Create app.py

app.py

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+import gradio as gr
+from PIL import Image, ImageDraw
+
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+# pipe = pipeline("object-detection", model="facebook/detr-resnet-50")
+
+model_path = "../Models/models--facebook--detr-resnet-50/snapshots/1d5f47bd3bdd2c4bbfa585418ffe6da5028b4c0b"
+
+object_detector = pipeline("object-detection", model=model_path)
+
+def draw_bounding_boxes(image, object_detections):
+    """
+    Draws bounding boxes around detected objects on a PIL image.
+    
+    Args:
+        image (PIL.Image): The input image.
+        object_detections (list): A list of dictionaries, where each dictionary represents a detected object. 
+                                 Each dictionary should have the following keys:
+                                 - 'score': the confidence score of the detection
+                                 - 'label': the label of the detected object
+                                 - 'box': a dictionary with keys 'xmin', 'ymin', 'xmax', 'ymax' 
+                                   representing the bounding box coordinates.
+    
+    Returns:
+        PIL.Image: The input image with bounding boxes drawn around the detected objects.
+    """
+    draw = ImageDraw.Draw(image)
+    for detection in object_detections:
+        box = detection['box']
+        label = detection['label']
+        score = detection['score']
+        
+        # Draw the bounding box
+        draw.rectangle((box['xmin'], box['ymin'], box['xmax'], box['ymax']), outline=(255, 0, 0), width=2)
+        
+        # Draw the label and score
+        text = f"{label} ({score:.2f})"
+        draw.text((box['xmin'], box['ymin'] - 20), text, fill=(255, 0, 0))
+    
+    return image
+
+def detect_object(image):
+    # raw_image = Image.open(image)
+    output = object_detector(image)
+    processed_image = draw_bounding_boxes(image, output)
+    return processed_image
+
+gr.close_all()
+
+demo = gr.Interface(fn=detect_object,
+                    inputs=[gr.Image(label="Select Image", type="pil")],
+                    outputs=[gr.Image(label="Processed Image", type="pil")],
+                    title="@IT AI Enthusiast (https://www.youtube.com/@itaienthusiast/) - Project 6: Object Detector",
+                    description="THIS APPLICATION WILL BE USED TO DETECT OBJECT INSIDE THE PROVIDED INPUT IMGAES",
+                    # examples=['Hello Friends, Welcome to my channel. I hope this video helps you understand AI.','Hello friends how are you?'],
+                    concurrency_limit=16)
+demo.launch()