Spaces:
Runtime error
Runtime error
File size: 19,295 Bytes
621ec47 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 | """
Hugging Face API Client
Provides methods for interacting with HuggingFace Inference API
"""
import os
import requests
from typing import Optional, List, Dict, Any
from huggingface_hub import InferenceClient, HfApi
from utils import load_settings
# Settings paths
SETTINGS_DIR = os.path.join(os.path.dirname(__file__), 'settings')
APP_SETTINGS_FILE = os.path.join(SETTINGS_DIR, 'app.json')
# Get HF token from settings
HF_TOKEN = load_settings(APP_SETTINGS_FILE).get('hf_token')
API_BASE = "https://api-inference.huggingface.co"
class HuggingFaceAPI:
def __init__(self, token: str = HF_TOKEN):
self.token = token
self.headers = {
"Authorization": f"Bearer {token}",
"Content-Type": "application/json"
}
self.client = InferenceClient(token=token)
self.hf_api = HfApi(token=token)
def model_info(self, model_id: str):
"""Get model info using HfApi (compatible with hf.py)"""
return self.hf_api.model_info(model_id)
def list_models(self, **kwargs):
"""List models using HfApi (compatible with hf.py)"""
return self.hf_api.list_models(**kwargs)
def chat_completion(
self,
model: str,
messages: List[Dict[str, str]],
max_tokens: int = 500,
temperature: float = 0.7,
stream: bool = False
) -> Dict[str, Any]:
"""
Send a chat completion request to HuggingFace API using huggingface_hub.
Args:
model: Model ID (e.g., "meta-llama/Llama-3.2-3B-Instruct")
messages: List of message dicts with 'role' and 'content'
max_tokens: Maximum tokens to generate
temperature: Sampling temperature (0.0 - 1.0)
stream: Whether to stream the response
Returns:
API response as dict
"""
# Validate model before use
validation_result = self.validate_model(model)
if not validation_result["valid"]:
# Try fallback models
fallback_models = validation_result.get("fallback_models", [])
if fallback_models:
# Use the first fallback model
fallback_model = fallback_models[0]["id"]
print(f"Warning: Model {model} not supported. Using fallback model {fallback_model}")
model = fallback_model
else:
raise ValueError(f"Model {model} is not supported and no fallback models available. "
f"Error: {validation_result.get('error', 'Unknown error')}")
try:
response = self.client.chat_completion(
model=model,
messages=messages,
max_tokens=max_tokens,
temperature=temperature,
stream=stream
)
except Exception as e:
error_str = str(e).lower()
if "model_not_supported" in error_str or "not supported by any provider" in error_str:
# Try fallback models
fallback_models = self._find_fallback_models(model)
if fallback_models:
# Try each fallback model
for fallback in fallback_models[:3]:
try:
print(f"Trying fallback model: {fallback['id']}")
response = self.client.chat_completion(
model=fallback['id'],
messages=messages,
max_tokens=max_tokens,
temperature=temperature,
stream=stream
)
return response
except:
continue
raise ValueError(f"Model {model} is not supported and all fallback models failed. "
f"Try one of these: {', '.join([m['id'] for m in fallback_models[:3]])}")
else:
raise ValueError(f"Model {model} is not supported and no fallback models available.")
else:
raise e
# Convert to dict format
return {
"choices": [{
"message": {
"role": "assistant",
"content": response.choices[0].message.content
},
"finish_reason": response.choices[0].finish_reason
}],
"model": model,
"usage": {
"prompt_tokens": getattr(response.usage, "prompt_tokens", 0),
"completion_tokens": getattr(response.usage, "completion_tokens", 0),
"total_tokens": getattr(response.usage, "total_tokens", 0)
} if response.usage else None
}
def validate_model(self, model_id: str) -> Dict[str, Any]:
"""
Validate if a model is supported and available.
Args:
model_id: Model ID to validate
Returns:
Validation result with status and fallback suggestions
"""
try:
# Try to get model info
model_info = self.hf_api.model_info(model_id)
# Check if model has inference API enabled
if hasattr(model_info, 'inference') and not model_info.inference:
# Try to find alternative models
fallback_models = self._find_fallback_models(model_id)
return {
"valid": False,
"error": f"Model {model_id} does not have inference API enabled",
"fallback_models": fallback_models,
"model_info": model_info
}
return {
"valid": True,
"model_info": model_info
}
except Exception as e:
# Check if it's an auth error
error_str = str(e).lower()
if "401" in error_str or "unauthorized" in error_str or "invalid username or password" in error_str:
# Auth error - model might be valid but we can't check
return {
"valid": True, # Assume valid since we can't verify due to auth
"warning": "Unable to verify model due to authentication. Assuming model is valid.",
"auth_error": True
}
# Model not found or not supported
fallback_models = self._find_fallback_models(model_id)
return {
"valid": False,
"error": str(e),
"fallback_models": fallback_models
}
def _find_fallback_models(self, model_id: str) -> List[Dict[str, str]]:
"""
Find fallback models similar to the requested model.
Args:
model_id: Original model ID
Returns:
List of fallback model suggestions
"""
# Extract model name parts
model_parts = model_id.lower().split('/')
if len(model_parts) > 1:
model_name = model_parts[-1]
else:
model_name = model_id.lower()
# Remove version numbers and common prefixes
clean_name = model_name.replace('-3b', '').replace('-8b', '').replace('-70b', '')
clean_name = clean_name.replace('llama', '').replace('hermes', '').strip('-')
# Search for similar models
try:
# Search for models with similar names
similar_models = self.hf_api.list_models(
search=model_name,
sort="downloads",
direction=-1,
limit=5
)
# Filter for text generation models
fallbacks = []
for model in similar_models:
if (hasattr(model, 'pipeline_tag') and
model.pipeline_tag in ['text-generation', 'conversational', 'translation']):
fallbacks.append({
"id": model.modelId,
"name": getattr(model, 'author', '') + '/' + model.modelId.split('/')[-1],
"downloads": getattr(model, 'downloads', 0)
})
return fallbacks[:5] # Return top 5 fallbacks
except:
# If search fails, return some common models including translation models
return [
{"id": "meta-llama/Llama-3.2-3B-Instruct", "name": "Llama 3.2 3B", "downloads": 0},
{"id": "microsoft/Phi-3-mini-4k-instruct", "name": "Phi-3 Mini", "downloads": 0},
{"id": "google/gemma-2-2b-it", "name": "Gemma 2 2B", "downloads": 0},
{"id": "Helsinki-NLP/opus-mt-en-es", "name": "English-Spanish Translator", "downloads": 0},
{"id": "Helsinki-NLP/opus-mt-en-fr", "name": "English-French Translator", "downloads": 0}
]
def get_model_task_support(self, model: str) -> Dict[str, Any]:
"""
Get information about what tasks a model supports.
Args:
model: Model ID
Returns:
Model task support information
"""
# Known conversational-only models
conversational_only_models = [
"meta-llama/Llama-3.2-3B-Instruct",
"meta-llama/Llama-3.1-8B-Instruct",
"meta-llama/Llama-3.1-70B-Instruct"
]
if model in conversational_only_models:
return {
"supports_text_generation": False,
"supports_conversational": True,
"recommended_method": "chat_completion"
}
else:
return {
"supports_text_generation": True,
"supports_conversational": True,
"recommended_method": "text_generation_or_chat_completion"
}
def text_generation(
self,
model: str,
prompt: str,
max_new_tokens: int = 250,
temperature: float = 0.7,
top_p: float = 0.95,
do_sample: bool = True
) -> Dict[str, Any]:
"""
Send a text generation request to HuggingFace API.
Args:
model: Model ID
prompt: Text prompt to complete
max_new_tokens: Maximum new tokens to generate
temperature: Sampling temperature
top_p: Nucleus sampling parameter
do_sample: Whether to use sampling
Returns:
API response as dict
"""
# Validate model before use
validation_result = self.validate_model(model)
if not validation_result["valid"]:
# Try fallback models
fallback_models = validation_result.get("fallback_models", [])
if fallback_models:
# Use the first fallback model
fallback_model = fallback_models[0]["id"]
print(f"Warning: Model {model} not supported. Using fallback model {fallback_model}")
model = fallback_model
else:
raise ValueError(f"Model {model} is not supported and no fallback models available. "
f"Error: {validation_result.get('error', 'Unknown error')}")
try:
response = self.client.text_generation(
model=model,
prompt=prompt,
max_new_tokens=max_new_tokens,
temperature=temperature,
top_p=top_p,
do_sample=do_sample
)
return {"generated_text": response}
except Exception as e:
# Check if the error is related to unsupported task
error_str = str(e).lower()
if "not supported for task text-generation" in error_str:
raise ValueError(f"Model {model} is not supported for text-generation task. "
f"This model only supports conversational tasks. "
f"Please use chat_completion method instead.")
elif "model_not_supported" in error_str or "not supported by any provider" in error_str:
# Try fallback models
fallback_models = self._find_fallback_models(model)
if fallback_models:
# Try each fallback model
for fallback in fallback_models[:3]:
try:
print(f"Trying fallback model: {fallback['id']}")
response = self.client.text_generation(
model=fallback['id'],
prompt=prompt,
max_new_tokens=max_new_tokens,
temperature=temperature,
top_p=top_p,
do_sample=do_sample
)
return {"generated_text": response}
except:
continue
raise ValueError(f"Model {model} is not supported and all fallback models failed. "
f"Try one of these: {', '.join([m['id'] for m in fallback_models[:3]])}")
else:
raise ValueError(f"Model {model} is not supported and no fallback models available.")
else:
raise e
def get_model_info(self, model: str) -> Dict[str, Any]:
"""
Get model information from HuggingFace Hub.
Args:
model: Model ID
Returns:
Model metadata dict
"""
url = f"https://huggingface.co/api/models/{model}"
response = requests.get(url, headers=self.headers)
response.raise_for_status()
return response.json()
def search_models(
self,
query: str,
task: str = "text-generation",
limit: int = 10
) -> List[Dict[str, Any]]:
"""
Search for models on HuggingFace Hub.
Args:
query: Search query
task: Filter by task (e.g., "text-generation", "text-classification")
limit: Maximum number of results
Returns:
List of model metadata dicts
"""
url = "https://huggingface.co/api/models"
params = {
"search": query,
"pipeline_tag": task,
"limit": limit,
"sort": "downloads",
"direction": -1
}
response = requests.get(url, headers=self.headers, params=params)
response.raise_for_status()
return response.json()
def image_generation(
self,
model: str,
prompt: str,
negative_prompt: Optional[str] = None,
num_inference_steps: int = 50
) -> bytes:
"""
Generate an image using a diffusion model.
Args:
model: Model ID (e.g., "stabilityai/stable-diffusion-xl-base-1.0")
prompt: Text prompt for image generation
negative_prompt: Negative prompt (what to avoid)
num_inference_steps: Number of denoising steps
Returns:
Image bytes
"""
url = f"{API_BASE}/models/{model}"
payload = {
"inputs": prompt,
"parameters": {
"num_inference_steps": num_inference_steps
}
}
if negative_prompt:
payload["parameters"]["negative_prompt"] = negative_prompt
response = requests.post(url, headers=self.headers, json=payload)
response.raise_for_status()
return response.content
def embedding(
self,
model: str,
texts: List[str]
) -> List[List[float]]:
"""
Get embeddings for texts.
Args:
model: Model ID (e.g., "sentence-transformers/all-MiniLM-L6-v2")
texts: List of texts to embed
Returns:
List of embedding vectors
"""
url = f"{API_BASE}/models/{model}"
payload = {
"inputs": texts
}
response = requests.post(url, headers=self.headers, json=payload)
response.raise_for_status()
return response.json()
def summarization(
self,
model: str,
text: str,
max_length: int = 150,
min_length: int = 30
) -> Dict[str, Any]:
"""
Summarize text using a summarization model.
Args:
model: Model ID (e.g., "facebook/bart-large-cnn")
text: Text to summarize
max_length: Maximum summary length
min_length: Minimum summary length
Returns:
API response with summary
"""
url = f"{API_BASE}/models/{model}"
payload = {
"inputs": text,
"parameters": {
"max_length": max_length,
"min_length": min_length
}
}
response = requests.post(url, headers=self.headers, json=payload)
response.raise_for_status()
return response.json()
def translation(
self,
model: str,
text: str
) -> Dict[str, Any]:
url = f"{API_BASE}/models/{model}"
payload = {
"inputs": text
}
response = requests.post(url, headers=self.headers, json=payload)
response.raise_for_status()
return response.json()
def question_answering(
self,
model: str,
question: str,
context: str
) -> Dict[str, Any]:
"""
Answer a question based on context.
Args:
model: Model ID (e.g., "deepset/roberta-base-squad2")
question: The question to answer
context: Context containing the answer
Returns:
API response with answer
"""
url = f"{API_BASE}/models/{model}"
payload = {
"inputs": {
"question": question,
"context": context
}
}
response = requests.post(url, headers=self.headers, json=payload)
response.raise_for_status()
return response.json()
|