Upload folder using huggingface_hub

README.md

@@ -1,6 +1,6 @@
 ---
 title: MyGradioApp
-app_file: mygradio.py
+app_file: myapp.py
 sdk: gradio
 sdk_version: 4.19.1
 ---

myapp.py

@@ -0,0 +1,54 @@
+from transformers import DetrImageProcessor, DetrForObjectDetection
+from PIL import Image, ImageDraw
+
+import requests
+import torch
+import numpy as np
+import gradio as gr
+
+# using the pre-trained model for image processing
+image_processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+
+# using the pre-trained model for object detection
+model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+def detect_objects(image):
+    # convert image from NumPy array to PIL format
+    image = Image.fromarray(image)
+    
+    # process the image
+    inputs = image_processor(images = image, 
+                             return_tensors = "pt")
+    outputs = model(**inputs)
+    
+    # create the target size in the format of (height,width)
+    target_sizes = torch.tensor([image.size[::-1]])
+
+    # detect objects in image
+    results = image_processor.post_process_object_detection(
+                  outputs,
+                  target_sizes = target_sizes, 
+                  threshold = 0.9)[0]
+    draw = ImageDraw.Draw(image)
+
+    for score, label, box in zip(results["scores"], results["labels"],
+      results["boxes"]):
+        box = [round(i, 2) for i in box.tolist()]
+        # draw bounding box around object
+        draw.rectangle(box, 
+                       outline="yellow", 
+                       width=2)
+        # display the object label
+        draw.text((box[0], box[1]-10), 
+                  model.config.id2label[label.item()], 
+                  fill="white")  
+    return image
+
+demo = gr.Interface(detect_objects, 
+                    inputs = gr.Image(width = 300, height = 300), 
+                            # indicate the size of image to be passed in
+                    outputs = gr.Image(width = 300, height= 300), 
+                            # indicate the size of image to be returned
+)
+demo.launch()
+

requirements.txt

@@ -0,0 +1,5 @@
+transformers==4.38.1
+torch==2.2.1
+timm==0.9.16
+gradio==4.19.2
+