Delete helper.py

helper.py

@@ -1,76 +0,0 @@
-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()