Upload app.py

app.py

@@ -0,0 +1,140 @@
+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()