Merge branch 'feature/loop_dataset'

app.py

@@ -16,85 +16,47 @@ MAX_N_LABELS = 5
 SPLIT_TO_CLASSIFY = 'pasta'
 
 COLS = st.columns([0.75, 0.25]) 
-#SCROLLABLE_TEXT = COLS[1].text_area("Conteúdo da segunda coluna", height=500)
 SCROLLABLE_TEXT = COLS[1].container(height=500)
 
 
-
-def classify_one_image(classifier_model, dataset_to_classify):
-       
-       
-          
-    #image_object = dataset[SPLIT_TO_CLASSIFY][i]["image"]
-    #st.image(image_object, caption="Uploaded Image", width=300)
-
-       
-    #for i in range(len(dataset_to_classify)): 
-    #for image in dataset_to_classify:
-        #image_object = dataset[SPLIT_TO_CLASSIFY][i]["image"]
-        #st.image(image_object, caption="Uploaded Image", width=300)
-        
-        #st.write(f"Image classification: ", image['file'])
-
-        # image_path = image['file']
-        # img = Image.open(image_path)
-        # st.image(img, caption="Original image", use_column_width=True)
-        # results = classifier(image_path, top_k=MAX_N_LABELS)
-        # st.write(results)
-        # st.write("----")
-    
-    return "done" 
-
-
-
 def classify_full_dataset(shosen_dataset_name, chosen_model_name):
     image_count = 0
      
+    #modle instance
+    classifier_pipeline = pipeline('image-classification', model=chosen_model_name)    
+        
     #dataset
-    dataset = load_dataset(shosen_dataset_name,"testedata_readme")
-    #with SCROLLABLE_TEXT:
+    dataset = load_dataset(shosen_dataset_name,"testedata_readme") 
+     
+    for i in range(len(dataset)):
+        SCROLLABLE_TEXT.write("i-1:" + str(i-1))
+        image_object = dataset['pasta'][i-1]["image"]
+        SCROLLABLE_TEXT.image(image_object, caption="Uploaded Image", width=300)
+        #classification
+        classification_result = classifier_pipeline(image_object)
+        SCROLLABLE_TEXT.write(classification_result)
+        #TODO save classification result in dataset
+        image_count += 1
+        SCROLLABLE_TEXT.write(f"Image count" + str(image_count))
+        #SCROLLABLE_TEXT.write(image_count)    
+
+ 
     #Image teste load 
-    image_object = dataset['pasta'][0]["image"]
-    
-    SCROLLABLE_TEXT.image(image_object, caption="Uploaded Image", width=300)
-    #st.write("### FLAG 3")
-        
-    #modle instance
-    classifier_pipeline = pipeline('image-classification', model=chosen_model_name)
-    #COLS[1].write("### FLAG 4")
-    
-    #classification
-    classification_result = classifier_pipeline(image_object)
-    SCROLLABLE_TEXT.write(classification_result)
-    #COLS[1].write("### FLAG 5")
-    #classification_array.append(classification_result)
+    #image_object = dataset['pasta'][0]["image"]
+    #SCROLLABLE_TEXT.image(image_object, caption="Uploaded Image", width=300)
+
+
     
-    #save classification
     
-    image_count += 1
-    SCROLLABLE_TEXT.write("Image count")
-    SCROLLABLE_TEXT.write(image_count)  
-    return image_count
-
 
-def make_template():
     
-    tile = CONTAINER_TOP.title(":balloon:")
-    tile.title(":balloon:")
     
-    with CONTAINER_FULL:
-        CONTAINER_TOP.title("titulo de teste dentro do container CONTAINER_TOP")
-        with CONTAINER_BODY:  
-            #COL1, COL2 = st.columns([3, 1])
-            with COLS[1]:
-                CONTAINER_LOOP.write("### OUTPUT")
+    
 
 
 def main():
-    
      
     COLS[0].write("# Bulk Image Classification App")
-    
       
     #with CONTAINER_BODY:
     with COLS[0]:
@@ -114,17 +76,13 @@ def main():
         COLS[0].write(shosen_dataset_name)
         
     #click to classify
-    #image_object = dataset['pasta'][0]  
     if chosen_model_name is not None and shosen_dataset_name is not None:
         if COLS[0].button("Classify images"):
         
-            #classification_array =[]
-            classification_result = classify_full_dataset(shosen_dataset_name, chosen_model_name)
+            classify_full_dataset(shosen_dataset_name, chosen_model_name)
             COLS[0].write("Classification result {classification_result}")
             COLS[0].write(classification_result)
-            #classification_array.append(classification_result)
-            #st.write("# FLAG 6")
-            #st.write(classification_array) 
+ 
                   
 if __name__ == "__main__":
     main()

hf_bulk_image_classifier.code-workspace

@@ -0,0 +1,29 @@
+{
+	"folders": [
+		{
+			"path": "."
+		}
+	],
+	"settings": {
+		"workbench.colorCustomizations": {
+			"activityBar.activeBackground": "#fa1b49",
+			"activityBar.background": "#fa1b49",
+			"activityBar.foreground": "#e7e7e7",
+			"activityBar.inactiveForeground": "#e7e7e799",
+			"activityBarBadge.background": "#155e02",
+			"activityBarBadge.foreground": "#e7e7e7",
+			"commandCenter.border": "#e7e7e799",
+			"sash.hoverBorder": "#fa1b49",
+			"statusBar.background": "#dd0531",
+			"statusBar.foreground": "#e7e7e7",
+			"statusBarItem.hoverBackground": "#fa1b49",
+			"statusBarItem.remoteBackground": "#dd0531",
+			"statusBarItem.remoteForeground": "#e7e7e7",
+			"titleBar.activeBackground": "#dd0531",
+			"titleBar.activeForeground": "#e7e7e7",
+			"titleBar.inactiveBackground": "#dd053199",
+			"titleBar.inactiveForeground": "#e7e7e799"
+		},
+		"peacock.color": "#dd0531"
+	}
+}