complete

README.md

@@ -3,9 +3,9 @@ title: 23A070B
 emoji: 🐢
 colorFrom: indigo
 colorTo: red
-python_version: 3.8
+# python_version: 3.8
 sdk: gradio
-sdk_version: 4.0.2
+sdk_version: 4.12.2
 app_file: app.py
 pinned: false
 license: apache-2.0

app.py

@@ -1,55 +1,50 @@
-import matplotlib.pyplot as plt
+import os
+import tarfile
+import wget 
 import numpy as np
-from six import BytesIO
-from PIL import Image
 import tensorflow as tf
+from huggingface_hub import snapshot_download
+import gradio as gr
+
 from object_detection.utils import label_map_util
 from object_detection.utils import visualization_utils as viz_utils
-from object_detection.utils import ops as utils_op
-import tarfile
-import wget 
-import gradio as gr
 
+# Constants
+REPO_ID = "wongshennan/iti107_model"
+PATH_TO_LABELS = 'data/label_map.pbtxt'
 
-PATH_TO_LABELS = 'data/label_map.pbtxt'   
+# Load category index
 category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
 
-def pil_image_as_numpy_array(pilimg):
-
-    img_array = tf.keras.utils.img_to_array(pilimg)
+def pil_image_as_numpy_array(pil_img):
+    """Convert PIL image to numpy array."""
+    img_array = tf.keras.utils.img_to_array(pil_img)
     img_array = np.expand_dims(img_array, axis=0)
     return img_array
-    
-def load_image_into_numpy_array(path):
-                                    
-    image = None
-    image_data = tf.io.gfile.GFile(path, 'rb').read()
-    image = Image.open(BytesIO(image_data))
-    return pil_image_as_numpy_array(image)            
 
 def load_model():
+    """Load model from Hugging Face Hub."""
+    download_dir = snapshot_download(REPO_ID)
+    saved_model_dir = os.path.join(download_dir, "saved_model")
+    return tf.saved_model.load(saved_model_dir)
+
+def load_model2():
+    """Load model from a tar.gz file."""
     wget.download("https://nyp-aicourse.s3-ap-southeast-1.amazonaws.com/pretrained-models/balloon_model.tar.gz")
     tarfile.open("balloon_model.tar.gz").extractall()
-    model_dir = 'saved_model'    
-    detection_model = tf.saved_model.load(str(model_dir))
-    return detection_model    
-
-# samples_folder = 'test_samples
-# image_path = 'test_samples/sample_balloon.jpeg
-# 
+    model_dir = 'saved_model'
+    return tf.saved_model.load(str(model_dir))
 
-def predict(pilimg):
-
-    image_np = pil_image_as_numpy_array(pilimg)
-    return predict2(image_np)
-
-def predict2(image_np):
+def predict(pil_img):
+    """Predict method for Gradio interface."""
+    image_np = pil_image_as_numpy_array(pil_img)
+    return detect_and_visualize(image_np)
 
+def detect_and_visualize(image_np):
+    """Helper function to run object detection and visualize results."""
     results = detection_model(image_np)
+    result = {key: value.numpy() for key, value in results.items()}
 
-    # different object detection models have additional results
-    result = {key:value.numpy() for key,value in results.items()}
-    
     label_id_offset = 0
     image_np_with_detections = image_np.copy()
 
@@ -65,17 +60,12 @@ def predict2(image_np):
         agnostic_mode=False,
         line_thickness=2)
 
-    result_pil_img = tf.keras.utils.array_to_img(image_np_with_detections[0])
-    
-    return result_pil_img
+    return tf.keras.utils.array_to_img(image_np_with_detections[0])
 
+# Load the model
 detection_model = load_model()
-# pil_image = Image.open(image_path)
-# image_arr = pil_image_as_numpy_array(pil_image)
-
-# predicted_img = predict(image_arr)
-# predicted_img.save('predicted.jpg')
 
+# Launch Gradio Interface
 gr.Interface(fn=predict,
              inputs=gr.Image(type="pil"),
              outputs=gr.Image(type="pil")

data/label_map.pbtxt

@@ -1,4 +1,8 @@
 item {
     id: 1
-    name: 'balloon'
-}
+    name: 'tshirt'
+}
+item {
+    id: 2
+    name: 'shoes'
+}

requirements.txt

@@ -3,3 +3,5 @@ tf-models-research-object-detection
 matplotlib
 wget
 Pillow==9.5
+huggingface_hub
+tensorflow