implement user result saving

app.py

@@ -20,6 +20,7 @@ def main():
         title = gr.Markdown("# Saliency evaluation - experiment 1")
         user_state = gr.State(0)
         user_id = gr.State(0)
+        answers = gr.State([])
 
         with gr.Row():
             target_img_label = gr.Markdown(f"### Target image: {class_names[user_state.value]}")
@@ -45,7 +46,6 @@ def main():
 
         gr.Markdown("### Image examples of the same class")
         with gr.Row():
-            # generate random integer value
             count = user_state if isinstance(user_state, int) else user_state.value
             images = load_example_images(count, data_dir)
             img1 = gr.Image(images[0])
@@ -70,14 +70,7 @@ def main():
 
         def update_images(dropdown1, dropdown2, dropdown3, dropdown4, user_state):
 
-            
-            print('dropdowns', dropdown1, dropdown2, dropdown3, dropdown4)
-            rank = [dropdown1,dropdown2,dropdown3,dropdown4]
-            print('rank', rank)
-
-            # image target and saliency images
             count = user_state if isinstance(user_state, int) else user_state.value
-            print(count, config['dataset'][config['dataset']['name']]['n_classes'])
             if count < config['dataset'][config['dataset']['name']]['n_classes']:
                 images = load_image_and_saliency(count, data_dir)
                 target_img = gr.Image(images[0], elem_classes="main-image")
@@ -130,10 +123,30 @@ def main():
             dropdown4 = gr.Dropdown(choices=options, label="rise")
             return dropdown1, dropdown2, dropdown3, dropdown4
         
+        def init(request: gr.Request):
+            user_id.value = id_generator.increment()
+            return user_id
+
+        def redirect():
+            pass
+
+        def register_answers(answers):
+            experiment_dir = config['results']['exp1_dir']
+            save_results(user_id.value, experiment_dir, answers)
+ 
+        def add_answer(dropdown1,dropdown2,dropdown3,dropdown4, answers):
+            rank = [dropdown1,dropdown2,dropdown3,dropdown4]
+            answers.append(rank)
+            return answers
+
         submit_button.click(
             update_state,
             inputs=user_state,
             outputs=user_state
+        ).then(
+            add_answer,
+            inputs=[dropdown1, dropdown2, dropdown3, dropdown4, answers],
+            outputs=answers
         ).then(
             update_img_label,
             inputs=user_state,
@@ -151,15 +164,13 @@ def main():
             inputs={dropdown1, dropdown2, dropdown3, dropdown4},
             outputs={dropdown1, dropdown2, dropdown3, dropdown4}
         )
-
-        def redirect():
-            pass
-            
-        finish_button.click(redirect, js="window.location = 'https://marcoparola.github.io/saliency-evaluation-app/end'")
-
-        def init(request: gr.Request):
-            user_id.value = id_generator.increment()
-            return user_id
+       
+        finish_button.click(
+            add_answer, inputs=[dropdown1, dropdown2, dropdown3, dropdown4, answers],outputs=answers
+        ).then(
+            register_answers, inputs=answers
+        ).then(
+            redirect, js="window.location = 'https://marcoparola.github.io/saliency-evaluation-app/end'")
 
         demo.load(init, inputs=None, outputs=user_id)
     

config/config.yaml

@@ -1,24 +1,26 @@
 data_dir: data
 image_dir: images
 saliency_dir: saliency
+repo_id: "MarcoParola/saliency-evaluation"
 
 gui:
   max_img_examples: 16
 
-repo_id: "MarcoParola/saliency-evaluation"
+results:
+  save_dir: results
+  exp1_dir: exp1
+  exp2_dir: exp2
 
 dataset: 
   name: intel_image
   path: data
   intel_image:
-    n_classes: 6
+    n_classes: 2
     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']
 
- 
-  
 saliency_methods:
   - gradcam
   - lime

src/utils.py

@@ -5,8 +5,7 @@ import yaml
 import numpy as np
 
 config = yaml.safe_load(open("./config/config.yaml")) 
-
-        
+    
 def load_image_and_saliency(class_idx, data_dir):
     path = os.path.join(data_dir, 'images', str(class_idx))
     images = os.listdir(path)
@@ -34,8 +33,17 @@ def load_words(idx):
     return words
 
 # Function to save results and increment global variable
-def save_results(dropdowns):
-    
+def save_results(user_it, experiment_dir, answers):
+    folder = os.path.join(config['results']['save_dir'], experiment_dir, str(user_it))
+
+    # convert answers (list of list) to a pandas dataframe
+    df = pd.DataFrame(answers, columns=config['saliency_methods'])
+    if not os.path.exists(folder):
+        os.makedirs(folder)
+    df.to_csv(os.path.join(folder, 'results.csv'), index=False)
+    print(f"Results saved to {folder}", df)
+
+    '''
     filename = "results.txt"
     print('ooooooo', global_counter)
     print(dropdowns)
@@ -48,6 +56,7 @@ def save_results(dropdowns):
     str_dropdowns = "\n".join([str(r) for r in dropdowns])
     with open(filename, 'w') as f:
         f.write(str_dropdowns)
+    '''
 
     # Upload the file to Hugging Face Hub
     api = HfApi()