created template

app.py

@@ -14,10 +14,14 @@ DATASETS = [
 ]
 MAX_N_LABELS = 5
 SPLIT_TO_CLASSIFY = 'pasta'
-COL1, COL2 = st.columns([3, 1])
-CONTAINER_TOP = st.container()
-CONTAINER_BODY = st.container()
 
+# COL1, COL2 = st.columns([3, 1])
+# CONTAINER_TOP = st.container()
+# CONTAINER_BODY = st.container()
+# CONTAINER_FULL = st.container()
+# CONTAINER_LOOP = st.container()
+COL1, COL2
+CONTAINER_TOP,  CONTAINER_BODY, CONTAINER_FULL, CONTAINER_LOOP
 
 
 
@@ -55,7 +59,7 @@ def classify_full_dataset(shosen_dataset_name, chosen_model_name):
      
     #dataset
     dataset = load_dataset(shosen_dataset_name,"testedata_readme")
-    with COL2:
+    with CONTAINER_LOOP:
         #Image teste load 
         image_object = dataset['pasta'][0]["image"]
         st.image(image_object, caption="Uploaded Image", width=300)
@@ -63,35 +67,41 @@ def classify_full_dataset(shosen_dataset_name, chosen_model_name):
         
     #modle instance
     classifier_pipeline = pipeline('image-classification', model=chosen_model_name)
-    st.write("### FLAG 4")
+    CONTAINER_LOOP.write("### FLAG 4")
     
     #classification
     classification_result = classifier_pipeline(image_object)
-    st.write(classification_result)
-    st.write("### FLAG 5")
+    CONTAINER_LOOP.write(classification_result)
+    CONTAINER_LOOP.write("### FLAG 5")
     #classification_array.append(classification_result)
     
     #save classification
     
     image_count += 1
+    CONTAINER_LOOP.write(f"Image count: {image_count}")
     
     return image_count
 
+
 def make_template():
-    #st.write("### FLAG 1")
-    #st.write("### FLAG 2")
-    pass
+    CONTAINER_FULL = st.container()
+    with CONTAINER_FULL:
+        CONTAINER_TOP = st.container()
+        CONTAINER_BODY = st.container()
+        with CONTAINER_BODY:  
+            COL1, COL2 = st.columns([3, 1])
+            with COL2:
+                CONTAINER_LOOP = st.container()
+
 
 def main():
     
     make_template()
     
-    with CONTAINER_TOP:
-        st.title("Bulk Image Classification DEMO")
-    #CONTAINER_TOP.title("Bulk Image Classification DEMO")
-    
+    CONTAINER_TOP.title("Bulk Image Classification DEMO")
+
     
-    # Restart or reset your app
+    # TODO Restart or reset your app
     # if st.button("Restart"):
     # # Code to restart or reset your app goes here
         #     import subprocess
@@ -103,29 +113,27 @@ def main():
             st.markdown("This app uses several 🤗 models to classify images stored in 🤗 datasets.")
             st.write("Soon we will have a dataset template")
 
-
-
-            #Model
-            chosen_model_name = st.selectbox("Select the model to use",  MODELS, index=0)
-            if chosen_model_name is not None:
-                st.write("You selected", chosen_model_name) 
-
-            #Dataset
-            shosen_dataset_name = st.selectbox("Select the dataset to use",  DATASETS, index=0)
-            if shosen_dataset_name is not None:
-                st.write("You selected", 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 st.button("Classify images"):
-                
-                    #classification_array =[]
-                    classification_result = classify_full_dataset(shosen_dataset_name, chosen_model_name)
-                    st.write(f"Classification result: {classification_result}")
-                    #classification_array.append(classification_result)
-                    #st.write("# FLAG 6")
-                    #st.write(classification_array)   
+        #Model
+        chosen_model_name = st.selectbox("Select the model to use",  MODELS, index=0)
+        if chosen_model_name is not None:
+            COL1.st.write("You selected", chosen_model_name) 
+
+        #Dataset
+        shosen_dataset_name = st.selectbox("Select the dataset to use",  DATASETS, index=0)
+        if shosen_dataset_name is not None:
+            COL1.st.write("You selected", 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 COL1.button("Classify images"):
+            
+                #classification_array =[]
+                classification_result = classify_full_dataset(shosen_dataset_name, chosen_model_name)
+                CONTAINER_LOOP.write(f"Classification result: {classification_result}")
+                #classification_array.append(classification_result)
+                #st.write("# FLAG 6")
+                #st.write(classification_array)   
 
 if __name__ == "__main__":
     main()