Update app.py

app.py

@@ -1,91 +1,91 @@
-import base64
-from flask import Flask, render_template, request, jsonify, Response, stream_with_context
-from visumem_core import SectorMemory
-import ai_engine
-import json
-
-app = Flask(__name__)
-
-mem_history = SectorMemory("history")
-
-
-@app.route('/')
-def index():
-    return render_template('index.html')
-
-@app.route('/visualize/<mem_type>')
-def visualize(mem_type):
-    target = mem_history
-    
-    # 1. Generate Image
-    img_bytes = target.to_image_bytes()
-    b64 = base64.b64encode(img_bytes).decode('utf-8')
-    
-    # 2. Get Stats (Updated for RGB Core Compatibility)
-    # The RGB core uses .count for items and .cursor_green for text size
-    # It does not use .slot_cursor or .heap_cursor anymore.
-    active_count = target.count
-    text_usage = target.cursor_green
-    
-    return jsonify({
-        "image": f"data:image/png;base64,{b64}",
-        "stats": f"{active_count} Active Entries",
-        "usage": f"{text_usage} Bytes Text"
-    })
-
-@app.route('/inspect/<mem_type>')
-def inspect(mem_type):
-    content = mem_history.dump_heap_content()
-    return jsonify({"content": content})
-
-@app.route('/chat', methods=['POST'])
-def chat():
-    user_msg = request.json.get('message')
-    if not user_msg: return jsonify({"error": "Empty"}), 400
-
-    def generate():
-        # 1. VECTORIZE USER INPUT
-        q_vec = ai_engine.get_embedding(user_msg)
-        
-        # B. Relevant Past History
-        hist_hits = mem_history.search(q_vec, top_k=2)
-        long_term_txt = "\n".join([f"[Memory]: {h['text']}" for h in hist_hits if h['score'] > 0.4])
-        
-        # C. Recent Conversation
-        recent_msgs = mem_history.get_recent_entries(n=4)
-        recent_txt = "\n".join(recent_msgs)
-        
-        # 3. BUILD PROMPT
-        system_prompt = f"""You are a helpful AI Assistant with an evolving rule system and knowledge base.
-
-### RELEVANT MEMORIES (Context from past):
-{long_term_txt}
-
-### CURRENT CONVERSATION:
-{recent_txt}
-"""
-        
-        messages = [
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": user_msg}
-        ]
-
-        # 4. STREAM GENERATION
-        full_response = ""
-        for chunk in ai_engine.chat_stream(messages):
-            full_response += chunk
-            yield chunk
-        
-        # 5. BACKGROUND: WRITE & REFLECT
-        log_entry = f"User: {user_msg}\nAI: {full_response}"
-        mem_history.write_entry(log_entry, q_vec)
-        
-    return Response(stream_with_context(generate()), mimetype='text/plain')
-
-@app.route('/wipe', methods=['POST'])
-def wipe():
-    mem_history.wipe()
-    return jsonify({"success":True})
-
-if __name__ == '__main__':
+import base64
+from flask import Flask, render_template, request, jsonify, Response, stream_with_context
+from vismem import SectorMemory
+import ai_engine
+import json
+
+app = Flask(__name__)
+
+mem_history = SectorMemory("history")
+
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/visualize/<mem_type>')
+def visualize(mem_type):
+    target = mem_history
+    
+    # 1. Generate Image
+    img_bytes = target.to_image_bytes()
+    b64 = base64.b64encode(img_bytes).decode('utf-8')
+    
+    # 2. Get Stats (Updated for RGB Core Compatibility)
+    # The RGB core uses .count for items and .cursor_green for text size
+    # It does not use .slot_cursor or .heap_cursor anymore.
+    active_count = target.count
+    text_usage = target.cursor_green
+    
+    return jsonify({
+        "image": f"data:image/png;base64,{b64}",
+        "stats": f"{active_count} Active Entries",
+        "usage": f"{text_usage} Bytes Text"
+    })
+
+@app.route('/inspect/<mem_type>')
+def inspect(mem_type):
+    content = mem_history.dump_heap_content()
+    return jsonify({"content": content})
+
+@app.route('/chat', methods=['POST'])
+def chat():
+    user_msg = request.json.get('message')
+    if not user_msg: return jsonify({"error": "Empty"}), 400
+
+    def generate():
+        # 1. VECTORIZE USER INPUT
+        q_vec = ai_engine.get_embedding(user_msg)
+        
+        # B. Relevant Past History
+        hist_hits = mem_history.search(q_vec, top_k=2)
+        long_term_txt = "\n".join([f"[Memory]: {h['text']}" for h in hist_hits if h['score'] > 0.4])
+        
+        # C. Recent Conversation
+        recent_msgs = mem_history.get_recent_entries(n=4)
+        recent_txt = "\n".join(recent_msgs)
+        
+        # 3. BUILD PROMPT
+        system_prompt = f"""You are a helpful AI Assistant with an evolving rule system and knowledge base.
+
+### RELEVANT MEMORIES (Context from past):
+{long_term_txt}
+
+### CURRENT CONVERSATION:
+{recent_txt}
+"""
+        
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_msg}
+        ]
+
+        # 4. STREAM GENERATION
+        full_response = ""
+        for chunk in ai_engine.chat_stream(messages):
+            full_response += chunk
+            yield chunk
+        
+        # 5. BACKGROUND: WRITE & REFLECT
+        log_entry = f"User: {user_msg}\nAI: {full_response}"
+        mem_history.write_entry(log_entry, q_vec)
+        
+    return Response(stream_with_context(generate()), mimetype='text/plain')
+
+@app.route('/wipe', methods=['POST'])
+def wipe():
+    mem_history.wipe()
+    return jsonify({"success":True})
+
+if __name__ == '__main__':
     app.run(debug=True, port=5000)