Delete helper.py

helper.py

@@ -1,103 +0,0 @@
-# -*- coding: utf-8 -*-
-"""helper.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1IDhEhDLbnCTaBfIbuMtlNFW3ntQiZBwA
-"""
-
-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)
-
-def render_results_in_image(in_pil_img, in_results):
-    plt.figure(figsize=(16, 10))
-    plt.imshow(in_pil_img)
-
-    ax = plt.gca()
-
-    for prediction in in_results:
-
-        x, y = prediction['box']['xmin'], prediction['box']['ymin']
-        w = prediction['box']['xmax'] - prediction['box']['xmin']
-        h = prediction['box']['ymax'] - prediction['box']['ymin']
-
-        ax.add_patch(plt.Rectangle((x, y),
-                                   w,
-                                   h,
-                                   fill=False,
-                                   color="green",
-                                   linewidth=2))
-        ax.text(
-           x,
-           y,
-           f"{prediction['label']}: {round(prediction['score']*100, 1)}%",
-           color='red'
-        )
-
-    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)
-
-    # Close the plot to prevent it from being displayed
-    plt.close()
-
-    return modified_image
-
-def summarize_predictions_natural_language(predictions):
-    summary = {}
-    p = inflect.engine()
-
-    for prediction in predictions:
-        label = prediction['label']
-        if label in summary:
-            summary[label] += 1
-        else:
-            summary[label] = 1
-
-    result_string = "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}"
-        if count > 1:
-          result_string += "s"
-
-        result_string += " "
-
-        if i == len(summary) - 2:
-          result_string += "and "
-
-    # Remove the trailing comma and space
-    result_string = result_string.rstrip(', ') + "."
-
-    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()
-
-########