Update app.py

app.py

@@ -1,107 +1,56 @@
 import gradio as gr
 from PIL import Image, ImageDraw, ImageFont
 import scipy.io.wavfile as wavfile
-
 from transformers import pipeline
 
-narrator = pipeline("text-to-speech",
-                    model="kakao-enterprise/vits-ljs")
-
-object_detector = pipeline("object-detection",
-                model="facebook/detr-resnet-50")
+narrator = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs")
+object_detector = pipeline("object-detection", model="facebook/detr-resnet-50")
 
 def generate_audio(text):
     # Generate the narrated text
     narrated_text = narrator(text)
-
     # Save the audio to a WAV file
-    wavfile.write("output.wav", rate=narrated_text["sampling_rate"],
-                  data=narrated_text["audio"][0])
-
-    # Return the path to the saved audio file
+    wavfile.write("output.wav", rate=narrated_text["sampling_rate"], data=narrated_text["audio"][0])
     return "output.wav"
 
 def read_objects(detection_objects):
-    # Initialize counters for each object label
     object_counts = {}
-
-    # Count the occurrences of each label
     for detection in detection_objects:
         label = detection['label']
-        if label in object_counts:
-            object_counts[label] += 1
-        else:
-            object_counts[label] = 1
-
-    # Generate the response string
+        object_counts[label] = object_counts.get(label, 0) + 1
     response = "This picture contains"
     labels = list(object_counts.keys())
     for i, label in enumerate(labels):
-        response += f" {object_counts[label]} {label}"
-        if object_counts[label] > 1:
-            response += "s"
+        response += f" {object_counts[label]} {label}" + ("s" if object_counts[label] > 1 else "")
         if i < len(labels) - 2:
             response += ","
         elif i == len(labels) - 2:
             response += " and"
-
     response += "."
-
     return response
 
-
-
 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
+        xmin, ymin, xmax, ymax = box['xmin'], box['ymin'], box['xmax'], box['ymax']
         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
+        if font_path:
             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)
@@ -110,10 +59,20 @@ def detect_object(image):
     processed_audio = generate_audio(natural_text)
     return processed_image, processed_audio
 
-
-demo = gr.Interface(fn=detect_object,
-                    inputs=[gr.Image(label="Select Image",type="pil")],
-                    outputs=[gr.Image(label="Processed Image", type="pil"), gr.Audio(label="Generated Audio")],
-                    title= "Audio Described Object Detector",
-                    description="THIS APPLICATION WILL BE USED TO HIGHLIGHT OBJECTS AND GIVES AUDIO DESCRIPTION FOR THE PROVIDED INPUT IMAGE.")
-demo.launch()
+examples = [
+    ["dogs.jpg"]
+]
+
+demo = gr.Interface(
+    fn=detect_object,
+    inputs=[gr.Image(label="Select Image", type="pil")],
+    outputs=[
+        gr.Image(label="Processed Image", type="pil"),
+        gr.Audio(label="Generated Audio")
+    ],
+    title="Audio Described Object Detector",
+    description="This application highlights objects in the provided image and generates an audio description.",
+    examples=examples
+)
+
+demo.launch()