Update app.py

app.py

@@ -6,9 +6,13 @@ from context_graph import ContextGraph
 from graph_view import visualize_reasoned_graph
 from memory_store import MemoryStore
 
-# === Setup ===
-os.environ["TRANSFORMERS_CACHE"] = "/home/user/.cache"
-MEMORY_PATH = "/home/user/app/memory.json"
+# === Lightweight Setup ===
+# Redirects model cache to ephemeral folder (won’t bloat storage)
+os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf_cache"
+os.environ["HF_HOME"] = "/tmp/hf_home"
+
+# Memory stored in /tmp so it resets safely if needed
+MEMORY_PATH = "/tmp/memory.json"
 
 # === Initialize Core Systems ===
 agent = AgentCore()
@@ -18,27 +22,29 @@ memory = MemoryStore(path=MEMORY_PATH)
 
 # === Core Chat Function ===
 def process_input(user_input, history=[]):
-    """Main conversational and memory loop."""
-    # Step 1: Embed input
+    """Aventra OS conversation loop with memory and reasoning."""
+    # 1️⃣ Embed user input
     vector = agent.embed_text(user_input)
-    
-    # Step 2: Save to memory
+
+    # 2️⃣ Add memory safely
     memory.add_memory(user_input, vector)
 
-    # Step 3: Reasoning
+    # 3️⃣ Reason on new input
     reasoning = reasoner.reason(user_input)
     response = f"[Aventra Response]: {reasoning}"
 
-    # Step 4: Update history
+    # 4️⃣ Append to chat history
     history.append((user_input, response))
     return history, history
 
-# === Visualize Memory Function ===
+# === Visualize Memory ===
 def visualize_memory():
-    """Render memory graph visualization inline."""
+    """Inline visualization for memory graph."""
     memories = memory.memories
-    relationships = []
+    if not memories:
+        return gr.HTML.update(value="<b>No memory data yet — start chatting first.</b>")
 
+    relationships = []
     for i, (text1, vec1) in enumerate(memories):
         for j, (text2, vec2) in enumerate(memories):
             if i != j:
@@ -46,11 +52,10 @@ def visualize_memory():
                 if similarity > 0.35:
                     relationships.append((text1, text2, float(similarity)))
 
-    # Generate visualization
+    # Generate and show visualization
     output_html = visualize_reasoned_graph(memories, relationships)
     print(f"✅ Visualization created at: {output_html}")
 
-    # Return graph inline
     if os.path.exists(output_html):
         with open(output_html, "r") as f:
             html_content = f.read()
@@ -65,11 +70,12 @@ def clear_memory():
     return "🧹 Memory cleared successfully."
 
 # === Build Gradio Interface ===
-with gr.Blocks(title="Aventra OS") as demo:
-    gr.Markdown("## 🧠 **Aventra OS — Context Graph Memory Engine**")
+with gr.Blocks(title="Aventra OS - Lightweight Mode") as demo:
+    gr.Markdown("## 🧠 **Aventra OS — Memory Engine (Light Mode)**")
+
     chatbot = gr.Chatbot(label="Aventra Chat")
     msg = gr.Textbox(label="Type a message")
-    send_btn = gr.Button("Send")
+    send_btn = gr.Button("Send 🚀")
     visualize_btn = gr.Button("Visualize Memory 🕸️")
     clear_btn = gr.Button("Clear Memory 🧹")
     graph_html = gr.HTML(label="Memory Graph")