sistemato layout

app.py

@@ -22,7 +22,6 @@ def multi_classification(text):
         raise gr.Error('Il testo è obbligatorio!')  
     
 def file_change(file):
-    print(file)
     image = cv2.imread(file)
     return image
 
@@ -43,49 +42,61 @@ def retina_classification(retina):
         
 with gr.Blocks() as demo:
     gr.Markdown("# NGT AI Platform")
-    with gr.Tab("binary classification"):
+    with gr.Tab("Sentiment Analysis"):
         gr.Markdown("Inviato il testo di una recensione, il servizio effettua classifica lo stesso come Positivo o Negativo")
-        binary_classification_input = gr.Textbox()
-        binary_classification_output = gr.Label()
-        binary_classification_button = gr.Button("Submit")
-    with gr.Tab("multiclass classification"):
+        with gr.Row(equal_height=True):
+            with gr.Column():
+                binary_classification_input = gr.Textbox()
+            with gr.Column(scale=1):
+                binary_classification_output = gr.Label()
+                binary_classification_button = gr.Button("Analizza")
+    with gr.Tab("Review Classification"):
         gr.Markdown("""
                     Inviato un testo in inglese, il servizio classifica lo stesso come:\n
                     'alt.atheism', 'comp.graphics', 'comp.os.ms-windows.misc', 'comp.sys.ibm.pc.hardware', 'comp.sys.mac.hardware', 'comp.windows.x', 'misc.forsale', 'rec.autos', 'rec.motorcycles', 'rec.sport.baseball', 'rec.sport.hockey', 'sci.crypt', 'sci.electronics', 'sci.med', 'sci.space', 'soc.religion.christian', 'talk.politics.guns', 'talk.politics.mideast',  'talk.politics.misc', 'talk.religion.misc'
                     """)
-        multi_classification_input = gr.Textbox()
-        multi_classification_output = gr.Label(num_top_classes=8)
-        multi_classification_button = gr.Button("Submit")
-    with gr.Tab("image classification"):
+        with gr.Row(equal_height=True):
+            with gr.Column():
+                multi_classification_input = gr.Textbox()
+            with gr.Column():
+                multi_classification_output = gr.Label(num_top_classes=8)
+                multi_classification_button = gr.Button("Analizza")
+    with gr.Tab("Pneumonia/Tuberculosis Detection"):
         gr.Markdown("""
                     Inviata una radiografia del petto, il servizio classifica la stessa come:\n 
                     'Tubercolosi', 'No_Tubercolosi',  'Pneumonia', 'No_Pneumonia'
                      """)
-        with gr.Row():
+        with gr.Row(equal_height=True):
             file_selected = gr.FileExplorer(
                 root_dir="data/gallery/xray",
-                file_count='single'
+                file_count='single',
+                label='Esplora',
+                height=400
             )
-            image_input = gr.Image()
-            image_output = [gr.Label()]
-        image_button = gr.Button("Submit")
+            image_input = gr.Image(show_download_button=False,
+                        show_share_button=False, sources=["upload"], height=400)
+            with gr.Column():
+                image_output = [gr.Label(label="Diagnosi", scale=1)]
+                image_button = gr.Button("Analizza")
     with gr.Tab("Diabetic Retinopathy Detection"):
         gr.Markdown("""
                     Questa interfaccia utilizza un modello di deep learning basato su rete neurale convoluzionale, in modo da predirre la presenza o meno di retinopatia diabetica a partire da un'immagine retinica
                      """)
-        with gr.Row():
+        with gr.Row(equal_height=True):
             with gr.Column(scale=1):
                 file_selected_dr = gr.FileExplorer(
                     root_dir="data/gallery/retinopaty",
                     file_count='single',
-                    height=300
+                    label='Esplora',
+                    height=400
                 )
             with gr.Column(scale=2):
                 with gr.Row():
                     image_i = gr.Image(
                         show_download_button=False,
                         show_share_button=False,
-                        sources=["upload"]
+                        sources=["upload"],
+                        height=400
                     )
                     with gr.Column():
                         image_o = [gr.Label(label="Diagnosi"), gr.Label(label="Probabilità di patologia")]
@@ -102,4 +113,4 @@ with gr.Blocks() as demo:
     file_selected_dr.change(file_change_dr, inputs=file_selected_dr, outputs=image_i)
         
 
-demo.launch(debug=True)
+demo.launch()