New app file

app.py

@@ -1,9 +1,115 @@
+import numpy as np
+import torch
+import sys
+import os
+from fastai.vision.all import *
 import gradio as gr
 
+############### HF ###########################
 
-def greet(name):
-    return "Hello " + name + "!!"
+HF_TOKEN = os.getenv('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')
 
+hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "savtadepth-flags")
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+############## DVC ################################
+
+PROD_MODEL_PATH = "src/models"
+TRAIN_PATH = "src/data/processed/train/bathroom"
+TEST_PATH = "src/data/processed/test/bathroom"
+
+if os.path.isdir(".dvc"):
+    print("Running DVC")
+    os.system("dvc config cache.type copy")
+    os.system("dvc config core.no_scm true")
+    if os.system(f"dvc pull {PROD_MODEL_PATH} {TRAIN_PATH } {TEST_PATH }") != 0:
+        exit("dvc pull failed")
+    os.system("rm -r .dvc")
+# .apt/usr/lib/dvc
+
+############## Inference ##############################
+
+
+class ImageImageDataLoaders(DataLoaders):
+    """Basic wrapper around several `DataLoader`s with factory methods for Image to Image problems"""
+    @classmethod
+    @delegates(DataLoaders.from_dblock)
+    def from_label_func(cls, path, filenames, label_func, valid_pct=0.2, seed=None, item_transforms=None,
+                        batch_transforms=None, **kwargs):
+        """Create from list of `fnames` in `path`s with `label_func`."""
+        datablock = DataBlock(blocks=(ImageBlock(cls=PILImage), ImageBlock(cls=PILImageBW)),
+                              get_y=label_func,
+                              splitter=RandomSplitter(valid_pct, seed=seed),
+                              item_tfms=item_transforms,
+                              batch_tfms=batch_transforms)
+        res = cls.from_dblock(datablock, filenames, path=path, **kwargs)
+        return res
+
+
+def get_y_fn(x):
+    y = str(x.absolute()).replace('.jpg', '_depth.png')
+    y = Path(y)
+
+    return y
+
+
+def create_data(data_path):
+    fnames = get_files(data_path/'train', extensions='.jpg')
+    data = ImageImageDataLoaders.from_label_func(
+        data_path/'train', seed=42, bs=4, num_workers=0, filenames=fnames, label_func=get_y_fn)
+    return data
+
+
+data = create_data(Path('src/data/processed'))
+learner = unet_learner(data, resnet34, metrics=rmse,
+                       wd=1e-2, n_out=3, loss_func=MSELossFlat(), path='src/')
+learner.load('model')
+
+
+def gen(input_img):
+    return PILImageBW.create((learner.predict(input_img))[0]).convert('L')
+
+################### Gradio Web APP ################################
+
+
+title = "SavtaDepth WebApp"
+
+description = """
+<p>
+<center>
+Savta Depth is a collaborative Open Source Data Science project for monocular depth estimation - Turn 2d photos into 3d photos. To test the model and code please check out the link bellow.
+<img src="https://huggingface.co/spaces/kingabzpro/savtadepth/resolve/main/examples/cover.png" alt="logo" width="250"/>
+</center>
+</p>
+"""
+article = "<p style='text-align: center'><a href='https://dagshub.com/OperationSavta/SavtaDepth' target='_blank'>SavtaDepth Project from OperationSavta</a></p><p style='text-align: center'><a href='https://colab.research.google.com/drive/1XU4DgQ217_hUMU1dllppeQNw3pTRlHy1?usp=sharing' target='_blank'>Google Colab Demo</a></p></center></p>"
+
+examples = [
+    ["examples/00008.jpg"],
+    ["examples/00045.jpg"],
+]
+favicon = "examples/favicon.ico"
+thumbnail = "examples/SavtaDepth.png"
+
+
+def main():
+    iface = gr.Interface(
+        gen,
+        gr.inputs.Image(shape=(640, 480), type='numpy'),
+        "image",
+        title=title,
+        flagging_options=["incorrect", "worst", "ambiguous"],
+        allow_flagging="manual",
+        flagging_callback=hf_writer,
+        description=description,
+        article=article,
+        examples=examples,
+        theme="peach",
+        allow_screenshot=True
+    )
+
+    iface.launch(enable_queue=True)
+# enable_queue=True,auth=("admin", "pass1234")
+
+
+if __name__ == '__main__':
+    main()