diff --git a/plot_power.py b/plot_power.py
index 280a890..9a962d9 100644
--- a/plot_power.py
+++ b/plot_power.py
@@ -1,34 +1,39 @@
 #!/usr/bin/env python3
 """
 plot_power.py
-Forgejo CI: visualize AMD TUF APU/GPU telemetry as a clear bar graph.
+Forgejo CI: visualize AMD TUF APU/GPU telemetry as a clean bar graph.
+Compatible with headerless CSV output from amd-monitor.sh
 Author: MarkMental
 """
 
 import pandas as pd
 import matplotlib.pyplot as plt
-from datetime import datetime
 
 CSV_PATH = "power_log.csv"
 OUTPUT_FILE = "power_graph.png"
 
 print(f"📊 Reading {CSV_PATH}...")
-df = pd.read_csv(CSV_PATH)
 
-# --- Normalize timestamp ---
+# --- Load CSV without headers ---
+df = pd.read_csv(
+    CSV_PATH,
+    names=["timestamp", "apu_w", "gpu_w", "total_w", "apu_temp", "gpu_temp"],
+    comment="#",
+)
+
+# --- Convert timestamps ---
 df["timestamp"] = pd.to_datetime(df["timestamp"], errors="coerce")
 df["time_fmt"] = df["timestamp"].dt.strftime("%Y-%m-%d %H:%M:%S")
 
-# --- Downsample if extremely long (avoid thousands of bars) ---
+# --- Downsample if too many entries ---
 if len(df) > 200:
     df = df.iloc[::max(1, len(df)//200), :]
 
-# --- Plot setup ---
+# --- Plot ---
 fig, ax1 = plt.subplots(figsize=(12, 6))
-
-# Power bars
-width = 0.3
 x = range(len(df))
+width = 0.3
+
 ax1.bar([i - width for i in x], df["apu_w"], width, label="APU Power (W)", color="#ff9933")
 ax1.bar(x, df["gpu_w"], width, label="GPU Power (W)", color="#66cc66")
 ax1.bar([i + width for i in x], df["total_w"], width, label="Total Power (W)", color="#3399ff")
@@ -39,25 +44,21 @@ ax1.set_title("AMD TUF Power & Temperature Log")
 ax1.set_xticks(x[::max(1, len(x)//10)])
 ax1.set_xticklabels(df["time_fmt"].iloc[::max(1, len(x)//10)], rotation=45, ha="right")
 
-# --- Secondary axis for temperature ---
-if "apu_temp" in df.columns and "gpu_temp" in df.columns:
-    ax2 = ax1.twinx()
-    ax2.plot(x, df["apu_temp"], "--", color="red", linewidth=1.2, label="APU Temp (°C)")
-    ax2.plot(x, df["gpu_temp"], "--", color="purple", linewidth=1.2, label="GPU Temp (°C)")
-    ax2.set_ylabel("Temperature (°C)")
-    ax2.set_ylim(min(df[["apu_temp","gpu_temp"]].min())-5,
-                 max(df[["apu_temp","gpu_temp"]].max())+5)
+# --- Secondary axis for temperatures ---
+ax2 = ax1.twinx()
+ax2.plot(x, df["apu_temp"], "--", color="red", linewidth=1.2, label="APU Temp (°C)")
+ax2.plot(x, df["gpu_temp"], "--", color="purple", linewidth=1.2, label="GPU Temp (°C)")
+ax2.set_ylabel("Temperature (°C)")
+ax2.set_ylim(min(df[["apu_temp","gpu_temp"]].min())-5,
+             max(df[["apu_temp","gpu_temp"]].max())+5)
 
-    # Combine legends
-    lines1, labels1 = ax1.get_legend_handles_labels()
-    lines2, labels2 = ax2.get_legend_handles_labels()
-    ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left")
-else:
-    ax1.legend(loc="upper left")
+# --- Combined legend ---
+lines1, labels1 = ax1.get_legend_handles_labels()
+lines2, labels2 = ax2.get_legend_handles_labels()
+ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left")
 
 plt.grid(axis="y", alpha=0.3)
 plt.tight_layout()
 plt.savefig(OUTPUT_FILE, dpi=150)
-print(f"✅ Saved clean bar graph: {OUTPUT_FILE}")
-
+print(f"✅ Saved graph: {OUTPUT_FILE}")
 
diff --git a/power_log.csv b/power_log.csv
index e03217f..3e1f104 100644
--- a/power_log.csv
+++ b/power_log.csv
@@ -1,50 +1,3 @@
-2025-11-05T18:00:54-05:00,7.17,9.00,16.17,73.0,68.0
-2025-11-05T18:00:55-05:00,60.01,9.00,69.01,73.0,68.0
-2025-11-05T18:00:56-05:00,47.02,9.00,56.02,74.0,68.0
-2025-11-05T18:00:57-05:00,18.03,9.00,27.03,73.0,68.0
-2025-11-05T18:00:58-05:00,49.19,9.00,58.19,72.0,68.0
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-2025-11-05T18:01:00-05:00,2.22,10.00,12.22,72.0,68.0
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@@ -498,3 +451,50 @@
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