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This commit is contained in:
Aaron Crate
2026-06-26 19:24:36 -05:00
parent 284e4f92fe
commit dbbfbd638f
6 changed files with 176 additions and 75 deletions
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5
garfpy/aod.py
View File

@@ -121,7 +121,7 @@ async def aod_message(garfbot, message):
for field, values in zip(table_fields, table_columns):
stats_embed.add_field(name=field, value="\n".join(values), inline=True)
await message.channel.send(embed=stats_embed)
# # Boy You Said It
# words = re.findall(r"[a-zA-Z']+", message.content.lower())
# stops = {"a", "an", "the", "and", "or", "but", "is", "it", "in", "on", "at", "to", "of"}
@@ -131,7 +131,6 @@ async def aod_message(garfbot, message):
# firsts = [w[0] for w in words]
# commons = max(set(firsts), key=firsts.count)
# count = firsts.count(commons)
# if count >= 3 or (len(words) >= 2 and count / len(words) >= 0.75):
# await message.channel.send("Boy, you said it!")

171
garfpy/garfai.py
View File

@@ -13,13 +13,31 @@ from garfpy import logger
INVOKEAI_BASE = config.INVOKEAI_URL
_MODEL_KEY = "0eb50094-5c9b-431b-ba01-87e145edb849"
_VAE_KEY = "dde3627c-8a45-4088-93d1-66c44acbb337"
_MODEL_KEY = "0eb50094-5c9b-431b-ba01-87e145edb849"
_VAE_KEY = "dde3627c-8a45-4088-93d1-66c44acbb337"
_ENCODER_KEY = "7ba22542-4687-4946-a52e-c92f925f4b75"
_MODEL_REF = {"key": _MODEL_KEY, "hash": "blake3:c3ee838d71d99497db01fae6f304eafd9e734e935f3b783e968d50febb56be2c", "name": "FLUX.2 Klein 4B (GGUF Q4)", "base": "flux2", "type": "main"}
_VAE_REF = {"key": _VAE_KEY, "hash": "blake3:531855de70db993d0f6181f82cde27d15411d58b7ffa3b2fdce2b9434c0173c2", "name": "FLUX.2 VAE", "base": "flux2", "type": "vae"}
_ENCODER_REF = {"key": _ENCODER_KEY, "hash": "blake3:af5840e6770dc99f678e69867949c8b9264835915eb82a990e940fa6e4fa6c81", "name": "FLUX.2 Klein Qwen3 4B Encoder", "base": "any", "type": "qwen3_encoder"}
_MODEL_REF = {
"key": _MODEL_KEY,
"hash": "blake3:c3ee838d71d99497db01fae6f304eafd9e734e935f3b783e968d50febb56be2c",
"name": "FLUX.2 Klein 4B (GGUF Q4)",
"base": "flux2",
"type": "main",
}
_VAE_REF = {
"key": _VAE_KEY,
"hash": "blake3:531855de70db993d0f6181f82cde27d15411d58b7ffa3b2fdce2b9434c0173c2",
"name": "FLUX.2 VAE",
"base": "flux2",
"type": "vae",
}
_ENCODER_REF = {
"key": _ENCODER_KEY,
"hash": "blake3:af5840e6770dc99f678e69867949c8b9264835915eb82a990e940fa6e4fa6c81",
"name": "FLUX.2 Klein Qwen3 4B Encoder",
"base": "any",
"type": "qwen3_encoder",
}
_POLL_INTERVAL = 2
_POLL_ATTEMPTS = 60
@@ -31,49 +49,103 @@ def _node_id(prefix: str) -> str:
def _build_graph(prompt: str) -> dict:
seed = int(time.time() * 1000) % (2 ** 31)
seed = int(time.time() * 1000) % (2**31)
p = _node_id("positive_prompt")
ml = _node_id("flux2_klein_model_loader")
te = _node_id("flux2_klein_text_encoder")
dn = _node_id("flux2_denoise")
p = _node_id("positive_prompt")
ml = _node_id("flux2_klein_model_loader")
te = _node_id("flux2_klein_text_encoder")
dn = _node_id("flux2_denoise")
out = _node_id("canvas_output")
nodes = {
p: {"id": p, "is_intermediate": True, "use_cache": True, "value": prompt, "type": "string"},
ml: {"id": ml, "is_intermediate": True, "use_cache": True, "type": "flux2_klein_model_loader",
"model": _MODEL_REF, "vae_model": _VAE_REF, "qwen3_encoder_model": _ENCODER_REF},
te: {"id": te, "is_intermediate": True, "use_cache": True, "type": "flux2_klein_text_encoder"},
dn: {"id": dn, "is_intermediate": True, "use_cache": True, "type": "flux2_denoise", "seed": seed},
out: {"id": out, "is_intermediate": False, "use_cache": False, "type": "flux2_vae_decode"},
p: {
"id": p,
"is_intermediate": True,
"use_cache": True,
"value": prompt,
"type": "string",
},
ml: {
"id": ml,
"is_intermediate": True,
"use_cache": True,
"type": "flux2_klein_model_loader",
"model": _MODEL_REF,
"vae_model": _VAE_REF,
"qwen3_encoder_model": _ENCODER_REF,
},
te: {
"id": te,
"is_intermediate": True,
"use_cache": True,
"type": "flux2_klein_text_encoder",
},
dn: {
"id": dn,
"is_intermediate": True,
"use_cache": True,
"type": "flux2_denoise",
"seed": seed,
},
out: {
"id": out,
"is_intermediate": False,
"use_cache": False,
"type": "flux2_vae_decode",
},
}
edges = [
{"source": {"node_id": ml, "field": "qwen3_encoder"}, "destination": {"node_id": te, "field": "qwen3_encoder"}},
{"source": {"node_id": ml, "field": "max_seq_len"}, "destination": {"node_id": te, "field": "max_seq_len"}},
{"source": {"node_id": p, "field": "value"}, "destination": {"node_id": te, "field": "prompt"}},
{"source": {"node_id": ml, "field": "transformer"}, "destination": {"node_id": dn, "field": "transformer"}},
{"source": {"node_id": ml, "field": "vae"}, "destination": {"node_id": dn, "field": "vae"}},
{"source": {"node_id": te, "field": "conditioning"}, "destination": {"node_id": dn, "field": "positive_text_conditioning"}},
{"source": {"node_id": ml, "field": "vae"}, "destination": {"node_id": out, "field": "vae"}},
{"source": {"node_id": dn, "field": "latents"}, "destination": {"node_id": out, "field": "latents"}},
{
"source": {"node_id": ml, "field": "qwen3_encoder"},
"destination": {"node_id": te, "field": "qwen3_encoder"},
},
{
"source": {"node_id": ml, "field": "max_seq_len"},
"destination": {"node_id": te, "field": "max_seq_len"},
},
{
"source": {"node_id": p, "field": "value"},
"destination": {"node_id": te, "field": "prompt"},
},
{
"source": {"node_id": ml, "field": "transformer"},
"destination": {"node_id": dn, "field": "transformer"},
},
{
"source": {"node_id": ml, "field": "vae"},
"destination": {"node_id": dn, "field": "vae"},
},
{
"source": {"node_id": te, "field": "conditioning"},
"destination": {"node_id": dn, "field": "positive_text_conditioning"},
},
{
"source": {"node_id": ml, "field": "vae"},
"destination": {"node_id": out, "field": "vae"},
},
{
"source": {"node_id": dn, "field": "latents"},
"destination": {"node_id": out, "field": "latents"},
},
]
return {"nodes": nodes, "edges": edges}
async def _poll_batch(session: aiohttp.ClientSession, base: str, batch_id: str) -> bool:
"""Poll batch status until completed, failed, or timed out. Returns True on success."""
for _ in range(_POLL_ATTEMPTS):
await asyncio.sleep(_POLL_INTERVAL)
try:
async with session.get(f"{base}/api/v1/queue/default/b/{batch_id}/status") as resp:
async with session.get(
f"{base}/api/v1/queue/default/b/{batch_id}/status"
) as resp:
if not resp.ok:
continue
s = await resp.json(content_type=None)
total = s.get("total", 0)
total = s.get("total", 0)
completed = s.get("completed", 0)
failed = s.get("failed", 0)
failed = s.get("failed", 0)
if total > 0 and failed >= total:
logger.error(f"Batch {batch_id} failed")
return False
@@ -84,7 +156,9 @@ async def _poll_batch(session: aiohttp.ClientSession, base: str, batch_id: str)
return False
async def _get_image_name(session: aiohttp.ClientSession, base: str, batch_id: str) -> str | None:
async def _get_image_name(
session: aiohttp.ClientSession, base: str, batch_id: str
) -> str | None:
try:
async with session.get(f"{base}/api/v1/queue/default/i/{batch_id}") as resp:
if resp.ok:
@@ -110,8 +184,9 @@ async def _get_image_name(session: aiohttp.ClientSession, base: str, batch_id: s
return None
async def _fetch_image_bytes(session: aiohttp.ClientSession, base: str, name: str) -> bytes | None:
"""Try the full image endpoint, then fall back to thumbnail."""
async def _fetch_image_bytes(
session: aiohttp.ClientSession, base: str, name: str
) -> bytes | None:
urls = [
f"{base}/api/v1/images/i/{name}/full",
f"{base}/api/v1/images/i/{name}/thumbnail",
@@ -119,9 +194,11 @@ async def _fetch_image_bytes(session: aiohttp.ClientSession, base: str, name: st
for url in urls:
try:
async with session.get(url) as resp:
ct = resp.headers.get("Content-Type", "")
ct = resp.headers.get("Content-Type", "")
data = await resp.read()
logger.info(f"Image fetch {url}: status={resp.status} content-type={ct} size={len(data)}")
logger.info(
f"Image fetch {url}: status={resp.status} content-type={ct} size={len(data)}"
)
if "html" not in ct and len(data) >= _MIN_IMAGE_BYTES:
return data
except Exception as e:
@@ -131,10 +208,10 @@ async def _fetch_image_bytes(session: aiohttp.ClientSession, base: str, name: st
class GarfAI:
def __init__(self):
self.baseurl = config.BASE_URL
self.baseurl = config.BASE_URL
self.sysprompt = config.SYSTEM_PROMPT
self.txtmodel = config.TXT_MODEL
self.imgmodel = config.IMG_MODEL
self.txtmodel = config.TXT_MODEL
self.imgmodel = config.IMG_MODEL
self._oai = AsyncOpenAI(
api_key=config.OPENAI_TOKEN,
@@ -145,7 +222,9 @@ class GarfAI:
async def garfpic(self, ctx, prompt):
await self.image_request_queue.put({"ctx": ctx, "prompt": prompt})
async def generate_image(self, session: aiohttp.ClientSession, prompt: str) -> bytes | str:
async def generate_image(
self, session: aiohttp.ClientSession, prompt: str
) -> bytes | str:
base = INVOKEAI_BASE
try:
@@ -157,7 +236,7 @@ class GarfAI:
text = await resp.text()
logger.error(f"InvokeAI enqueue failed {resp.status}: {text}")
return "`GarfBot Error: InvokeAI rejected the request`"
data = await resp.json(content_type=None)
data = await resp.json(content_type=None)
batch_id = data["batch"]["batch_id"]
except Exception as e:
logger.error(f"InvokeAI enqueue error: {e}")
@@ -182,19 +261,21 @@ class GarfAI:
return "`GarfBot Error: Odie`"
async def process_image_requests(self):
async with aiohttp.ClientSession(headers={"Accept": "application/json"}) as session:
async with aiohttp.ClientSession(
headers={"Accept": "application/json"}
) as session:
while True:
request = await self.image_request_queue.get()
ctx = request["ctx"]
ctx = request["ctx"]
prompt = request["prompt"]
result = await self.generate_image(session, prompt)
if isinstance(result, bytes):
logger.info("Sending image...")
image = io.BytesIO(result)
image = io.BytesIO(result)
timestamp = ctx.message.created_at.strftime("%Y%m%d%H%M%S")
filename = f"{timestamp}_generated_image.png"
filename = f"{timestamp}_generated_image.png"
try:
await ctx.reply(file=discord.File(fp=image, filename=filename))
except Exception as e:
@@ -211,7 +292,7 @@ class GarfAI:
model=self.txtmodel,
messages=[
{"role": "system", "content": self.sysprompt},
{"role": "user", "content": question},
{"role": "user", "content": question},
],
max_tokens=400,
temperature=1.2,
@@ -231,7 +312,9 @@ class GarfAI:
async def wikisum(self, query: str) -> str:
try:
summary = wikipedia.summary(query)
return await self.generate_chat(f"Please summarize in your own words: {summary}")
return await self.generate_chat(
f"Please summarize in your own words: {summary}"
)
except wikipedia.exceptions.DisambiguationError as e:
options = ", ".join(e.options[:3])
return f"`GarfBot Error: Ambiguous query — did you mean: {options}?`"

4
garfpy/qr.py
View File

@@ -47,7 +47,7 @@ async def generate_qr(text):
qr = qrcode.QRCode(
version=version,
error_correction=qrcode.constants.ERROR_CORRECT_L, # type: ignore
error_correction=qrcode.constants.ERROR_CORRECT_L, # type: ignore
box_size=box_size,
border=4,
)
@@ -58,7 +58,7 @@ async def generate_qr(text):
qr_image = qr.make_image(fill_color="black", back_color="white")
img_buffer = BytesIO()
qr_image.save(img_buffer, format="PNG") # type: ignore
qr_image.save(img_buffer, format="PNG") # type: ignore
img_buffer.seek(0)
return img_buffer