Update app.py

app.py

@@ -3,14 +3,10 @@ from PIL import Image, ImageDraw, ImageFont
 import scipy.io.wavfile as wavfile
 
 
-# Use a pipeline as a high-level helper
+
 from transformers import pipeline
 
-# model_path = ("../Models/models--facebook--detr-resnet-50/snapshots"
-#               "/1d5f47bd3bdd2c4bbfa585418ffe6da5028b4c0b")
-#
-# tts_model_path = ("../Models/models--kakao-enterprise--vits-ljs/snapshots"
-#                   "/3bcb8321394f671bd948ebf0d086d694dda95464")
+
 
 
 narrator = pipeline("text-to-speech",
@@ -19,35 +15,25 @@ narrator = pipeline("text-to-speech",
 object_detector = pipeline("object-detection",
                 model="facebook/detr-resnet-50")
 
-# object_detector = pipeline("object-detection",
-#                 model=model_path)
-#
-# narrator = pipeline("text-to-speech",
-#                     model=tts_model_path)
 
-# [{'score': 0.9996405839920044, 'label': 'person', 'box': {'xmin': 435, 'ymin': 282, 'xmax': 636, 'ymax': 927}}, {'score': 0.9995879530906677, 'label': 'dog', 'box': {'xmin': 570, 'ymin': 694, 'xmax': 833, 'ymax': 946}}]
-
-# Define the function to generate audio from text
 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
+   
     return "output.wav"
 
-# Could you please write me a python code that will take list of detection object as an input and it will give the response that will include all the objects (labels) provided in the input. For example if the input is like this: [{'score': 0.9996405839920044, 'label': 'person', 'box': {'xmin': 435, 'ymin': 282, 'xmax': 636, 'ymax': 927}}, {'score': 0.9995879530906677, 'label': 'dog', 'box': {'xmin': 570, 'ymin': 694, 'xmax': 833, 'ymax': 946}}]
-# The output should be, This pictuture contains 1 person and 1 dog. If there are multiple objects, do not add 'and' between every objects but 'and' should be at the end only
 
 
 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:
@@ -55,7 +41,7 @@ def read_objects(detection_objects):
         else:
             object_counts[label] = 1
 
-    # Generate the response string
+  
     response = "This picture contains"
     labels = list(object_counts.keys())
     for i, label in enumerate(labels):
@@ -85,18 +71,17 @@ def draw_bounding_boxes(image, detections, font_path=None, font_size=20):
     :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']
@@ -104,19 +89,19 @@ def draw_bounding_boxes(image, detections, font_path=None, font_size=20):
         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
+       
+        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")