1st version is ready to teste

app.py

@@ -5,28 +5,66 @@ from datasets import load_dataset, Image, list_datasets
 from PIL import Image
 
 MODELS = [
+            "",
             "google/vit-base-patch16-224", #Classifição geral
             "nateraw/vit-age-classifier" #Classifição de idade
 ]
+DATASETS = [
+            "",
+            "NunT/vit-base-patch16-224", #Classifição geral
+            "NunT/vit-age-classifier" #Classifição de idade
+]
 MAX_N_LABELS = 5
 
 
+def classify_images(classifier_model, dataset_to_classify):
+       
+    for image in dataset:
+        st("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("----")
+    '''
+
+
 
 def main():
     st.title("Bulk Image Classification")
     st.markdown("This app uses several 🤗 models to classify images stored in 🤗 datasets.")
     st.write("Soon we will have a dataset template")
     st.write("**Soon we will have dataset selector**")   
-    st.text("Select a model to use:")
-    st.get_option("model", MODELS)
-    dataset = load_dataset("Nunt/testedata","testedata_readme")          
-    st.markdown("The models available are:")
-    st.markdown("**PUT IT HERE**")
-    
-    
-    dataset = load_dataset("Nunt/testedata","testedata_readme")
-    
     
+
+    '''
+        Model
+    '''
+    shosen_model_name = st.selectbox("Select the model to use",  MODELS)
+    if shosen_model is not None:
+        st.write("You selected", shosen_model_name) 
+        
+    '''
+        Dataset
+    '''          
+    shosen_dataset_name =st.radio("Select the model to use",  MODELS)
+    if shosen_dataset is not None:
+        st.write("You selected", shosen_dataset_name)
+        image_object = dataset['pasta'][0]["image"]
+   
+        
+        
+        
+    if shosen_model is not None and shosen_dataset is not None:
+        st.image(shosen_dataset[0], caption="Uploaded Image", use_column_width=True)
+        if st.button("Classify images"):
+            dataset = load_dataset("Nunt/testedata","testedata_readme")
+            classifier = pipeline('image-classification', model=model_name, device=0)
+            classify_images(classifier, dataset)
+
+
     
 
 if __name__ == "__main__":