Port meme acquisition pipeline to new API, database

Also fix a really stupid oversight in crawling code.

meme-rater/crawler.py

@@ -55,6 +55,11 @@ filetypes = {
     "image/webp": "webp",
     "image/avif": "avif"
 }
+hard_exclude = {
+    ".mp4",
+    ".mkv",
+    ".webm"
+}
 
 CHUNK_SIZE = 1<<18 # entirely arbitrary
 async def download(sess, url, file):
@@ -80,12 +85,19 @@ async def main(time_threshold):
             bck = bucket(id)
             os.makedirs(os.path.join("images", bck), exist_ok=True)
             os.makedirs(os.path.join("meta", bck), exist_ok=True)
+            if not item.get("preview"): return
             if not item["url"].startswith("https://"): return
             meta_path = os.path.join("meta", bck, id + ".json")
             if not os.path.exists(meta_path): # sorry
                 print("|", end="")
                 sys.stdout.flush()
+                excluded = False
+                for excl in hard_exclude:
+                    if item["url"].endswith(excl):
+                        excluded = True
+                        break
                 try:
+                    if not excluded:
                         result = await download(sess, item["url"], os.path.join("images", bck, id))
                 except Exception as e:
                     print("\nMeme acquisition failure:", e)

meme-rater/meme_pipeline.py

@@ -35,7 +35,7 @@ print("crawling...")
 crawl_start = time.time()
 subprocess.run(["python", "crawler.py", str(last_crawl)]).check_returncode()
 print("indexing...")
-subprocess.run(["python", "../mse.py", "rater_mse_config.json"]).check_returncode()
+subprocess.run(["./meme-search-engine", "rater_mse_config.json"]).check_returncode()
 print("evaluating...")
 
 batch_size = 128
@@ -80,11 +80,11 @@ async def run_inserts():
     async with aiohttp.ClientSession():
         async def duplicate_exists(embedding):
             async with aiohttp.request("POST", meme_search_backend, json={
-                "embeddings": [ list(float(x) for x in embedding) ], # sorry
-                "top_k": 1
+                "terms": [{ "embedding": list(float(x) for x in embedding) }], # sorry
+                "k": 1
             }) as res:
                 result = await res.json()
-                closest = result[0]["score"]
+                closest = result["matches"][0][0]
                 return closest > 0.99 # arbitrary threshold, TODO
 
         for filename, rating in ratings.items():

meme-rater/rater_mse_config.json

@@ -1,5 +1,5 @@
 {
-    "clip_server": "http://100.64.0.10:1708/",
+    "clip_server": "http://100.64.0.10:1708",
     "db_path": "data.sqlite3",
     "port": 1707,
     "files": "./images",

meme-rater/shared.py

@@ -14,7 +14,7 @@ def is_val_set(meme1, meme2):
     return is_one_val(meme1) or is_one_val(meme2)
 
 def fetch_embedding(filename):
-    csr = db.execute("SELECT embedding_vector FROM files WHERE filename = ?", (filename,))
+    csr = db.execute("SELECT embedding FROM files WHERE filename = ?", (filename,))
     x = numpy.frombuffer(csr.fetchone()[0], dtype="float16")
     csr.close()
     return x.copy() # PyTorch complains otherwise due to bad
@@ -44,7 +44,7 @@ def generate_random_permutations(x, n):
     return out
 
 def fetch_all_files():
-    csr = db.execute("SELECT filename, embedding_vector FROM files")
+    csr = db.execute("SELECT filename, embedding FROM files WHERE embedding IS NOT NULL")
     x = [ (row[0], numpy.frombuffer(row[1], dtype="float16").copy()) for row in csr.fetchall() ]
     csr.close()
     return x