Adding new model

.idea/bone-age-estimation.iml

@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="inheritedJdk" />
+    <orderEntry type="jdk" jdkName="Python 3.11 (bone-age-estimation)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

.idea/misc.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="bodyct" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (bone-age-estimation)" project-jdk-type="Python SDK" />
 </project>

Dockerfile

@@ -1,10 +1,7 @@
-FROM docker.io/jackrio/bae_repo:v2
+FROM docker.io/jackrio/bae_repo:v3
 
 USER root
 
-COPY ./requirements_hf.txt /bae/requirements_hf.txt
-RUN pip install --no-cache-dir --upgrade -r /bae/requirements_hf.txt
-
 RUN useradd -m -u 1000 dockeruser
 USER dockeruser
 # Set home to the user's home directory

predict.py

@@ -4,6 +4,10 @@ import numpy as np
 import torch
 from albumentations.pytorch import ToTensorV2
 from models.model_zoo import BoneAgeEstModelZoo
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 device = "cpu"
 
@@ -11,7 +15,7 @@ device = "cpu"
 def initialize_model():
     # Load model
     model = BoneAgeEstModelZoo(branch="gender", pretrained=False, lr=0.001).load_from_checkpoint(
-        "/bae/output/inception_1024/epoch14_inception_1024_kaggle.ckpt", map_location=device)
+        "/bae/output/inception_1024_new_data/epoch=13-step=9828.ckpt", map_location=device)
     model.model.eval()
     return model
 
@@ -46,6 +50,7 @@ def run():
     gender_input = gr.inputs.Dropdown(choices=["male", "female"], type="index", label="Select male or female")
     output = gr.outputs.Textbox(label="Predicted Age")
 
+    logger.info("Gender input", gender_input)
     BAE = gr.Interface(
         fn=predict,
         inputs=[image_input, gender_input],