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f0d9a3e | 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 | """MCP Server implementation for Flux Seamless Texture LoRA."""
import json
import logging
from typing import Any, Dict, List, Optional
from pathlib import Path
from datetime import datetime
from config.settings import MCP_ENABLED, EXAMPLES_DIR
from src.model_handler import ModelHandler
from src.presets import list_presets, get_preset, get_preset_prompt, get_preset_params
from src.image_processor import load_metadata, OUTPUT_DIR
logger = logging.getLogger(__name__)
class MCPServer:
"""MCP Server for exposing the Space via Model Context Protocol."""
def __init__(self, model_handler: ModelHandler):
"""Initialize the MCP server.
Args:
model_handler: Instance of ModelHandler.
"""
self.model_handler = model_handler
self.history: List[Dict[str, Any]] = []
# Tools (Ferramentas)
def tool_generate_texture(
self,
prompt: str,
negative_prompt: str = "",
guidance_scale: float = 7.5,
num_inference_steps: int = 50,
seed: Optional[int] = None,
width: int = 1024,
height: int = 1024,
) -> Dict[str, Any]:
"""Generate a texture image.
Args:
prompt: Text prompt for generation.
negative_prompt: Negative prompt.
guidance_scale: Guidance scale.
num_inference_steps: Number of inference steps.
seed: Random seed.
width: Image width.
height: Image height.
Returns:
Dictionary with image path and metadata.
"""
try:
image, metadata = self.model_handler.generate(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
seed=seed,
width=width,
height=height,
)
# Add to history
history_entry = {
"timestamp": datetime.now().timestamp(),
"prompt": prompt,
"params": {
"negative_prompt": negative_prompt,
"guidance_scale": guidance_scale,
"num_inference_steps": num_inference_steps,
"seed": seed,
"width": width,
"height": height,
},
"image_path": metadata.get("image_path"),
}
self.history.append(history_entry)
return {
"success": True,
"image_path": metadata.get("image_path"),
"seed": metadata.get("seed"),
"message": "Texture generated successfully",
}
except Exception as e:
logger.error(f"Error in generate_texture: {e}")
return {
"success": False,
"error": str(e),
}
def tool_generate_batch_textures(
self,
prompts: List[str],
guidance_scale: float = 7.5,
num_inference_steps: int = 50,
) -> Dict[str, Any]:
"""Generate multiple textures in batch.
Args:
prompts: List of prompts.
guidance_scale: Guidance scale.
num_inference_steps: Number of inference steps.
Returns:
Dictionary with results.
"""
try:
base_params = {
"guidance_scale": guidance_scale,
"num_inference_steps": num_inference_steps,
}
results = []
for image, metadata, idx in self.model_handler.generate_batch(prompts, base_params):
if image is not None:
results.append({
"index": idx,
"success": True,
"image_path": metadata.get("image_path"),
})
else:
results.append({
"index": idx,
"success": False,
"error": metadata.get("error"),
})
return {
"success": True,
"total": len(prompts),
"results": results,
}
except Exception as e:
logger.error(f"Error in generate_batch_textures: {e}")
return {
"success": False,
"error": str(e),
}
def tool_get_presets(self) -> Dict[str, Any]:
"""Get list of available presets.
Returns:
Dictionary with preset names and details.
"""
presets = list_presets()
preset_details = {}
for name in presets:
preset = get_preset(name)
if preset:
preset_details[name] = {
"prompt": preset.get("prompt"),
"guidance_scale": preset.get("guidance_scale"),
"num_inference_steps": preset.get("num_inference_steps"),
}
return {
"presets": presets,
"details": preset_details,
}
def tool_get_history(self, limit: int = 10) -> Dict[str, Any]:
"""Get generation history.
Args:
limit: Maximum number of entries to return.
Returns:
Dictionary with history entries.
"""
recent = self.history[-limit:] if len(self.history) > limit else self.history
return {
"total": len(self.history),
"entries": recent,
}
def tool_download_image(self, image_path: str) -> Dict[str, Any]:
"""Get information about an image for download.
Args:
image_path: Path to the image.
Returns:
Dictionary with image information.
"""
path = Path(image_path)
if not path.exists():
return {
"success": False,
"error": "Image not found",
}
metadata = load_metadata(path)
return {
"success": True,
"image_path": str(path),
"metadata": metadata,
}
# Resources (Recursos)
def resource_presets(self) -> str:
"""Get presets as a resource.
Returns:
JSON string of presets.
"""
presets = {}
for name in list_presets():
preset = get_preset(name)
if preset:
presets[name] = preset
return json.dumps(presets, indent=2)
def resource_history(self) -> str:
"""Get history as a resource.
Returns:
JSON string of history.
"""
return json.dumps(self.history, indent=2)
def resource_examples(self) -> str:
"""Get examples as a resource.
Returns:
JSON string of example information.
"""
examples = []
if EXAMPLES_DIR.exists():
for img_file in EXAMPLES_DIR.glob("*.{png,jpg,jpeg}"):
examples.append({
"filename": img_file.name,
"path": str(img_file),
})
return json.dumps(examples, indent=2)
def create_mcp_server(model_handler: ModelHandler) -> MCPServer:
"""Create an MCP server instance.
Args:
model_handler: Instance of ModelHandler.
Returns:
MCPServer instance.
"""
return MCPServer(model_handler)
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