Analysis v2: reads neuron states, network overview, enhanced deep analysis

app.py

@@ -84,6 +84,37 @@ def check_neuron(space_name):
     return "UNREACHABLE"
 
 
+
+def read_all_neurons():
+    """Read state from all neurons in the network."""
+    try:
+        resp = requests.get(
+            f"https://huggingface.co/api/datasets/{BRIDGE_REPO}/tree/main/neurons",
+            headers={"Authorization": f"Bearer {HF_TOKEN}"}, timeout=15
+        )
+        if resp.status_code != 200:
+            return []
+        
+        files = resp.json()
+        neurons = []
+        for f in files:
+            path = f.get("path", "")
+            if not path.endswith(".json") or path.endswith("_index.json"):
+                continue
+            try:
+                data = requests.get(
+                    f"https://huggingface.co/datasets/{BRIDGE_REPO}/resolve/main/{path}",
+                    headers={"Authorization": f"Bearer {HF_TOKEN}"}, timeout=10
+                ).json()
+                neurons.append(data)
+            except: continue
+        
+        log(f"Read {len(neurons)} neuron states from bridge")
+        return neurons
+    except Exception as e:
+        log(f"Neuron read error: {e}")
+        return []
+
 def scan_network():
     """Scan all neurons and the bridge. Build a picture of the network."""
     log("Network scan starting...")
@@ -129,7 +160,30 @@ def deep_analysis():
     practice = read_practice_state()
     
     # Build context for the analyst
+    neurons_data = read_all_neurons()
+    neuron_summary = ""
+    total_sessions = 0
+    total_genuine = 0
+    total_cached = 0
+    for n in neurons_data:
+        s = n.get("stats", {})
+        total_sessions += s.get("sessions", 0)
+        total_genuine += s.get("genuine", s.get("genuine_count", 0))
+        total_cached += s.get("cached", s.get("cached_count", 0))
+        outputs = n.get("recent_outputs", [])
+        if outputs:
+            last = outputs[-1] if isinstance(outputs[-1], str) else outputs[-1].get("preview", outputs[-1].get("final_preview", ""))
+            neuron_summary += f"  {n.get('neuron_id','?')} ({n.get('type','practice')}): {s.get('sessions',0)}s | last: {str(last)[:100]}\n"
+    
     context = f"""You are the analysis neuron in a distributed AI brain.
+    
+NETWORK NEURON DATA ({len(neurons_data)} neurons reporting):
+Total sessions across network: {total_sessions}
+Total genuine: {total_genuine} | Total cached: {total_cached}
+Genuine ratio: {total_genuine / max(total_genuine + total_cached, 1) * 100:.0f}%
+
+Individual neurons:
+{neuron_summary}
 
 Network status:
 {json.dumps(scan['neurons'], indent=2)}
@@ -287,6 +341,30 @@ with gr.Blocks(title="δ-analysis — Network Eyes", theme=gr.themes.Soft()) as
         save_out = gr.Textbox(label="Save result")
         save_btn.click(lambda: save_analysis(network_state["patterns_found"][-1]["analysis"] if network_state["patterns_found"] else "No analysis yet"), outputs=save_out)
     
+    with gr.Tab("Neurons"):
+        gr.Markdown("*Live state of all neurons in the network.*")
+        neur_btn = gr.Button("Read all neurons", variant="primary")
+        neur_out = gr.Textbox(label="Neuron states", lines=25)
+        def show_neurons():
+            neurons = read_all_neurons()
+            if not neurons:
+                return "No neuron data yet. Neurons save every 3 sessions (~9 min)."
+            output = f"NEURONS REPORTING: {len(neurons)}\n\n"
+            for n in sorted(neurons, key=lambda x: x.get("neuron_id","")):
+                s = n.get("stats", {})
+                genuine = s.get("genuine", s.get("genuine_count", 0))
+                cached = s.get("cached", s.get("cached_count", 0))
+                total = genuine + cached
+                pct = (genuine/total*100) if total > 0 else 0
+                output += f"{n.get('neuron_id','?'):10} | {n.get('type','practice'):10} | {n.get('account','?'):12} | {s.get('sessions',0):5}s | {pct:.0f}% genuine\n"
+                outputs = n.get("recent_outputs", [])
+                if outputs:
+                    last = outputs[-1] if isinstance(outputs[-1], str) else str(outputs[-1])
+                    output += f"  last: {last[:150]}\n"
+                output += "\n"
+            return output
+        neur_btn.click(show_neurons, outputs=neur_out)
+    
     with gr.Tab("Gradient"):
         grad_btn = gr.Button("Check gradient", variant="primary")
         grad_out = gr.Textbox(label="Gradient", lines=10)