Update helper.py

helper.py

@@ -1,94 +1,76 @@
 import io
 import matplotlib.pyplot as plt
-import requests
 import inflect
 from PIL import Image
-
-def load_image_from_url(url):
-    return Image.open(requests.get(url, stream=True).raw)
+import warnings
+import logging
+from transformers import logging as hf_logging
 
 def render_results_in_image(in_pil_img, in_results):
-    plt.figure(figsize=(16, 10))
+    plt.figure(figsize=(12, 8))
     plt.imshow(in_pil_img)
-
     ax = plt.gca()
 
     for prediction in in_results:
+        box = prediction["box"]
+        score = prediction["score"]
+        label = prediction["label"]
 
-        x, y = prediction['box']['xmin'], prediction['box']['ymin']
-        w = prediction['box']['xmax'] - prediction['box']['xmin']
-        h = prediction['box']['ymax'] - prediction['box']['ymin']
+        x, y = box['xmin'], box['ymin']
+        w = box['xmax'] - box['xmin']
+        h = box['ymax'] - box['ymin']
 
-        ax.add_patch(plt.Rectangle((x, y),
-                                   w,
-                                   h,
+        ax.add_patch(plt.Rectangle((x, y), w, h,
                                    fill=False,
-                                   color="green",
+                                   color="lime",
                                    linewidth=2))
         ax.text(
-           x,
-           y,
-           f"{prediction['label']}: {round(prediction['score']*100, 1)}%",
-           color='red'
+            x, y - 5,
+            f"{label}: {score:.2f}",
+            color="yellow",
+            fontsize=10,
+            backgroundcolor="black"
         )
 
     plt.axis("off")
 
-    # Save the modified image to a BytesIO object
-    img_buf = io.BytesIO()
-    plt.savefig(img_buf, format='png',
-                bbox_inches='tight',
-                pad_inches=0)
-    img_buf.seek(0)
-    modified_image = Image.open(img_buf)
+    # Save to buffer
+    buf = io.BytesIO()
+    plt.savefig(buf, format="png", bbox_inches="tight", pad_inches=0)
+    buf.seek(0)
+    modified_img = Image.open(buf)
 
-    # Close the plot to prevent it from being displayed
     plt.close()
+    return modified_img
 
-    return modified_image
 
 def summarize_predictions_natural_language(predictions):
+    if not predictions:
+        return "No objects detected."
+
     summary = {}
     p = inflect.engine()
 
-    for prediction in predictions:
-        label = prediction['label']
-        if label in summary:
-            summary[label] += 1
-        else:
-            summary[label] = 1
+    for pred in predictions:
+        label = pred["label"]
+        summary[label] = summary.get(label, 0) + 1
 
-    result_string = "In this image, there are "
+    result = "In this image, there are "
     for i, (label, count) in enumerate(summary.items()):
-        count_string = p.number_to_words(count)
-        result_string += f"{count_string} {label}"
+        count_str = p.number_to_words(count)
+        result += f"{count_str} {label}"
         if count > 1:
-          result_string += "s"
-
-        result_string += " "
-
-        if i == len(summary) - 2:
-          result_string += "and "
+            result += "s"
+        if i < len(summary) - 1:
+            result += ", "
+    result += "."
 
-    # Remove the trailing comma and space
-    result_string = result_string.rstrip(', ') + "."
+    return result
 
-    return result_string
-
-
-##### To ignore warnings #####
-import warnings
-import logging
-from transformers import logging as hf_logging
 
 def ignore_warnings():
-    # Ignore specific Python warnings
     warnings.filterwarnings("ignore", message="Some weights of the model checkpoint")
     warnings.filterwarnings("ignore", message="Could not find image processor class")
     warnings.filterwarnings("ignore", message="The `max_size` parameter is deprecated")
-
-    # Adjust logging for libraries using the logging module
     logging.basicConfig(level=logging.ERROR)
     hf_logging.set_verbosity_error()
-
-########