Update app.py

app.py

@@ -1,67 +1,67 @@
-# import requests
-# import os, io
-# import gradio as gr
-# # from PIL import Image
+import requests
+import os, io
+import gradio as gr
+# from PIL import Image
 
-# # API_URL = "https://api-inference.huggingface.co/models/facebook/detr-resnet-50-panoptic"
+# API_URL = "https://api-inference.huggingface.co/models/facebook/detr-resnet-50-panoptic"
 
-# SECRET_TOKEN = os.getenv("SECRET_TOKEN")
-# API_URL = "https://api-inference.huggingface.co/models/facebook/detr-resnet-50-dc5-panoptic"
-# headers = {"Authorization": f'Bearer {SECRET_TOKEN}'}
+SECRET_TOKEN = os.getenv("SECRET_TOKEN")
+API_URL = "https://api-inference.huggingface.co/models/facebook/detr-resnet-50-dc5-panoptic"
+headers = {"Authorization": f'Bearer {SECRET_TOKEN}'}
 
 
 
-# def image_classifier(inp):
-#     return {'cat': 0.3, 'dog': 0.7}
+def image_classifier(inp):
+    return {'cat': 0.3, 'dog': 0.7}
 
-# def query(filename):
-#     with open(filename, "rb") as f:
-#         data = f.read()
-#     response = requests.post(API_URL, headers=headers, data=data)
-#     return response.json()
+def query(filename):
+    with open(filename, "rb") as f:
+        data = f.read()
+    response = requests.post(API_URL, headers=headers, data=data)
+    return response.json()
 
-# def rb(img):
-#     # initialiaze io to_bytes converter
-#     img_byte_arr = io.BytesIO()
-#     # define quality of saved array
-#     img.save(img_byte_arr, format='JPEG', subsampling=0, quality=100)
-#     # converts image array to bytesarray
-#     img_byte_arr = img_byte_arr.getvalue()
-#     response = requests.post(API_URL, headers=headers, data=img_byte_arr)
-#     return response.json()
+def rb(img):
+    # initialiaze io to_bytes converter
+    img_byte_arr = io.BytesIO()
+    # define quality of saved array
+    img.save(img_byte_arr, format='JPEG', subsampling=0, quality=100)
+    # converts image array to bytesarray
+    img_byte_arr = img_byte_arr.getvalue()
+    response = requests.post(API_URL, headers=headers, data=img_byte_arr)
+    return response.json()
 
 
-# inputs = gr.inputs.Image(type="pil", label="Upload an image")
-# demo = gr.Interface(fn=rb, inputs=inputs, outputs="json")
-# demo.launch()
+inputs = gr.inputs.Image(type="pil", label="Upload an image")
+demo = gr.Interface(fn=rb, inputs=inputs, outputs="json")
+demo.launch()
 
-import io
-import requests
-from PIL import Image
-import torch
-import numpy
+# import io
+# import requests
+# from PIL import Image
+# import torch
+# import numpy
 
-from transformers import DetrFeatureExtractor, DetrForSegmentation
-from transformers.models.detr.feature_extraction_detr import rgb_to_id
+# from transformers import DetrFeatureExtractor, DetrForSegmentation
+# from transformers.models.detr.feature_extraction_detr import rgb_to_id
 
-url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-image = Image.open(requests.get(url, stream=True).raw)
+# url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+# image = Image.open(requests.get(url, stream=True).raw)
 
-feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50-panoptic")
-model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic")
+# feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50-panoptic")
+# model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic")
 
-# prepare image for the model
-inputs = feature_extractor(images=image, return_tensors="pt")
+# # prepare image for the model
+# inputs = feature_extractor(images=image, return_tensors="pt")
 
-# forward pass
-outputs = model(**inputs)
+# # forward pass
+# outputs = model(**inputs)
 
-# use the `post_process_panoptic` method of `DetrFeatureExtractor` to convert to COCO format
-processed_sizes = torch.as_tensor(inputs["pixel_values"].shape[-2:]).unsqueeze(0)
-result = feature_extractor.post_process_panoptic(outputs, processed_sizes)[0]
+# # use the `post_process_panoptic` method of `DetrFeatureExtractor` to convert to COCO format
+# processed_sizes = torch.as_tensor(inputs["pixel_values"].shape[-2:]).unsqueeze(0)
+# result = feature_extractor.post_process_panoptic(outputs, processed_sizes)[0]
 
-# the segmentation is stored in a special-format png
-panoptic_seg = Image.open(io.BytesIO(result["png_string"]))
-panoptic_seg = numpy.array(panoptic_seg, dtype=numpy.uint8)
-# retrieve the ids corresponding to each mask
-panoptic_seg_id = rgb_to_id(panoptic_seg)
+# # the segmentation is stored in a special-format png
+# panoptic_seg = Image.open(io.BytesIO(result["png_string"]))
+# panoptic_seg = numpy.array(panoptic_seg, dtype=numpy.uint8)
+# # retrieve the ids corresponding to each mask
+# panoptic_seg_id = rgb_to_id(panoptic_seg)