fix

bin/Dockerfile

@@ -0,0 +1,17 @@
+FROM guillaumeai/ast:picasso
+
+RUN apt update
+RUN apt upgrade -y
+
+RUN pip install -U pip
+#RUN pip install -U pyyaml
+RUN pip install -U runway-python 
+#runway --force-reinstall
+#RUN pip install -U tensorflow
+
+
+COPY server.py .
+
+
+
+CMD ["python", "server.py"]

bin/_env.sh

@@ -0,0 +1,60 @@
+ctlid=2403272038
+dockertag=jgwill/dev:astpicasso
+
+containername=astpicasso
+dkhostname=$containername
+
+# PORT
+dkport=7860:7860
+
+#xmount=/tmp:/a/tmp
+#xmount2=/var:/a/var
+
+
+dkcommand=bash #command to execute (default is the one in the dockerfile)
+
+#dkextra=" -v \$dworoot/x:/x -p 2288:2288 "
+
+#dkmounthome=true
+
+
+##########################
+############# RUN MODE
+#dkrunmode="bg" #default fg
+#dkrestart="--restart" #default
+#dkrestarttype="unless-stopped" #default
+
+
+#########################################
+################## VOLUMES
+#dkvolume="myvolname220413:/app" #create or use existing one
+#dkvolume="$containername:/app" #create with containername name
+
+
+
+#dkecho=true #just echo the docker run
+
+
+# Use TZ
+#DK_TZ=1
+
+
+
+#####################################
+#Build related
+#
+##chg back to that user
+#dkchguser=vscode
+
+######################## HOOKS BASH
+### IF THEY EXIST, THEY are Executed, you can change their names
+
+dkbuildprebuildscript=dkbuildprebuildscript.sh
+dkbuildbuildsuccessscript=dkbuildbuildsuccessscript.sh
+dkbuildfailedscript=dkbuildfailedscript.sh
+dkbuildpostbuildscript=dkbuildpostbuildscript.sh
+
+###########################################
+# Unset deprecated
+unset DOCKER_BUILDKIT
+

bin/server.py

@@ -0,0 +1,59 @@
+import os
+import numpy as np
+import tensorflow as tf
+from module import encoder, decoder
+from glob import glob
+import runway
+
+
+@runway.setup(options={"styleCheckpoint": runway.file(is_directory=True)})
+def setup(opts):
+    sess = tf.Session()
+    init_op = tf.global_variables_initializer()
+    sess.run(init_op)
+    with tf.name_scope("placeholder"):
+        input_photo = tf.placeholder(
+            dtype=tf.float32, shape=[1, None, None, 3], name="photo"
+        )
+    input_photo_features = encoder(
+        image=input_photo, options={"gf_dim": 32}, reuse=False
+    )
+    output_photo = decoder(
+        features=input_photo_features, options={"gf_dim": 32}, reuse=False
+    )
+    saver = tf.train.Saver()
+    path = opts["styleCheckpoint"]
+    model_name = [p for p in os.listdir(path) if os.path.isdir(os.path.join(path, p))][
+        0
+    ]
+    checkpoint_dir = os.path.join(path, model_name, "checkpoint_long")
+    ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
+    ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
+    saver.restore(sess, os.path.join(checkpoint_dir, ckpt_name))
+    return dict(sess=sess, input_photo=input_photo, output_photo=output_photo)
+
+
+@runway.command(
+    "stylize",
+    inputs={"contentImage": runway.image},
+    outputs={"stylizedImage": runway.image},
+)
+def stylize(model, inp):
+    img = inp["contentImage"]
+    img = np.array(img)
+    img = img / 127.5 - 1.0
+    img = np.expand_dims(img, axis=0)
+    img = model["sess"].run(
+        model["output_photo"], feed_dict={model["input_photo"]: img}
+    )
+    img = (img + 1.0) * 127.5
+    img = img.astype("uint8")
+    img = img[0]
+    return dict(stylizedImage=img)
+
+
+if __name__ == "__main__":
+    #print("External Service port is:" + os.environ.get("SPORT",7860))
+    #set env var: RW_PORT=7860
+    os.environ["RW_PORT"] = "7860"
+    runway.run()