import json
import math
import os
import ast
import re
from dataclasses import dataclass
from functools import lru_cache
from html import escape
from itertools import permutations
from pathlib import Path
from typing import Dict, Iterable, List, Optional, Tuple

import gradio as gr
import pandas as pd


DEPOT = {
    "customer": "Depot",
    "lat": 40.7280,
    "lng": -73.9980,
}

SAMPLE_PATH = Path(__file__).with_name("sample_orders.csv")
AVG_SPEED_KMPH = 22.0
CAPACITY = 18
START_MINUTE = 8 * 60
MINICPM_REPO = "openbmb/MiniCPM5-1B-GGUF"
MINICPM_FILE = "MiniCPM5-1B-Q4_K_M.gguf"
MINICPM_PARAMS = "1.08B"


@dataclass(frozen=True)
class Stop:
    order_id: str
    customer: str
    lat: float
    lng: float
    demand: int
    service_min: int
    ready_time: int
    due_time: int
    priority: str
    notes: str
    manual_sequence: int


@dataclass(frozen=True)
class PlanStop:
    stop: Stop
    route_id: int
    arrival: int
    start: int
    depart: int
    distance_km: float
    late_min: int
    wait_min: int


def time_to_min(value: str) -> int:
    value = str(value or "").strip()
    if not value:
        return 17 * 60
    match = re.match(r"^(\d{1,2}):(\d{2})$", value)
    if not match:
        return 17 * 60
    hour, minute = int(match.group(1)), int(match.group(2))
    return max(0, min(23 * 60 + 59, hour * 60 + minute))


def min_to_time(value: int) -> str:
    value = max(0, int(round(value)))
    return f"{value // 60:02d}:{value % 60:02d}"


def haversine_km(a_lat: float, a_lng: float, b_lat: float, b_lng: float) -> float:
    radius = 6371.0
    lat1, lat2 = math.radians(a_lat), math.radians(b_lat)
    d_lat = math.radians(b_lat - a_lat)
    d_lng = math.radians(b_lng - a_lng)
    h = (
        math.sin(d_lat / 2) ** 2
        + math.cos(lat1) * math.cos(lat2) * math.sin(d_lng / 2) ** 2
    )
    return 2 * radius * math.asin(math.sqrt(h))


def travel_minutes(distance_km: float) -> int:
    return int(math.ceil((distance_km / AVG_SPEED_KMPH) * 60))


def parse_orders(file_obj) -> List[Stop]:
    if file_obj is None:
        df = pd.read_csv(SAMPLE_PATH)
    else:
        file_path = file_obj if isinstance(file_obj, str) else file_obj.name
        df = pd.read_csv(file_path)

    required = {
        "order_id",
        "customer",
        "lat",
        "lng",
        "demand",
        "service_min",
        "ready_time",
        "due_time",
        "priority",
        "notes",
    }
    missing = sorted(required - set(df.columns))
    if missing:
        raise gr.Error(f"CSV is missing required columns: {', '.join(missing)}")

    if "manual_sequence" not in df.columns:
        df["manual_sequence"] = range(1, len(df) + 1)

    stops: List[Stop] = []
    for row in df.to_dict("records"):
        stops.append(
            Stop(
                order_id=str(row["order_id"]),
                customer=str(row["customer"]),
                lat=float(row["lat"]),
                lng=float(row["lng"]),
                demand=int(row["demand"]),
                service_min=int(row["service_min"]),
                ready_time=time_to_min(str(row["ready_time"])),
                due_time=time_to_min(str(row["due_time"])),
                priority=str(row["priority"]).lower(),
                notes=str(row.get("notes", "")),
                manual_sequence=int(row.get("manual_sequence", len(stops) + 1)),
            )
        )
    return stops


def parse_dispatch_notes(notes: str) -> Dict[str, object]:
    text = (notes or "").lower()
    constraints: Dict[str, object] = {
        "prefer_early_priority": True,
        "avoid_late_penalty": 2.0,
        "max_route_load": CAPACITY,
        "depot_start": START_MINUTE,
        "boost_terms": [],
    }

    if "cold" in text or "fresh" in text or "produce" in text:
        constraints["boost_terms"].append("fresh")
    if "medical" in text or "clinic" in text or "medicine" in text:
        constraints["boost_terms"].append("medical")
    if "school" in text:
        constraints["boost_terms"].append("school")
    if "lunch" in text or "noon" in text:
        constraints["soft_due_before"] = 12 * 60

    hour_match = re.search(r"(?:start|leave|depart)\D{0,12}(\d{1,2})(?::(\d{2}))?", text)
    if hour_match:
        hour = int(hour_match.group(1))
        minute = int(hour_match.group(2) or 0)
        if 1 <= hour <= 23:
            constraints["depot_start"] = hour * 60 + minute

    capacity_match = re.search(r"(?:capacity|load|max load|van)\D{0,12}(\d{1,3})", text)
    if capacity_match:
        constraints["max_route_load"] = max(1, int(capacity_match.group(1)))

    return constraints


def normalize_constraints(raw: Dict[str, object]) -> Dict[str, object]:
    constraints = {
        "prefer_early_priority": bool(raw.get("prefer_early_priority", True)),
        "avoid_late_penalty": float(raw.get("avoid_late_penalty", 2.0) or 2.0),
        "max_route_load": int(raw.get("max_route_load", CAPACITY) or CAPACITY),
        "depot_start": int(raw.get("depot_start", START_MINUTE) or START_MINUTE),
        "boost_terms": list(raw.get("boost_terms", []) or []),
        "source": raw.get("source", "rule-fallback"),
    }
    if raw.get("soft_due_before") is not None:
        constraints["soft_due_before"] = int(raw["soft_due_before"])
    constraints["max_route_load"] = max(1, min(200, constraints["max_route_load"]))
    constraints["avoid_late_penalty"] = max(0.5, min(10.0, constraints["avoid_late_penalty"]))
    return constraints


def extract_json_object(text: str) -> Optional[Dict[str, object]]:
    cleaned = (text or "").strip()
    fenced = re.search(r"```(?:json)?\s*(\{.*?\})\s*```", cleaned, re.DOTALL | re.IGNORECASE)
    if fenced:
        cleaned = fenced.group(1)
    match = re.search(r"\{.*\}", cleaned, re.DOTALL)
    if not match:
        return None
    raw = match.group(0)
    try:
        parsed = json.loads(raw)
    except json.JSONDecodeError:
        try:
            parsed = ast.literal_eval(raw)
        except (SyntaxError, ValueError):
            normalized = (
                raw.replace("True", "true")
                .replace("False", "false")
                .replace("None", "null")
            )
            try:
                parsed = json.loads(normalized)
            except json.JSONDecodeError:
                return None
    return parsed if isinstance(parsed, dict) else None


@lru_cache(maxsize=1)
def get_minicpm_llm():
    if os.environ.get("DISABLE_MINICPM", "").lower() in {"1", "true", "yes"}:
        return None
    try:
        from huggingface_hub import hf_hub_download
        from llama_cpp import Llama
    except Exception:
        return None

    try:
        model_path = hf_hub_download(repo_id=MINICPM_REPO, filename=MINICPM_FILE)
        return Llama(
            model_path=model_path,
            n_ctx=768,
            n_threads=max(1, min(4, os.cpu_count() or 2)),
            n_batch=32,
            n_gpu_layers=0,
            verbose=False,
        )
    except Exception:
        return None


def minicpm_parse_dispatch_notes(notes: str, use_minicpm: bool = False) -> Tuple[Dict[str, object], str]:
    fallback = normalize_constraints(parse_dispatch_notes(notes))
    if not use_minicpm:
        fallback["source"] = "rule-fallback"
        return (
            fallback,
            "Fast CPU Basic mode used the deterministic parser. Enable MiniCPM5 parser to run the local GGUF model path.",
        )

    llm = get_minicpm_llm()
    if llm is None:
        fallback["source"] = "rule-fallback"
        return fallback, "MiniCPM5 local runtime is unavailable, so the deterministic parser handled the notes."

    prompt = f"""<|im_start|>system
You convert dispatcher notes into one compact JSON object.
Return JSON only. No markdown. No explanation.
Schema:
{{
  "prefer_early_priority": true,
  "avoid_late_penalty": 2.0,
  "max_route_load": 18,
  "depot_start": 480,
  "soft_due_before": 720,
  "boost_terms": ["school", "medical", "fresh"]
}}
Use minutes after midnight for times. Omit soft_due_before if no lunch/noon constraint is present.
<|im_end|>
<|im_start|>user
Dispatcher notes: {notes}
<|im_end|>
<|im_start|>assistant
"""
    try:
        result = llm(
            prompt,
            max_tokens=96,
            temperature=0.0,
            top_p=1.0,
            stop=["<|im_end|>", "\n\n\n"],
        )
        text = result["choices"][0]["text"]
        parsed = extract_json_object(text)
        if parsed:
            parsed["source"] = f"{MINICPM_REPO}/{MINICPM_FILE}"
            return normalize_constraints(parsed), text.strip()
    except Exception as exc:
        fallback["source"] = "rule-fallback"
        return fallback, f"MiniCPM5 parsing failed and fallback parser was used: {exc}"

    fallback["source"] = "rule-fallback"
    return fallback, "MiniCPM5 returned no valid JSON, so the deterministic parser handled the notes."


def priority_weight(stop: Stop, constraints: Dict[str, object]) -> float:
    score = 0.0
    if stop.priority == "high":
        score -= 1.4
    if constraints.get("soft_due_before") and stop.due_time <= int(constraints["soft_due_before"]):
        score -= 0.8
    searchable = f"{stop.customer} {stop.notes}".lower()
    for term in constraints.get("boost_terms", []):
        if term in searchable:
            score -= 1.0
    return score


def score_route(route: List[Stop], start_minute: int) -> Tuple[int, float, int]:
    plan, metrics = simulate_single_route(route, start_minute, route_id=1)
    late_stops = sum(1 for item in plan if item.late_min > 0)
    return int(metrics["late_min"]), float(metrics["distance_km"]), late_stops


def best_order_for_group(stops: List[Stop], start_minute: int) -> List[Stop]:
    if len(stops) <= 1:
        return stops[:]
    if len(stops) <= 7:
        candidates = permutations(stops)
    else:
        ordered = sorted(stops, key=lambda s: (s.due_time, s.ready_time, -priority_weight(s, {})))
        candidates = [ordered]

    best_route: Optional[List[Stop]] = None
    best_score: Optional[Tuple[int, float, int]] = None
    for candidate in candidates:
        route = list(candidate)
        score = score_route(route, start_minute)
        if best_score is None or score < best_score:
            best_score = score
            best_route = route
    return best_route or stops[:]


def build_capacity_routes(stops: List[Stop], constraints: Dict[str, object]) -> List[List[Stop]]:
    capacity = int(constraints["max_route_load"])
    ordered = sorted(
        stops,
        key=lambda stop: (
            stop.due_time,
            stop.ready_time,
            0 if stop.priority == "high" else 1,
            priority_weight(stop, constraints),
        ),
    )
    routes: List[List[Stop]] = []
    loads: List[int] = []
    for stop in ordered:
        best_idx = None
        best_score = None
        for idx, route in enumerate(routes):
            if loads[idx] + stop.demand > capacity:
                continue
            trial = best_order_for_group(route + [stop], int(constraints["depot_start"]))
            late, dist, late_stops = score_route(trial, int(constraints["depot_start"]))
            score = (late, late_stops, dist)
            if best_score is None or score < best_score:
                best_score = score
                best_idx = idx
        if best_idx is None:
            routes.append([stop])
            loads.append(stop.demand)
        else:
            routes[best_idx] = best_order_for_group(routes[best_idx] + [stop], int(constraints["depot_start"]))
            loads[best_idx] += stop.demand

    return [best_order_for_group(route, int(constraints["depot_start"])) for route in routes]


def route_distance(route: Iterable[Stop]) -> float:
    cur_lat, cur_lng = DEPOT["lat"], DEPOT["lng"]
    total = 0.0
    last_lat, last_lng = cur_lat, cur_lng
    for stop in route:
        total += haversine_km(last_lat, last_lng, stop.lat, stop.lng)
        last_lat, last_lng = stop.lat, stop.lng
    total += haversine_km(last_lat, last_lng, cur_lat, cur_lng)
    return total


def simulate_single_route(route: List[Stop], start_minute: int, route_id: int) -> Tuple[List[PlanStop], Dict[str, float]]:
    cur_lat, cur_lng = DEPOT["lat"], DEPOT["lng"]
    current = start_minute
    plan: List[PlanStop] = []
    load = 0
    total_distance = 0.0
    total_late = 0
    total_wait = 0

    for stop in route:
        distance = haversine_km(cur_lat, cur_lng, stop.lat, stop.lng)
        arrival = current + travel_minutes(distance)
        start = max(arrival, stop.ready_time)
        wait = max(0, start - arrival)
        late = max(0, start - stop.due_time)
        depart = start + stop.service_min
        plan.append(
            PlanStop(
                stop=stop,
                route_id=route_id,
                arrival=arrival,
                start=start,
                depart=depart,
                distance_km=distance,
                late_min=late,
                wait_min=wait,
            )
        )
        total_distance += distance
        total_late += late
        total_wait += wait
        load += stop.demand
        current = depart
        cur_lat, cur_lng = stop.lat, stop.lng

    back = haversine_km(cur_lat, cur_lng, DEPOT["lat"], DEPOT["lng"])
    total_distance += back
    finish = current + travel_minutes(back)
    metrics = {
        "distance_km": total_distance,
        "late_min": total_late,
        "wait_min": total_wait,
        "finish_min": finish,
        "load": load,
        "on_time_rate": 100.0 * (1 - sum(1 for p in plan if p.late_min > 0) / max(1, len(plan))),
    }
    return plan, metrics


def simulate_routes(routes: List[List[Stop]], start_minute: int) -> Tuple[List[PlanStop], Dict[str, float]]:
    all_plan: List[PlanStop] = []
    total_distance = 0.0
    total_late = 0
    total_wait = 0
    total_load = 0
    finish_min = start_minute
    for route_id, route in enumerate(routes, start=1):
        plan, metrics = simulate_single_route(route, start_minute, route_id)
        all_plan.extend(plan)
        total_distance += metrics["distance_km"]
        total_late += metrics["late_min"]
        total_wait += metrics["wait_min"]
        total_load += metrics["load"]
        finish_min = max(finish_min, metrics["finish_min"])
    metrics = {
        "distance_km": total_distance,
        "late_min": total_late,
        "wait_min": total_wait,
        "finish_min": finish_min,
        "load": total_load,
        "routes": len(routes),
        "on_time_rate": 100.0 * (1 - sum(1 for p in all_plan if p.late_min > 0) / max(1, len(all_plan))),
    }
    return all_plan, metrics


def manual_route(stops: List[Stop]) -> List[Stop]:
    return sorted(stops, key=lambda stop: stop.manual_sequence)


def route_table(plan: List[PlanStop]) -> pd.DataFrame:
    return pd.DataFrame(
        [
            {
                "Route": item.route_id,
                "Stop #": sum(1 for prev in plan[:idx] if prev.route_id == item.route_id) + 1,
                "Order": item.stop.order_id,
                "Customer": item.stop.customer,
                "Arrive": min_to_time(item.arrival),
                "Start": min_to_time(item.start),
                "Depart": min_to_time(item.depart),
                "Window": f"{min_to_time(item.stop.ready_time)}-{min_to_time(item.stop.due_time)}",
                "Demand": item.stop.demand,
                "Late min": item.late_min,
                "Notes": item.stop.notes,
            }
            for idx, item in enumerate(plan)
        ]
    )


def metrics_markdown(auto_metrics: Dict[str, float], manual_metrics: Dict[str, float]) -> str:
    distance_delta = manual_metrics["distance_km"] - auto_metrics["distance_km"]
    late_delta = manual_metrics["late_min"] - auto_metrics["late_min"]
    return f"""
### Dispatch Score

| Metric | Manual baseline | Tiny Dispatch Coach | Change |
|---|---:|---:|---:|
| Routes / trips | {manual_metrics.get('routes', 1):.0f} | {auto_metrics.get('routes', 1):.0f} | |
| Distance | {manual_metrics['distance_km']:.1f} km | {auto_metrics['distance_km']:.1f} km | {distance_delta:+.1f} km |
| Late minutes | {manual_metrics['late_min']:.0f} | {auto_metrics['late_min']:.0f} | {late_delta:+.0f} |
| Waiting minutes | {manual_metrics['wait_min']:.0f} | {auto_metrics['wait_min']:.0f} | {manual_metrics['wait_min'] - auto_metrics['wait_min']:+.0f} |
| Finish time | {min_to_time(manual_metrics['finish_min'])} | {min_to_time(auto_metrics['finish_min'])} | |
| On-time rate | {manual_metrics['on_time_rate']:.0f}% | {auto_metrics['on_time_rate']:.0f}% | {auto_metrics['on_time_rate'] - manual_metrics['on_time_rate']:+.0f} pts |

**Coach note:** The planner treats time-window risk as the first objective, then uses distance as a tie-breaker. It may split the day into multiple feasible trips when the notes imply a small van capacity.
"""


def constraints_markdown(constraints: Dict[str, object], model_trace: str) -> str:
    rows = "\n".join(f"- **{key}**: `{value}`" for key, value in constraints.items())
    trace = escape(model_trace or "")
    return f"""### OpenBMB MiniCPM5 Constraint Parse

**Model path:** `{MINICPM_REPO}` / `{MINICPM_FILE}`  
**Parameter count:** `{MINICPM_PARAMS}`  
**Runtime target:** local GGUF via llama.cpp; deterministic parser fallback if the runtime is unavailable.

{rows}

<details>
<summary>Parser trace</summary>

```text
{trace}
```
</details>
"""


def route_cards(plan: List[PlanStop]) -> str:
    cards = []
    for idx, item in enumerate(plan, start=1):
        status = "late" if item.late_min else "on time"
        cards.append(
            f"""
<div class="route-card">
  <div class="route-card-top">
    <span class="route-index">{item.route_id}.{sum(1 for prev in plan[:idx - 1] if prev.route_id == item.route_id) + 1}</span>
    <span class="route-title">{escape(item.stop.customer)}</span>
    <span class="route-status {status.replace(' ', '-')}">{status}</span>
  </div>
  <div class="route-meta">{escape(item.stop.order_id)} · route {item.route_id} · arrive {min_to_time(item.arrival)} · depart {min_to_time(item.depart)} · load {item.stop.demand}</div>
  <div class="route-note">{escape(item.stop.notes)}</div>
</div>
"""
        )
    return "<div class='route-cards'>" + "\n".join(cards) + "</div>"


def route_map(plan: List[PlanStop]) -> str:
    points = [(DEPOT["lat"], DEPOT["lng"], "Depot")]
    points.extend((item.stop.lat, item.stop.lng, item.stop.customer) for item in plan)
    lat_values = [p[0] for p in points]
    lng_values = [p[1] for p in points]
    min_lat, max_lat = min(lat_values), max(lat_values)
    min_lng, max_lng = min(lng_values), max(lng_values)
    pad_lat = max(0.002, (max_lat - min_lat) * 0.12)
    pad_lng = max(0.002, (max_lng - min_lng) * 0.12)
    min_lat -= pad_lat
    max_lat += pad_lat
    min_lng -= pad_lng
    max_lng += pad_lng

    def xy(lat: float, lng: float) -> Tuple[float, float]:
        x = 40 + (lng - min_lng) / (max_lng - min_lng) * 820
        y = 520 - (lat - min_lat) / (max_lat - min_lat) * 460
        return x, y

    coords = [xy(lat, lng) for lat, lng, _ in points]
    route_paths = []
    for route_id in sorted({item.route_id for item in plan}):
        route_items = [item for item in plan if item.route_id == route_id]
        route_points = [coords[0]]
        for item in route_items:
            idx = plan.index(item) + 1
            route_points.append(coords[idx])
        route_points.append(coords[0])
        route_paths.append(" ".join(f"{x:.1f},{y:.1f}" for x, y in route_points))
    marker_html = []
    for idx, ((lat, lng, label), (x, y)) in enumerate(zip(points, coords)):
        is_depot = idx == 0
        fill = "#0f766e" if is_depot else "#f59e0b"
        text = "D" if is_depot else str(idx)
        marker_html.append(
            f"""
<g>
  <circle cx="{x:.1f}" cy="{y:.1f}" r="15" fill="{fill}" stroke="#fff" stroke-width="3" />
  <text x="{x:.1f}" y="{y + 5:.1f}" text-anchor="middle" font-size="13" font-weight="700" fill="#fff">{text}</text>
  <text x="{x + 20:.1f}" y="{y - 10:.1f}" font-size="12" fill="#1f2937">{label}</text>
</g>
"""
        )
    return f"""
<div class="map-wrap">
  <svg viewBox="0 0 900 560" role="img" aria-label="Route map">
    <rect x="0" y="0" width="900" height="560" rx="8" fill="#f8fafc" />
    {''.join(f'<path d="M {path}" fill="none" stroke="#2563eb" stroke-width="4" stroke-linejoin="round" stroke-linecap="round" opacity="0.62" />' for path in route_paths)}
    {''.join(marker_html)}
  </svg>
</div>
"""


def analyze(file_obj, notes: str, use_minicpm: bool):
    stops = parse_orders(file_obj)
    constraints, model_trace = minicpm_parse_dispatch_notes(notes, use_minicpm)
    auto_routes = build_capacity_routes(stops, constraints)
    manual = manual_route(stops)
    auto_plan, auto_metrics = simulate_routes(auto_routes, int(constraints["depot_start"]))
    manual_plan, manual_metrics = simulate_routes([manual], int(constraints["depot_start"]))

    return (
        metrics_markdown(auto_metrics, manual_metrics),
        constraints_markdown(constraints, model_trace),
        route_table(auto_plan),
        route_cards(auto_plan),
        route_map(auto_plan),
    )


CUSTOM_CSS = """
.gradio-container {
  --radius-lg: 8px;
}
.hero {
  min-height: 260px;
  border-radius: 8px;
  padding: 36px;
  background:
    linear-gradient(rgba(9, 47, 44, .72), rgba(9, 47, 44, .62)),
    url('https://images.unsplash.com/photo-1601584115197-04ecc0da31d7?auto=format&fit=crop&w=1600&q=80');
  background-size: cover;
  background-position: center;
  color: white;
  display: flex;
  flex-direction: column;
  justify-content: end;
}
.hero h1 {
  font-size: 42px;
  line-height: 1.05;
  margin: 0 0 10px 0;
  letter-spacing: 0;
}
.hero p {
  max-width: 760px;
  font-size: 16px;
  margin: 0;
}
.badges {
  display: flex;
  flex-wrap: wrap;
  gap: 8px;
  margin-top: 18px;
}
.badge {
  border: 1px solid rgba(255, 255, 255, .42);
  border-radius: 999px;
  padding: 5px 10px;
  color: #f8fafc;
  background: rgba(15, 118, 110, .58);
  font-size: 13px;
  font-weight: 700;
}
.model-note {
  border: 1px solid #d6d3d1;
  border-radius: 8px;
  padding: 12px;
  background: #f8fafc;
  color: #292524;
  font-size: 14px;
}
.route-cards {
  display: grid;
  grid-template-columns: repeat(auto-fit, minmax(260px, 1fr));
  gap: 10px;
}
.route-card {
  border: 1px solid #d6d3d1;
  border-radius: 8px;
  padding: 12px;
  background: #fff;
}
.route-card-top {
  display: flex;
  align-items: center;
  gap: 8px;
}
.route-index {
  display: inline-grid;
  place-items: center;
  width: 26px;
  height: 26px;
  border-radius: 50%;
  background: #0f766e;
  color: white;
  font-weight: 700;
}
.route-title {
  font-weight: 700;
  flex: 1;
}
.route-status {
  border-radius: 999px;
  padding: 3px 8px;
  font-size: 12px;
  background: #dcfce7;
  color: #166534;
}
.route-status.late {
  background: #fee2e2;
  color: #991b1b;
}
.route-meta {
  color: #57534e;
  font-size: 13px;
  margin-top: 8px;
}
.route-note {
  color: #292524;
  font-size: 14px;
  margin-top: 6px;
}
.map-wrap {
  border: 1px solid #d6d3d1;
  border-radius: 8px;
  overflow: hidden;
  background: white;
}
"""


DEFAULT_NOTES = (
    "Start at 8:00. School and clinic stops are urgent. Fresh produce should be "
    "delivered before lunch. Van capacity 18."
)


with gr.Blocks(
    title="Tiny Dispatch Coach",
    css=CUSTOM_CSS,
    theme=gr.themes.Soft(primary_hue="emerald", secondary_hue="amber", neutral_hue="stone"),
) as demo:
    gr.HTML(
        """
<section class="hero">
  <h1>Tiny Dispatch Coach</h1>
  <p>Turn a small delivery sheet and messy dispatcher notes into route plans, tradeoff explanations, and driver-ready cards. Built around OpenBMB MiniCPM5-1B-GGUF plus a deterministic planner.</p>
  <div class="badges">
    <span class="badge">OpenBMB MiniCPM5</span>
    <span class="badge">1.08B params</span>
    <span class="badge">GGUF / llama.cpp</span>
    <span class="badge">No cloud LLM API</span>
    <span class="badge">Synthetic demo data</span>
  </div>
</section>
"""
    )

    with gr.Row():
        with gr.Column(scale=2):
            order_file = gr.File(
                label="Orders CSV",
                file_types=[".csv"],
                type="filepath",
            )
            notes = gr.Textbox(
                label="Dispatcher notes",
                value=DEFAULT_NOTES,
                lines=5,
            )
            use_minicpm = gr.Checkbox(
                label="Use MiniCPM5 parser",
                value=False,
                info="Optional on CPU Basic. Default fast mode keeps the demo responsive.",
            )
            run = gr.Button("Plan route", variant="primary")
        with gr.Column(scale=1):
            gr.HTML(
                f"""
<div class="model-note">
  <strong>Small-model core:</strong><br>
  <code>{MINICPM_REPO}</code><br>
  <code>{MINICPM_FILE}</code><br>
  MiniCPM5 parses human dispatch notes when llama.cpp is available. CPU Basic falls back to the same auditable constraint schema.
</div>
"""
            )
            gr.Markdown(
                """
### CSV columns
`order_id`, `customer`, `lat`, `lng`, `demand`, `service_min`, `ready_time`, `due_time`, `priority`, `notes`, optional `manual_sequence`.

Leave the file empty to run the included sample route.
"""
            )
            gr.Markdown(
                """
### Build Small fit
OpenBMB MiniCPM5, 1.08B parameters, local GGUF path, no cloud LLM API, synthetic sample data, explicit parser trace.
"""
            )

    metrics = gr.Markdown()
    constraints = gr.Markdown(
        "### OpenBMB MiniCPM5 Constraint Parse\nClick **Plan route** to parse notes with MiniCPM5-1B-GGUF when available, or the deterministic fallback on CPU Basic."
    )
    table = gr.Dataframe(label="Optimized route", interactive=False)
    cards = gr.HTML(label="Driver cards")
    map_html = gr.HTML(label="Route map")

    run.click(
        analyze,
        inputs=[order_file, notes, use_minicpm],
        outputs=[metrics, constraints, table, cards, map_html],
    )


if __name__ == "__main__":
    demo.launch()