image

README.md

@@ -1,12 +1,13 @@
 ---
 title: Image Classification
-emoji: 🚀
-colorFrom: indigo
-colorTo: blue
+emoji: 🖼️
+colorFrom: green
+colorTo: green
 sdk: gradio
-sdk_version: 4.24.0
+sdk_version: 4.22.0
 app_file: app.py
 pinned: false
+license: mit
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,140 +0,0 @@
-import os
-import random
-import logging
-import gradio as gr
-from PIL import Image
-from zipfile import ZipFile
-from typing import Any, Dict,List
-from transformers import pipeline
-
-class Image_classification:
-  def __init__(self):
-    pass
-    
-
-  def unzip_image_data(self) -> str:
-    """
-    Unzips an image dataset into a specified directory.
-
-    Returns:
-        str: The path to the directory containing the extracted image files.
-    """
-    try:
-      with ZipFile("image_dataset.zip","r") as extract:
-        
-        directory_path=str("dataset")
-        os.mkdir(directory_path)
-        extract.extractall(f"{directory_path}")
-        return f"{directory_path}"
-
-    except Exception as e:
-            logging.error(f"An error occurred during extraction: {e}")
-            return ""
-
-  def example_images(self) -> List[str]:
-    """
-    Unzips the image dataset and generates a list of paths to the individual image files and use image for showing example
-
-    Returns:
-        List[str]: A list of file paths to each image in the dataset.
-    """
-    try:
-      image_dataset_folder = self.unzip_image_data()
-      image_extensions = ['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp']
-      image_count = len([name for name in os.listdir(image_dataset_folder) if os.path.isfile(os.path.join(image_dataset_folder, name)) and os.path.splitext(name)[1].lower() in image_extensions])
-      example=[]
-      for i in range(image_count):
-        for name in os.listdir(image_dataset_folder):
-            path=(os.path.join(os.path.dirname(image_dataset_folder),os.path.join(image_dataset_folder,name)))
-            example.append(path)
-      return example
-
-    except Exception as e:
-            logging.error(f"An error occurred in  example images: {e}")
-            return ""
-
-  def classify(self, image: Image.Image, model: Any) -> Dict[str, float]:
-    """
-    Classifies an image using a specified model.
-
-    Args:
-        image (Image.Image): The image to classify.
-        model (Any): The model used for classification.
-
-    Returns:
-        Dict[str, float]: A dictionary of classification labels and their corresponding scores.
-    """
-    try:
-      
-      classifier = pipeline("image-classification", model=model)
-      result= classifier(image)
-      return result
-    except Exception as e:
-            logging.error(f"An error occurred during image classification: {e}")
-            raise
-
-  def format_the_result(self, image: Image.Image, model: Any) -> Dict[str, float]:
-    """
-    Formats the classification result by retaining the highest score for each label.
-
-    Args:
-        image (Image.Image): The image to classify.
-        model (Any): The model used for classification.
-
-    Returns:
-        Dict[str, float]: A dictionary with unique labels and the highest score for each label.
-    """
-    try:
-      data=self.classify(image,model)
-      new_dict = {}
-      for item in data:
-          label = item['label']
-          score = item['score']
-
-          if label in new_dict:
-              if new_dict[label] < score:
-                  new_dict[label] = score
-          else:
-              new_dict[label] = score
-      return new_dict
-    except Exception as e:
-      logging.error(f"An error occurred while formatting the results: {e}")
-      raise
-
-  def interface(self):
-
-    with gr.Blocks(css="""
-    
-    .gradio-container {background: #314755;
-       background: -webkit-linear-gradient(to right, #26a0da, #314755);
-       background: linear-gradient(to right, #26a0da, #314755);}
-       .block svelte-90oupt padded{background:314755;
-       margin:0;
-    padding:0;}""") as demo:
-
-      gr.HTML("""
-            <center><h1 style="color:#fff">Image Classification</h1></center>""")
-
-      exam_img=self.example_images()
-      with gr.Row():
-        model = gr.Dropdown(["facebook/regnet-x-040","google/vit-large-patch16-384","microsoft/resnet-50",""],label="Choose a model")
-      with gr.Row():
-        image = gr.Image(type="filepath",sources="upload")
-        with gr.Column():
-          output=gr.Label()
-      with gr.Row():
-        button=gr.Button()
-      button.click(self.format_the_result,[image,model],output)
-      gr.Examples(
-        examples=exam_img,
-        inputs=[image],
-        outputs=output,
-        fn=self.format_the_result,
-        cache_examples=False,
-    )
-    demo.launch(debug=True)
-
-if __name__=="__main__":
-  
-  image_classification=Image_classification()
-  result=image_classification.interface()

.gitattributes → gitattributes

@@ -32,4 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.xz filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

image_dataset.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:611febdc03ee83d4b47ec112b4ee2e25b35e3369dc13df8afb5316db7873c438
-size 1875790

requirements.txt

@@ -1,4 +0,0 @@
-gradio 
-transformers
-tensorflow
-tf-keras