first commit

app.py

@@ -0,0 +1,46 @@
+import gradio as gr
+import pandas as pd
+import csv
+import os
+from huggingface_hub import HfApi, HfFolder
+import yaml
+from src.utils import load_images, load_words, save_results, load_global_variable
+
+# set css style for gallery, specifically for the width of the container
+css = """
+    #gallery {
+        height: 300px;
+    }
+    """
+
+
+
+def main():
+    
+    config = yaml.safe_load(open("config/config.yaml"))
+    print(config)
+    
+    with gr.Blocks(theme=gr.themes.Glass(), css=css) as demo:
+        title = gr.Markdown(f"# My Gradio App Title")
+        welcome_message = gr.Markdown("# Welcome to the Gradio App!")
+        
+        # Load global variable, images, and words after confirming alert
+        global_var = load_global_variable()
+        images = load_images(global_var)
+        words = load_words(global_var)
+        
+        image_gallery = gr.Gallery(images, columns=10, elem_classes="gallery-container", elem_id="gallery")
+        checkbox_group = gr.CheckboxGroup(words, label="Select 3 words")
+        
+        result_message = gr.Textbox(label="Result")
+        
+        submit_button = gr.Button("Submit")
+        submit_button.click(save_results, inputs=checkbox_group, outputs=result_message)
+        
+        demo.load(lambda: None, inputs=None, outputs=welcome_message)
+
+    demo.launch()
+    
+
+if __name__ == "__main__":
+    main()

config/config.yaml

@@ -0,0 +1,13 @@
+data_dir: data
+image_dir: images
+saliency_dir: saliency
+
+repo_id: "MarcoParola/saliency-evaluation"
+
+dataset: 
+  intel-image:
+    n_classes: 6
+    class_names: ['building', 'forest', 'glacier', 'mountain', 'sea', 'street']
+  imagenette:
+    n_classes: 10
+    class_names: ['tench', 'English springer', 'cassette player', 'chain saw', 'church', 'French horn', 'garbage truck', 'gas pump', 'golf ball', 'parachute']

requirements.txt

@@ -0,0 +1,3 @@
+gradio
+hydra-core
+huggingface_hub

src/utils.py

@@ -0,0 +1,55 @@
+import os
+import pandas as pd
+from huggingface_hub import HfApi, HfFolder
+import yaml
+
+
+config = yaml.safe_load(open("./config/config.yaml")) 
+
+# Function to load global variable from CSV
+def load_global_variable():
+    if os.path.exists('global_variable.csv'):
+        df = pd.read_csv('global_variable.csv')
+        return df['value'][0]
+    else:
+        return 0
+
+def load_images(global_var):
+    image_dir = os.path.join(config["data_dir"], config["image_dir"])
+    #images = [f"image_{global_var}_{i}.jpg" for i in range(10)]
+    images = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if os.path.isfile(os.path.join(image_dir, f))]
+    return images
+
+# Function to load words based on global variable
+def load_words(global_var):
+    words = [f"word_{global_var}_{i}" for i in range(10)]
+    return words
+
+# Function to save results and increment global variable
+def save_results(results):
+    
+    filename = "results.txt"
+    results_str = "\n".join(results)
+    with open(filename, 'w') as f:
+        f.write(results_str)
+
+    # Upload the file to Hugging Face Hub
+    api = HfApi()
+    token = os.getenv("HUGGINGFACE_TOKEN")
+    #token = HfFolder.get_token()
+    
+    if not token:
+        print("Token not found. Please login to Hugging Face.")
+        return
+    
+    try:
+        api.upload_file(
+            path_or_fileobj=filename,
+            path_in_repo=filename,
+            repo_id=config["repo_id"],
+            token=token,
+            commit_message="Add results.txt"
+        )
+        print(f"File {filename} uploaded successfully to {config['repo_id']}.")
+    except Exception as e:
+        print(f"Failed to upload file: {e}")