Create helper.py

helper.py

@@ -0,0 +1,76 @@
+import io
+import matplotlib.pyplot as plt
+import inflect
+from PIL import Image
+import warnings
+import logging
+from transformers import logging as hf_logging
+
+def render_results_in_image(in_pil_img, in_results):
+    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 = box['xmin'], box['ymin']
+        w = box['xmax'] - box['xmin']
+        h = box['ymax'] - box['ymin']
+
+        ax.add_patch(plt.Rectangle((x, y), w, h,
+                                   fill=False,
+                                   color="lime",
+                                   linewidth=2))
+        ax.text(
+            x, y - 5,
+            f"{label}: {score:.2f}",
+            color="yellow",
+            fontsize=10,
+            backgroundcolor="black"
+        )
+
+    plt.axis("off")
+
+    # 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)
+
+    plt.close()
+    return modified_img
+
+
+def summarize_predictions_natural_language(predictions):
+    if not predictions:
+        return "No objects detected."
+
+    summary = {}
+    p = inflect.engine()
+
+    for pred in predictions:
+        label = pred["label"]
+        summary[label] = summary.get(label, 0) + 1
+
+    result = "In this image, there are "
+    for i, (label, count) in enumerate(summary.items()):
+        count_str = p.number_to_words(count)
+        result += f"{count_str} {label}"
+        if count > 1:
+            result += "s"
+        if i < len(summary) - 1:
+            result += ", "
+    result += "."
+
+    return result
+
+
+def ignore_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")
+    logging.basicConfig(level=logging.ERROR)
+    hf_logging.set_verbosity_error()