Spaces:
Running
Running
File size: 12,031 Bytes
9314c03 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Model configuration and catalog for Warp API
Contains model definitions, configurations, and OpenAI compatibility mappings.
"""
import time
def get_model_config(model_name: str) -> dict:
"""
Simple model configuration mapping.
All models use the same pattern: base model + o3 planning + auto coding
"""
# Known models that map directly
known_models = {
"claude-4-sonnet", "claude-4-opus", "claude-4.1-opus",
"gpt-5", "gpt-4o", "gpt-4.1", "o3", "o4-mini",
"gemini-2.5-pro", "warp-basic"
}
model_name = model_name.lower().strip()
# Use the model name directly if it's known, otherwise use "auto"
base_model = model_name if model_name in known_models else "auto"
return {
"base": base_model,
"planning": "o3",
"coding": "auto"
}
def get_warp_models():
"""Get comprehensive list of Warp AI models from packet analysis"""
return {
"agent_mode": {
"default": "auto",
"models": [
{
"id": "auto",
"display_name": "auto",
"description": "claude 4 sonnet",
"vision_supported": True,
"usage_multiplier": 1,
"category": "agent"
},
{
"id": "warp-basic",
"display_name": "lite",
"description": "basic model",
"vision_supported": True,
"usage_multiplier": 1,
"category": "agent"
},
{
"id": "gpt-5",
"display_name": "gpt-5",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "agent"
},
{
"id": "claude-4-sonnet",
"display_name": "claude 4 sonnet",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "agent"
},
{
"id": "claude-4-opus",
"display_name": "claude 4 opus",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "agent"
},
{
"id": "claude-4.1-opus",
"display_name": "claude 4.1 opus",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "agent"
},
{
"id": "gpt-4o",
"display_name": "gpt-4o",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "agent"
},
{
"id": "gpt-4.1",
"display_name": "gpt-4.1",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "agent"
},
{
"id": "o4-mini",
"display_name": "o4-mini",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "agent"
},
{
"id": "o3",
"display_name": "o3",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "agent"
},
{
"id": "gemini-2.5-pro",
"display_name": "gemini 2.5 pro",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "agent"
}
]
},
"planning": {
"default": "o3",
"models": [
{
"id": "warp-basic",
"display_name": "lite",
"description": "basic model",
"vision_supported": True,
"usage_multiplier": 1,
"category": "planning"
},
{
"id": "gpt-5 (high reasoning)",
"display_name": "gpt-5",
"description": "high reasoning",
"vision_supported": False,
"usage_multiplier": 1,
"category": "planning"
},
{
"id": "claude-4-opus",
"display_name": "claude 4 opus",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "planning"
},
{
"id": "claude-4.1-opus",
"display_name": "claude 4.1 opus",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "planning"
},
{
"id": "gpt-4.1",
"display_name": "gpt-4.1",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "planning"
},
{
"id": "o4-mini",
"display_name": "o4-mini",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "planning"
},
{
"id": "o3",
"display_name": "o3",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "planning"
}
]
},
"coding": {
"default": "auto",
"models": [
{
"id": "auto",
"display_name": "auto",
"description": "claude 4 sonnet",
"vision_supported": True,
"usage_multiplier": 1,
"category": "coding"
},
{
"id": "warp-basic",
"display_name": "lite",
"description": "basic model",
"vision_supported": True,
"usage_multiplier": 1,
"category": "coding"
},
{
"id": "gpt-5",
"display_name": "gpt-5",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "coding"
},
{
"id": "claude-4-sonnet",
"display_name": "claude 4 sonnet",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "coding"
},
{
"id": "claude-4-opus",
"display_name": "claude 4 opus",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "coding"
},
{
"id": "claude-4.1-opus",
"display_name": "claude 4.1 opus",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "coding"
},
{
"id": "gpt-4o",
"display_name": "gpt-4o",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "coding"
},
{
"id": "gpt-4.1",
"display_name": "gpt-4.1",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "coding"
},
{
"id": "o4-mini",
"display_name": "o4-mini",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "coding"
},
{
"id": "o3",
"display_name": "o3",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "coding"
},
{
"id": "gemini-2.5-pro",
"display_name": "gemini 2.5 pro",
"description": None,
"vision_supported": True,
"usage_multiplier": 1,
"category": "coding"
}
]
}
}
def get_all_unique_models():
"""Get all unique models across all categories for OpenAI API compatibility"""
try:
models_data = get_warp_models()
unique_models = {}
# Collect all unique models across categories
for category_data in models_data.values():
for model in category_data["models"]:
model_id = model["id"]
if model_id not in unique_models:
# Create OpenAI-compatible model entry
unique_models[model_id] = {
"id": model_id,
"object": "model",
"created": int(time.time()),
"owned_by": "warp",
"display_name": model["display_name"],
"description": model["description"] or model["display_name"],
"vision_supported": model["vision_supported"],
"usage_multiplier": model["usage_multiplier"],
"categories": [model["category"]]
}
else:
# Add category if model appears in multiple categories
if model["category"] not in unique_models[model_id]["categories"]:
unique_models[model_id]["categories"].append(model["category"])
return list(unique_models.values())
except Exception:
# Fallback to simple model list
return [
{
"id": "auto",
"object": "model",
"created": int(time.time()),
"owned_by": "warp",
"display_name": "auto",
"description": "Auto-select best model"
}
] |