chore(ci): restore missing src/cost_benefit.py [skip ci]

src/cost_benefit.py

@@ -0,0 +1,48 @@
+import argparse
+import subprocess
+import time
+import requests
+
+def detect_available_budget(runtime_env: str) -> int:
+    """
+    Return an approximate VRAM‑based budget (MB) when running locally,
+    else default to 100.
+    """
+    import torch
+    if "local" in runtime_env and torch.cuda.is_available():
+        total_vram_mb = torch.cuda.get_device_properties(0).total_memory // (1024 ** 2)
+        return min(total_vram_mb, 100)
+    return 100
+
+def get_best_model(runtime_env: str, *, use_local_only: bool = False, use_api_only: bool = False) -> dict:
+    """
+    Pick the fastest model that fits in the detected budget while
+    respecting the locality filters.
+    """
+    static_costs = {
+        "llama3.2":  {"size": 20, "token_cost": 0.0001, "tokens_sec": 30, "type": "local"},
+        "mistral":   {"size": 40, "token_cost": 0.0002, "tokens_sec": 50, "type": "local"},
+        "gemini-2.0-flash":            {"size": 60, "token_cost": 0.0005, "tokens_sec": 60, "type": "api"},
+        "gemini-2.5-pro-preview-03-25": {"size": 80, "token_cost": 0.002 , "tokens_sec": 45, "type": "api"},
+    }
+
+    budget = detect_available_budget(runtime_env)
+    best_model, best_speed = None, -1
+
+    for model, info in static_costs.items():
+        if info["size"] > budget:
+            continue
+        if use_local_only and info["type"] != "local":
+            continue
+        if use_api_only and info["type"] != "api":
+            continue
+        if info["tokens_sec"] > best_speed:
+            best_model, best_speed = model, info["tokens_sec"]
+
+    chosen = best_model or "llama3.2"  # sensible default
+    return {
+        "model": chosen,
+        "token_cost": static_costs[chosen]["token_cost"],
+        "tokens_sec": static_costs[chosen]["tokens_sec"],
+        "note": None if best_model else "Defaulted because no model met the constraints",
+    }