add training script and prediction

README.md

@@ -1,25 +1,42 @@
 ---
+license: mit
 library_name: keras
 tags:
 - dragon-detection
 - Keras
 - dragon
-- image classification
+- image-classification
 ---
 
-## Model description
+## Dragon detector with Tensor Flow
+This is a simple `tensorflow` model to detect dragon in images.
+If you just want to test the trained model, make sure you have the following packages:
 
-More information needed
+```
+tensorflow keras sklearn-deap datasets transformers[torch] sentencepiece
+```
 
-## Intended uses & limitations
+## Predict
 
-More information needed
+To run prediction you need to run below code:
 
-## Training and evaluation data
+```python
+from huggingface_hub import from_pretrained_keras
 
-More information needed
+model = from_pretrained_keras("hadilq/dragon-notdragon")
+
+img = keras.preprocessing.image.load_img(filename, target_size=(224, 224))
+x = keras.preprocessing.image.img_to_array(img)
+x = np.expand_dims(x, axis=0)
+x = keras.applications.vgg16.preprocess_input(x)
+prediction = model.predict(x)
+print("model:", filename, "dragon" if prediction[0][0] >= 0.99 else "notdragon")
+```
+
+Additionally, you can check https://replicate.com/hadilq/dragon-notdragon to play around.
 
 ## Training procedure
+I trained it in Google colab, where you can find the original code in `training` directory.
 
 ### Training hyperparameters
 
@@ -44,3 +61,12 @@ The following hyperparameters were used during training:
 | amsgrad | False |
 | training_precision | float32 |
 
+
+ ## Model Plot
+
+<details>
+<summary>View Model Plot</summary>
+
+![Model Image](./model.png)
+
+</details>

config.json

@@ -1,5 +1,9 @@
 {
   "name": "sequential_2",
+  "id2label": {
+   "0": "dragon",
+   "1": "not dragon"
+  },
   "layers": [
     {
       "module": "keras.layers",
@@ -959,4 +963,4 @@
       }
     }
   ]
-}
+}

pipeline.py

@@ -0,0 +1,31 @@
+from typing import  Dict
+from PIL import Image
+import numpy as np
+import os
+import json
+import tensorflow as tf
+from tensorflow import keras
+
+class PreTrainedPipeline():
+    def __init__(self, path=""):
+        self.model = keras.saving.load_model("./")
+        with open(os.path.join(path, "config.json")) as config:
+            config = json.load(config)
+        self.id2label = config["id2label"]
+
+    def __call__(self, inputs: "Image.Image")-> Dict[str, str]:
+        """
+        Args:
+            inputs (:obj:`PIL.Image`):
+                The raw image representation as PIL.
+                No transformation made whatsoever from the input. Make all necessary transformations here.
+        Return:
+            A :obj:`list`:. The list contains items that are dicts should be liked {"label": "XXX", "score": 0.82}
+                It is preferred if the returned list is in decreasing `score` order
+        """
+        img = keras.preprocessing.image.load_img(input, target_size=(224, 224))
+        x = keras.preprocessing.image.img_to_array(img)
+        x = np.expand_dims(x, axis=0)
+        x = keras.applications.vgg16.preprocess_input(x)
+        prediction = self.model.predict(x)
+        return { 'label': "detected", 'score': "dragon" if prediction[0][0] >= 0.99 else "not-dragon" }

requirements.txt

@@ -0,0 +1,5 @@
+tensorflow==2.13.0
+keras==2.13.1
+sklearn-deap==0.3.0
+pillow==10.3.0
+