metrics: add host multi-mount storage metrics, Prometheus exporter, and Grafana panels

core/metrics.py

@@ -1,15 +1,11 @@
 from prometheus_client import Counter, Histogram, Gauge, start_http_server
 import logging
+from typing import Optional
 
 logger = logging.getLogger(__name__)
 
 # Counters
-COLLECTED_POSTS_TOTAL = Counter(
-    "copykar_posts_collected_total",
-    "Total posts collected by the Telethon Userbot",
-    ["source_channel_id"]
-)
-
+# Ingest is counted once, by SOURCE_ACTIVITY_TOTAL, which also carries the channel title.
 SOURCE_ACTIVITY_TOTAL = Counter(
     "copykar_source_activity_total",
     "Total posts ingested per source channel",
@@ -40,10 +36,11 @@ ADMIN_ACTIONS_TOTAL = Counter(
     ["action"]
 )
 
-POSTS_PUBLISHED_TOTAL = Counter(
-    "copykar_posts_published_total",
-    "Total posts successfully published to target channels",
-    ["target_channel_id"]
+# Delivery is counted once, by TARGET_ACTIVITY_TOTAL.
+AUTO_ROUTED_POSTS_TOTAL = Counter(
+    "copykar_auto_routed_posts_total",
+    "Posts queued automatically by a source-to-target route",
+    ["source_channel_id", "target_title"]
 )
 
 ERRORS_TOTAL = Counter(
@@ -52,6 +49,12 @@ ERRORS_TOTAL = Counter(
     ["service", "error_type"]
 )
 
+ERRORS_RESOLVED_TOTAL = Counter(
+    "copykar_errors_resolved_total",
+    "Errors an admin has marked as fixed",
+    ["service", "error_type"]
+)
+
 # Histograms
 AI_LATENCY_SECONDS = Histogram(
     "copykar_ai_latency_seconds",
@@ -66,14 +69,136 @@ QUEUE_POSTS_GAUGE = Gauge(
     ["status"]
 )
 
-REDIS_QUEUE_SIZE_GAUGE = Gauge(
-    "copykar_redis_queue_size",
-    "Current number of posts waiting in Redis incoming queue"
+ERRORS_OPEN_GAUGE = Gauge(
+    "copykar_errors_open",
+    "Unresolved errors currently recorded, by service and exception type",
+    ["service", "error_type"]
 )
 
-def start_metrics_server(port: int = 8000):
+ERRORS_OPEN_TOTAL_GAUGE = Gauge(
+    "copykar_errors_open_total",
+    "Total unresolved errors across all services"
+)
+
+import shutil
+import asyncio
+
+# Disk Space Gauges per Mount Point
+DISK_TOTAL_BYTES = Gauge(
+    "copykar_disk_total_bytes",
+    "Total disk capacity in bytes",
+    ["mountpoint", "device"]
+)
+
+DISK_USED_BYTES = Gauge(
+    "copykar_disk_used_bytes",
+    "Used disk space in bytes",
+    ["mountpoint", "device"]
+)
+
+DISK_FREE_BYTES = Gauge(
+    "copykar_disk_free_bytes",
+    "Free/available disk space in bytes",
+    ["mountpoint", "device"]
+)
+
+DISK_FREE_PERCENT = Gauge(
+    "copykar_disk_free_percent",
+    "Percentage of free disk space",
+    ["mountpoint", "device"]
+)
+
+
+import os
+
+VOLUME_DEFINITIONS = [
+    {
+        "mountpoint": "/",
+        "device": "/dev/sda1",
+        "container_paths": ["/host_os/home", "/hostfs/host_mnt/home", "/home", "/host_os_disk", "/hostfs", "/"]
+    },
+    {
+        "mountpoint": "/projects",
+        "device": "/dev/sda2",
+        "container_paths": ["/host_os/projects", "/projects"]
+    },
+    {
+        "mountpoint": "/boot",
+        "device": "/dev/sda4",
+        "container_paths": ["/host_os/boot", "/boot"]
+    },
+    {
+        "mountpoint": "/boot/efi",
+        "device": "/dev/sda3",
+        "container_paths": ["/host_os/boot_efi", "/boot/efi"]
+    },
+    {
+        "mountpoint": "/run/media/mamad/WIN11_25H2_",
+        "device": "/dev/sdc1",
+        "container_paths": ["/host_os/media/mamad/WIN11_25H2_", "/run/media/mamad/WIN11_25H2_"]
+    }
+]
+
+
+def update_disk_metrics():
+    """Update Prometheus gauges with real host OS filesystem disk usage for each mounted volume."""
+    try:
+        recorded_mounts = set()
+        for v in VOLUME_DEFINITIONS:
+            mnt = v["mountpoint"]
+            dev = v["device"]
+            for path in v["container_paths"]:
+                if os.path.exists(path) and os.path.isdir(path):
+                    try:
+                        total, used, free = shutil.disk_usage(path)
+                        DISK_TOTAL_BYTES.labels(mountpoint=mnt, device=dev).set(total)
+                        DISK_USED_BYTES.labels(mountpoint=mnt, device=dev).set(used)
+                        DISK_FREE_BYTES.labels(mountpoint=mnt, device=dev).set(free)
+                        free_pct = (free / total * 100.0) if total > 0 else 0.0
+                        DISK_FREE_PERCENT.labels(mountpoint=mnt, device=dev).set(free_pct)
+                        recorded_mounts.add(mnt)
+                        break
+                    except Exception as err:
+                        logger.debug(f"Error measuring disk usage for {path}: {err}")
+
+        # If none of the specific mounts matched, fallback to root
+        if not recorded_mounts:
+            total, used, free = shutil.disk_usage("/")
+            DISK_TOTAL_BYTES.labels(mountpoint="/", device="default").set(total)
+            DISK_USED_BYTES.labels(mountpoint="/", device="default").set(used)
+            DISK_FREE_BYTES.labels(mountpoint="/", device="default").set(free)
+            free_pct = (free / total * 100.0) if total > 0 else 0.0
+            DISK_FREE_PERCENT.labels(mountpoint="/", device="default").set(free_pct)
+
+    except Exception as e:
+        logger.debug(f"Error updating host disk metrics: {e}")
+
+
+
+async def _disk_metrics_loop(interval: int = 15):
+    """Background task to keep disk metrics updated."""
+    while True:
+        try:
+            update_disk_metrics()
+        except Exception as e:
+            logger.debug(f"Disk metrics loop error: {e}")
+        await asyncio.sleep(interval)
+
+
+# The overall queue depth is sum(copykar_posts_queue_gauge) - no separate total series,
+# which previously made the dashboard report the queue twice.
+def start_metrics_server(port: int = 8008):
     try:
         start_http_server(port)
+        update_disk_metrics()
+        try:
+            loop = asyncio.get_event_loop()
+            if loop.is_running():
+                asyncio.create_task(_disk_metrics_loop())
+        except Exception:
+            pass
         logger.info(f"Prometheus metrics server running on port {port}")
     except Exception as e:
         logger.error(f"Failed to start Prometheus metrics server: {e}")
+
+

monitoring/grafana/provisioning/datasources/prometheus.yml

@@ -2,6 +2,7 @@ apiVersion: 1
 
 datasources:
   - name: Prometheus
+    uid: copykar-prometheus
     type: prometheus
     access: proxy
     url: http://prometheus:9090

monitoring/prometheus/prometheus.yml

@@ -5,4 +5,5 @@ global:
 scrape_configs:
   - job_name: "copykar"
     static_configs:
-      - targets: ["copykar:8000"]
+      - targets: ["copykar:8008"]
+

services/metrics_reporter.py

@@ -0,0 +1,306 @@
+import os
+import io
+import time
+import httpx
+import logging
+from typing import Dict, Any, List, Optional, Tuple
+
+logger = logging.getLogger(__name__)
+
+PROMETHEUS_URL = os.getenv(
+    "PROMETHEUS_URL",
+    "http://prometheus:9090" if os.path.exists("/.dockerenv") else "http://localhost:9090"
+)
+
+# Colors and style configuration for dark-mode graphs
+BG_COLOR = "#12171f"
+PANEL_BG = "#1a2230"
+GRID_COLOR = "#2c384d"
+TEXT_COLOR = "#e2e8f0"
+CYAN = "#38bdf8"
+GREEN = "#4ade80"
+PURPLE = "#a855f7"
+YELLOW = "#facc15"
+RED = "#f87171"
+ORANGE = "#fb923c"
+
+
+async def _query_instant(query: str) -> Optional[float]:
+    url = f"{PROMETHEUS_URL.rstrip('/')}/api/v1/query"
+    try:
+        async with httpx.AsyncClient(timeout=4.0) as client:
+            resp = await client.get(url, params={"query": query})
+            if resp.status_code == 200:
+                data = resp.json()
+                results = data.get("data", {}).get("result", [])
+                if results:
+                    val = results[0].get("value", [0, "0"])[1]
+                    return float(val)
+    except Exception as e:
+        logger.debug(f"Prometheus instant query failed ({query}): {e}")
+    return None
+
+
+async def _query_vector(query: str) -> List[Dict[str, Any]]:
+    url = f"{PROMETHEUS_URL.rstrip('/')}/api/v1/query"
+    try:
+        async with httpx.AsyncClient(timeout=4.0) as client:
+            resp = await client.get(url, params={"query": query})
+            if resp.status_code == 200:
+                data = resp.json()
+                return data.get("data", {}).get("result", [])
+    except Exception as e:
+        logger.debug(f"Prometheus vector query failed ({query}): {e}")
+    return []
+
+
+async def _query_range(query: str, start: float, end: float, step: str) -> List[Tuple[float, float]]:
+    url = f"{PROMETHEUS_URL.rstrip('/')}/api/v1/query_range"
+    try:
+        async with httpx.AsyncClient(timeout=8.0) as client:
+            resp = await client.get(url, params={"query": query, "start": start, "end": end, "step": step})
+            if resp.status_code == 200:
+                data = resp.json()
+                results = data.get("data", {}).get("result", [])
+                if results:
+                    values = results[0].get("values", [])
+                    return [(float(t), float(v)) for t, v in values]
+    except Exception as e:
+        logger.debug(f"Prometheus range query failed ({query}): {e}")
+    return []
+
+
+async def get_instant_metrics_report() -> str:
+    """Fetch current real-time metrics from Prometheus / Grafana data source and format a comprehensive report."""
+    # 1. Ingest metrics
+    total_ingested = await _query_instant("sum(copykar_source_activity_total)") or 0
+    ingest_rate = await _query_instant("sum(rate(copykar_source_activity_total[5m])) * 60") or 0.0
+
+    # 2. Publish metrics
+    total_published = await _query_instant("sum(copykar_target_activity_total)") or 0
+    publish_rate = await _query_instant("sum(rate(copykar_target_activity_total[5m])) * 60") or 0.0
+
+    # 3. AI metrics
+    ai_success = await _query_instant('sum(copykar_ai_requests_total{status="success"})') or 0
+    ai_error = await _query_instant('sum(copykar_ai_requests_total{status="error"})') or 0
+    ai_total = ai_success + ai_error
+    ai_latency_sum = await _query_instant("sum(copykar_ai_latency_seconds_sum)") or 0.0
+    ai_latency_cnt = await _query_instant("sum(copykar_ai_latency_seconds_count)") or 0.0
+    avg_latency = (ai_latency_sum / ai_latency_cnt) if ai_latency_cnt > 0 else 0.0
+    ai_rate = await _query_instant("sum(rate(copykar_ai_requests_total[5m])) * 60") or 0.0
+
+    # 4. Queue breakdown
+    queue_data = await _query_vector("copykar_posts_queue_gauge")
+    queue_breakdown: Dict[str, int] = {}
+    for item in queue_data:
+        st = item.get("metric", {}).get("status", "unknown")
+        val = int(float(item.get("value", [0, 0])[1]))
+        queue_breakdown[st] = val
+    total_queue = sum(queue_breakdown.values())
+
+    # 5. Duplicates & Errors
+    duplicates = await _query_instant("sum(copykar_duplicates_detected_total)") or 0
+    total_errors = await _query_instant("sum(copykar_errors_total)") or 0
+    open_errors = await _query_instant("copykar_errors_open_total") or 0
+    resolved_errors = await _query_instant("sum(copykar_errors_resolved_total)") or 0
+
+    # 6. Admin Actions
+    admin_actions = await _query_instant("sum(copykar_admin_actions_total)") or 0
+
+    # 7. Disk Space per mount point
+    disk_free_data = await _query_vector("copykar_disk_free_bytes")
+    disk_total_data = await _query_vector("copykar_disk_total_bytes")
+    disk_pct_data = await _query_vector("copykar_disk_free_percent")
+
+    mount_stats: Dict[str, Dict[str, Any]] = {}
+    for item in disk_free_data:
+        m = item.get("metric", {}).get("mountpoint", "/")
+        val = float(item.get("value", [0, 0])[1])
+        mount_stats.setdefault(m, {})["free_gb"] = val / (1024 ** 3)
+
+    for item in disk_total_data:
+        m = item.get("metric", {}).get("mountpoint", "/")
+        val = float(item.get("value", [0, 0])[1])
+        mount_stats.setdefault(m, {})["total_gb"] = val / (1024 ** 3)
+
+    for item in disk_pct_data:
+        m = item.get("metric", {}).get("mountpoint", "/")
+        val = float(item.get("value", [0, 0])[1])
+        mount_stats.setdefault(m, {})["pct"] = val
+
+    timestamp_str = time.strftime("%Y-%m-%d %H:%M:%S UTC", time.gmtime())
+
+    report = (
+        "📊 <b>گزارش وضعیت و متریک‌های لحظه‌ای سیستم (Grafana Metrics):</b>\n"
+        f"🕒 <i>زمان گزارش: {timestamp_str}</i>\n\n"
+        "📥 <b>ورودی از کانال‌های مبدا (Ingest):</b>\n"
+        f"• کل پست‌های دریافت شده: <b>{int(total_ingested):,}</b>\n"
+        f"• نرخ ورودی لحظه‌ای: <b>{ingest_rate:.2f}</b> پست در دقیقه\n\n"
+        "🚀 <b>انتشار در کانال‌های مقصد (Published):</b>\n"
+        f"• کل پست‌های منتشر شده: <b>{int(total_published):,}</b>\n"
+        f"• نرخ انتشار لحظه‌ای: <b>{publish_rate:.2f}</b> پست در دقیقه\n\n"
+        "🧠 <b>پردازش هوش مصنوعی (AI Engine):</b>\n"
+        f"• کل درخواست‌ها: <b>{int(ai_total):,}</b> (✅ {int(ai_success)} موفق | ❌ {int(ai_error)} خطا)\n"
+        f"• میانگین تاخیر پاسخ: <b>{avg_latency:.2f}s</b>\n"
+        f"• نرخ درخواست: <b>{ai_rate:.2f}</b> req/min\n\n"
+        "📬 <b>وضعیت صف انتشار (Paced Queue):</b>\n"
+        f"• کل پیام‌ها در صف: <b>{total_queue}</b>\n"
+    )
+
+    if queue_breakdown:
+        details = " | ".join([f"<code>{k}</code>: {v}" for k, v in queue_breakdown.items()])
+        report += f"  ({details})\n\n"
+    else:
+        report += "  <i>(صف خالی است)</i>\n\n"
+
+    report += (
+        "🛡 <b>پایش و سلامت سیستم:</b>\n"
+        f"• پست‌های تکراری شناسایی‌شده: <b>{int(duplicates):,}</b>\n"
+        f"• خطاهای ثبت‌شده: <b>{int(total_errors):,}</b> (⚠️ {int(open_errors)} باز | ✅ {int(resolved_errors)} رفع‌شده)\n"
+        f"• اقدامات ادمین: <b>{int(admin_actions):,}</b>\n\n"
+    )
+
+    if mount_stats:
+        report += "💽 <b>فضای ذخیره‌سازی تفکیکی درایوها (Mount Points Storage):</b>\n"
+        for mnt, data in sorted(mount_stats.items()):
+            free_gb = data.get("free_gb", 0.0)
+            tot_gb = data.get("total_gb", 0.0)
+            pct = data.get("pct", 0.0)
+            if tot_gb > 0 and tot_gb < 1.0:
+                free_mb = free_gb * 1024
+                tot_mb = tot_gb * 1024
+                report += f"• <code>{mnt}</code>: <b>{free_mb:.0f} MB</b> آزاد از <b>{tot_mb:.0f} MB</b> (<b>{pct:.1f}%</b> آزاد)\n"
+            else:
+                report += f"• <code>{mnt}</code>: <b>{free_gb:.2f} GB</b> آزاد از <b>{tot_gb:.2f} GB</b> (<b>{pct:.1f}%</b> آزاد)\n"
+        report += "\n"
+
+    report += "<i>👇 برای دریافت نمودار تصویری متریک‌ها روی بازه زمانی مورد نظر بزنید:</i>"
+    return report
+
+
+def _generate_chart_image(
+    time_range_label: str,
+    ingest_pts: List[Tuple[float, float]],
+    publish_pts: List[Tuple[float, float]],
+    ai_pts: List[Tuple[float, float]],
+    queue_pts: List[Tuple[float, float]],
+    error_pts: List[Tuple[float, float]],
+) -> bytes:
+    """Generate dark-mode multi-panel metrics graph using matplotlib in memory."""
+    import matplotlib
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+    import matplotlib.dates as mdates
+    from datetime import datetime
+
+    plt.style.use("dark_background")
+    fig, axes = plt.subplots(2, 2, figsize=(12, 7.5), dpi=140)
+    fig.patch.set_facecolor(BG_COLOR)
+    fig.suptitle(f"Copykar System Metrics Dashboard ({time_range_label})", fontsize=15, color=TEXT_COLOR, fontweight="bold", y=0.98)
+
+    for ax in axes.flat:
+        ax.set_facecolor(PANEL_BG)
+        ax.tick_params(colors=TEXT_COLOR, labelsize=8)
+        ax.grid(True, linestyle="--", alpha=0.3, color=GRID_COLOR)
+        for spine in ax.spines.values():
+            spine.set_color(GRID_COLOR)
+
+    # 1. Ingest & Publish Rates
+    ax1 = axes[0, 0]
+    ax1.set_title("Ingest vs Publish Rate (posts/min)", fontsize=10, color=CYAN, fontweight="bold")
+    if ingest_pts:
+        t1 = [datetime.fromtimestamp(p[0]) for p in ingest_pts]
+        v1 = [p[1] for p in ingest_pts]
+        ax1.plot(t1, v1, label="Ingested (posts/m)", color=CYAN, linewidth=1.8)
+    if publish_pts:
+        t2 = [datetime.fromtimestamp(p[0]) for p in publish_pts]
+        v2 = [p[1] for p in publish_pts]
+        ax1.plot(t2, v2, label="Published (posts/m)", color=GREEN, linewidth=1.8)
+    if ingest_pts or publish_pts:
+        ax1.legend(loc="upper left", fontsize=8, facecolor=PANEL_BG, edgecolor=GRID_COLOR)
+
+    # 2. AI Request Rate
+    ax2 = axes[0, 1]
+    ax2.set_title("AI Request Rate (req/min)", fontsize=10, color=PURPLE, fontweight="bold")
+    if ai_pts:
+        t_ai = [datetime.fromtimestamp(p[0]) for p in ai_pts]
+        v_ai = [p[1] for p in ai_pts]
+        ax2.plot(t_ai, v_ai, label="AI Calls / min", color=PURPLE, linewidth=1.8)
+        ax2.fill_between(t_ai, v_ai, color=PURPLE, alpha=0.2)
+        ax2.legend(loc="upper left", fontsize=8, facecolor=PANEL_BG, edgecolor=GRID_COLOR)
+
+    # 3. Queue Depth
+    ax3 = axes[1, 0]
+    ax3.set_title("Queue Depth (Active Posts)", fontsize=10, color=YELLOW, fontweight="bold")
+    if queue_pts:
+        t_q = [datetime.fromtimestamp(p[0]) for p in queue_pts]
+        v_q = [p[1] for p in queue_pts]
+        ax3.plot(t_q, v_q, label="Queue Size", color=YELLOW, linewidth=1.8)
+        ax3.fill_between(t_q, v_q, color=YELLOW, alpha=0.2)
+        ax3.legend(loc="upper left", fontsize=8, facecolor=PANEL_BG, edgecolor=GRID_COLOR)
+
+    # 4. Error Rate
+    ax4 = axes[1, 1]
+    ax4.set_title("Error Rate (errors/min)", fontsize=10, color=RED, fontweight="bold")
+    if error_pts:
+        t_err = [datetime.fromtimestamp(p[0]) for p in error_pts]
+        v_err = [p[1] for p in error_pts]
+        ax4.plot(t_err, v_err, label="Errors / min", color=RED, linewidth=1.8)
+        ax4.fill_between(t_err, v_err, color=RED, alpha=0.2)
+        ax4.legend(loc="upper left", fontsize=8, facecolor=PANEL_BG, edgecolor=GRID_COLOR)
+
+
+    # Formatting date axes
+    for ax in axes.flat:
+        ax.xaxis.set_major_formatter(mdates.DateFormatter("%H:%M"))
+        fig.autofmt_xdate(rotation=25)
+
+    plt.tight_layout(rect=[0, 0.03, 1, 0.95])
+    buf = io.BytesIO()
+    plt.savefig(buf, format="jpg", facecolor=BG_COLOR, edgecolor="none", bbox_inches="tight", pil_kwargs={"quality": 95})
+    plt.close(fig)
+    buf.seek(0)
+    return buf.getvalue()
+
+
+async def generate_metrics_graph(time_range: str = "15m") -> Tuple[bytes, str]:
+    """Fetch time-series range metrics and return a JPG chart image along with its exact timestamped filename."""
+    now = time.time()
+    now_dt = time.strftime("%Y-%m-%d_%H-%M-%S", time.localtime(now))
+    filename = f"copykar_metrics_{time_range}_{now_dt}.jpg"
+
+    if time_range == "15m":
+        start = now - 15 * 60
+        step = "15s"
+        label = "Last 15 Minutes"
+    elif time_range == "3h":
+        start = now - 3 * 3600
+        step = "1m"
+        label = "Last 3 Hours"
+    elif time_range == "24h":
+        start = now - 24 * 3600
+        step = "5m"
+        label = "Last 24 Hours"
+    else:
+        start = now - 15 * 60
+        step = "15s"
+        label = "Last 15 Minutes"
+
+    rate_window = "1m" if time_range in ("15m", "3h") else "5m"
+
+    ingest_pts = await _query_range(f"sum(rate(copykar_source_activity_total[{rate_window}])) * 60", start, now, step)
+    publish_pts = await _query_range(f"sum(rate(copykar_target_activity_total[{rate_window}])) * 60", start, now, step)
+    ai_pts = await _query_range(f"sum(rate(copykar_ai_requests_total[{rate_window}])) * 60", start, now, step)
+    queue_pts = await _query_range("sum(copykar_posts_queue_gauge)", start, now, step)
+    error_pts = await _query_range(f"sum(rate(copykar_errors_total[{rate_window}])) * 60", start, now, step)
+
+    chart_bytes = _generate_chart_image(
+        time_range_label=label,
+        ingest_pts=ingest_pts,
+        publish_pts=publish_pts,
+        ai_pts=ai_pts,
+        queue_pts=queue_pts,
+        error_pts=error_pts
+    )
+    return chart_bytes, filename
+

tests/test_vision_and_disk_metrics.py

@@ -0,0 +1,122 @@
+import asyncio
+import tempfile
+import os
+import shutil
+from unittest.mock import AsyncMock, patch, MagicMock
+from db.models import TargetChannel, AIProviderProfile
+from core.llm import LLMClient
+from services.ai_processor import AIProcessor
+from core.metrics import (
+    DISK_TOTAL_BYTES,
+    DISK_USED_BYTES,
+    DISK_FREE_BYTES,
+    DISK_FREE_PERCENT,
+    update_disk_metrics
+)
+from services.metrics_reporter import get_instant_metrics_report
+
+
+def test_disk_metrics_gauge():
+    update_disk_metrics()
+    sample = DISK_TOTAL_BYTES.collect()[0].samples
+    assert len(sample) > 0
+    for s in sample:
+        assert s.value > 0
+
+
+async def test_metrics_report_includes_disk():
+    fake_vector_free = [
+        {"metric": {"mountpoint": "/"}, "value": [0, str(20.0 * 1024 ** 3)]},
+        {"metric": {"mountpoint": "/projects"}, "value": [0, str(35.0 * 1024 ** 3)]}
+    ]
+    fake_vector_total = [
+        {"metric": {"mountpoint": "/"}, "value": [0, str(200.0 * 1024 ** 3)]},
+        {"metric": {"mountpoint": "/projects"}, "value": [0, str(40.0 * 1024 ** 3)]}
+    ]
+    fake_vector_pct = [
+        {"metric": {"mountpoint": "/"}, "value": [0, "10.0"]},
+        {"metric": {"mountpoint": "/projects"}, "value": [0, "87.5"]}
+    ]
+
+    async def fake_query_vector(query):
+        if "copykar_disk_free_bytes" in query:
+            return fake_vector_free
+        if "copykar_disk_total_bytes" in query:
+            return fake_vector_total
+        if "copykar_disk_free_percent" in query:
+            return fake_vector_pct
+        return []
+
+    with patch("services.metrics_reporter._query_instant", return_value=0.0), \
+         patch("services.metrics_reporter._query_vector", side_effect=fake_query_vector):
+
+        report = await get_instant_metrics_report()
+        assert "فضای ذخیره‌سازی تفکیکی درایوها (Mount Points Storage)" in report
+        assert "<code>/</code>" in report
+        assert "<code>/projects</code>" in report
+        assert "20.00 GB" in report
+        assert "35.00 GB" in report
+        assert "10.0%" in report
+        assert "87.5%" in report
+
+
+async def test_multimodal_vision_image_payload():
+    with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmp:
+        tmp.write(b"fake-image-binary-data")
+        tmp_path = tmp.name
+
+    try:
+        profile_openai = AIProviderProfile(
+            id=1,
+            name="OpenAI Vision",
+            provider_type="openai",
+            model="gpt-4o",
+            is_active=True
+        )
+        repo_mock = AsyncMock()
+        repo_mock.get_active_provider_profile.return_value = profile_openai
+        repo_mock.get_provider_profiles.return_value = [profile_openai]
+        repo_mock.get_setting.return_value = "false"
+        repo_mock.record_ai_log = AsyncMock()
+
+        client = LLMClient(repo=repo_mock)
+
+        # 1. Test OpenAI vision call formatting
+        captured_messages = []
+        async def fake_post(url, headers=None, json=None):
+            captured_messages.extend(json.get("messages", []))
+            mock_resp = MagicMock()
+            mock_resp.raise_for_status = MagicMock()
+            mock_resp.json.return_value = {
+                "choices": [{"message": {"content": '{"decision": "accept", "rewritten_text": "Image saw a cat"}'}}]
+            }
+            return mock_resp
+
+        with patch("httpx.AsyncClient.post", side_effect=fake_post):
+            target = TargetChannel(id=1, channel_id=-100123456, title="Vision Channel", username="vision_ch", language="fa", personality="طنز")
+            processor = AIProcessor(repo=repo_mock, llm=client)
+            res = await processor.rewrite_for_target("عکس را ببین", target, has_media=True, image_path=tmp_path)
+            assert res.is_rejected is False
+            assert "Image saw a cat" in str(res)
+
+            user_msg = [m for m in captured_messages if m.get("role") == "user"][0]
+            assert isinstance(user_msg["content"], list)
+            types = [part["type"] for part in user_msg["content"]]
+            assert "text" in types
+            assert "image_url" in types
+            img_url = [part["image_url"]["url"] for part in user_msg["content"] if part["type"] == "image_url"][0]
+            assert img_url.startswith("data:image/jpeg;base64,")
+
+    finally:
+        if os.path.exists(tmp_path):
+            os.remove(tmp_path)
+
+
+async def main():
+    test_disk_metrics_gauge()
+    await test_metrics_report_includes_disk()
+    await test_multimodal_vision_image_payload()
+    print("All disk metrics and multimodal vision image passing tests passed successfully!")
+
+if __name__ == "__main__":
+    asyncio.run(main())