first commit

app.py

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+### -------------------------------- ###
+###            libraries             ###
+### -------------------------------- ###
+import gradio as gr
+import numpy as np
+import os
+from tensorflow.keras.models import load_model
+
+### -------------------------------- ###
+###           model loading          ###
+### -------------------------------- ###
+model = load_model('model.h5') # single file model from colab
+
+## --------------------------------- ###
+###     reading: categories.txt      ###
+### -------------------------------- ###
+labels = ['please upload categories.txt' for i in range(10)] # placeholder
+
+if os.path.isfile("categories.txt"):
+    # open categories.txt in read mode
+    categories = open("categories.txt", "r")
+    labels = categories.readline().split()
+
+## --------------------------------- ###
+###     rendering: info.html         ###
+### -------------------------------- ###
+# borrow file reading functionality from reader.py
+# info =
+description = "A Hugging Space demo created by datasith"
+title = "Cast parts: Deffective or Okay?"
+# css = \
+# '''
+#   .div {
+#     border: 2px solid black;
+#     margin: 10px;
+#     padding: 5%;
+#   }
+#   ul {
+#     display: inline-block;
+#     text-align: left;
+#   }
+#   img {
+#     display: block;
+#     margin: auto;
+#   }
+#   .description {
+#     text-align: center;
+#   }
+# '''
+
+article = \
+'''
+Deffective or Okay? Demo app including a binary classification model for casted parts
+This is a test project to get familiar with Hugging Face! 
+The space includes the necessary files for everything to run smoothly on HF's `Spaces`:
+- `app.py`
+- `reader.py`
+- `requirements.txt`
+- `model.h5` (TensorFlow/Keras)
+- `categories.txt`
+- `info.txt`
+The data used to train the model is available as [Kaggle dataset](https://www.kaggle.com/datasets/ravirajsinh45/real-life-industrial-dataset-of-casting-product).
+The space was inspired by @Isabel's wonderful [cat or pug](https://huggingface.co/spaces/isabel/pug-or-cat-image-classifier) one. Enjoy!d
+'''
+
+
+### -------------------------------- ###
+###        interface creation        ###
+### -------------------------------- ###
+samples = ['defective.jpeg', 'okay.jpeg']
+
+def preprocess(image):
+  image = np.array(image) / 255
+  image = np.expand_dims(image, axis=0)
+  return image
+  
+def predict_image(image):
+  pred = model.predict(preprocess(image))
+  results = {}
+  for row in pred:
+    for idx, item in enumerate(row):
+      results[labels[idx]] = float(item)
+  return results
+
+# generate img input and text label output
+image = gr.inputs.Image(shape=(300, 300), label="Upload Your Image Here")
+label = gr.outputs.Label(num_top_classes=len(labels))
+
+# generate and launch interface
+interface = gr.Interface(fn=predict_image, inputs=image, outputs=label, article=article, theme='default', title=title, allow_flagging='never', description=description, examples=samples)
+interface.launch()

categories.txt

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+def_front ok_front

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:aacf5ddc63a89f828b15fbb0fe87b7c28fce1b521271fe7a2ac563809ac23a9c
+size 134670360

requirements.txt

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+tensorflow>=2.6.1
+keras>=2.6.0
+yattag==1.14.0