deleting cost_benefit.py

src/cost_benefit.py

@@ -1,48 +0,0 @@
-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",
-    }