Rename app with boundry.py to app without box-boundry.py

app with boundry.py

@@ -1,81 +0,0 @@
-import gradio as gr
-from PIL import Image, ImageDraw, ImageFont
-
-
-# Use a pipeline as a high-level helper
-from transformers import pipeline
-
-#model_path = ("../Models/models--facebook--detr-resnet-50/snapshots/1d5f47bd3bdd2c4bbfa585418ffe6da5028b4c0b")
-
-object_detector = pipeline("object-detection",
-                model="facebook/detr-resnet-50")
-
-#object_detector = pipeline("object-detection",
- #              model=model_path)
-
-
-def draw_bounding_boxes(image, detections, font_path=None, font_size=20):
-    """
-    Draws bounding boxes on the given image based on the detections.
-    :param image: PIL.Image object
-    :param detections: List of detection results, where each result is a dictionary containing
-                       'score', 'label', and 'box' keys. 'box' itself is a dictionary with 'xmin',
-                       'ymin', 'xmax', 'ymax'.
-    :param font_path: Path to the TrueType font file to use for text.
-    :param font_size: Size of the font to use for text.
-    :return: PIL.Image object with bounding boxes drawn.
-    """
-    # Make a copy of the image to draw on
-    draw_image = image.copy()
-    draw = ImageDraw.Draw(draw_image)
-
-    # Load custom font or default font if path not provided
-    if font_path:
-        font = ImageFont.truetype(font_path, font_size)
-    else:
-        # When font_path is not provided, load default font but it's size is fixed
-        font = ImageFont.load_default()
-        # Increase font size workaround by using a TTF font file, if needed, can download and specify the path
-
-    for detection in detections:
-        box = detection['box']
-        xmin = box['xmin']
-        ymin = box['ymin']
-        xmax = box['xmax']
-        ymax = box['ymax']
-
-        # Draw the bounding box
-        draw.rectangle([(xmin, ymin), (xmax, ymax)], outline="red", width=3)
-
-        # Optionally, you can also draw the label and score
-        label = detection['label']
-        score = detection['score']
-        text = f"{label} {score:.2f}"
-
-        # Draw text with background rectangle for visibility
-        if font_path:  # Use the custom font with increased size
-            text_size = draw.textbbox((xmin, ymin), text, font=font)
-        else:
-            # Calculate text size using the default font
-            text_size = draw.textbbox((xmin, ymin), text)
-
-        draw.rectangle([(text_size[0], text_size[1]), (text_size[2], text_size[3])], fill="red")
-        draw.text((xmin, ymin), text, fill="white", font=font)
-
-    return draw_image
-
-
-def detect_object(image):
-    raw_image = image
-    output = object_detector(raw_image)
-    processed_image = draw_bounding_boxes(raw_image, output)
-    return processed_image
-
-demo = gr.Interface(fn=detect_object,
-                    inputs=[gr.Image(label="Select Image",type="pil")],
-                    outputs=[gr.Image(label="Processed Image", type="pil")],
-                    title="GenAI Project: Object Detector",
-                    description="THIS APPLICATION WILL BE USED TO DETECT OBJECTS INSIDE THE PROVIDED INPUT IMAGE.")
-demo.launch()
-
-# print(output)

app without box-boundry.py

@@ -0,0 +1,20 @@
+import gradio as gr
+from PIL import Image
+from transformers import pipeline
+
+# Your existing code
+pipe = pipeline("object-detection", model="facebook/detr-resnet-50")
+
+def detect_objects(image):
+    # The pipeline handles the detection
+    results = pipe(image)
+    # You can then process 'results' to draw boxes or return text
+    return results
+
+demo = gr.Interface(
+    fn=detect_objects,
+    inputs=gr.Image(type="pil"),
+    outputs="json"
+)
+
+demo.launch()