your agent creator
 try:
     from mshauri_demo import create_mshauri_agent
 except ImportError as e:
@@ -30,13 +28,16 @@ if "messages" not in st.session_state:
 
 if "agent" not in st.session_state:
     with st.spinner("Initializing Mshauri Brain (Loading Models & Data)..."):
-        # The Dockerfile downloads the data to the root folder (/home/user/app)
-        sql_path = os.path.join(current_dir, "mshauri_fedha_v6.db")
+        # --- THE FIX IS HERE ---
+        # SQLAlchemy requires a URI starting with sqlite:///
+        # We use 4 slashes (sqlite:////) because it is an absolute path on Linux
+        sql_path = f"sqlite:///{os.path.join(current_dir, 'mshauri_fedha_v6.db')}"
         vector_path = os.path.join(current_dir, "mshauri_fedha_chroma_db")
         
         # Check if data exists (Debugging for Space deployment)
-        if not os.path.exists(sql_path):
-            st.error(f"❌ Database not found at {sql_path}. Did the clone fail?")
+        real_db_path = os.path.join(current_dir, "mshauri_fedha_v6.db")
+        if not os.path.exists(real_db_path):
+            st.error(f"❌ Database not found at {real_db_path}. Did the clone fail?")
             st.stop()
             
         try:
@@ -44,7 +45,7 @@ if "agent" not in st.session_state:
             st.session_state.agent = create_mshauri_agent(
                 sql_db_path=sql_path, 
                 vector_db_path=vector_path,
-                llm_model="qwen2.5:7b" # Force 2.5 7 B which can use CPU basic
+                llm_model="qwen2.5:7b"
             )
             st.success("System Ready!")
         except Exception as e:
@@ -64,9 +65,12 @@ if prompt := st.chat_input("Ask about inflation, exchange rates, or economic tre
     with st.chat_message("assistant"):
         with st.spinner("Analyzing..."):
             try:
-                response = st.session_state.agent.invoke({"input": prompt})
-                output_text = response.get("output", "Error generating response.")
-                st.markdown(output_text)
-                st.session_state.messages.append({"role": "assistant", "content": output_text})
+                if st.session_state.agent:
+                    response = st.session_state.agent.invoke({"input": prompt})
+                    output_text = response.get("output", "Error generating response.")
+                    st.markdown(output_text)
+                    st.session_state.messages.append({"role": "assistant", "content": output_text})
+                else:
+                    st.error("Agent failed to initialize. Please refresh the page.")
             except Exception as e:
                 st.error(f"An error occurred: {e}")