aggiunta servizio Retina e riorganizzazione albertatura immagini

app.py

@@ -1,7 +1,9 @@
 import gradio as gr
+import tensorflow as tf
 from modules.binary_classification import binary_classification as binary
 from modules.image_classification import image_classification as image
 from modules.multilabel_classification import multi_classification as multi
+from modules.retina import predict_diabetic_retinopathy as retina_detector
 import cv2
 
 def binary_classification(text):
@@ -20,6 +22,7 @@ def multi_classification(text):
         raise gr.Error('Il testo è obbligatorio!')  
     
 def file_change(file):
+    print(file)
     image = cv2.imread(file)
     return image
 
@@ -28,8 +31,16 @@ def image_classification(img):
         return image(img)
     except:
         raise gr.Error('L\'immagine è obbligatoria!')
-        
 
+def file_change_dr(file):
+    return file
+
+def retina_classification(retina):
+    try:
+        return retina_detector(retina)
+    except:
+        raise gr.Error('L\'immagine è obbligatoria!')
+        
 with gr.Blocks() as demo:
     gr.Markdown("# NGT AI Platform")
     with gr.Tab("binary classification"):
@@ -52,17 +63,43 @@ with gr.Blocks() as demo:
                      """)
         with gr.Row():
             file_selected = gr.FileExplorer(
-                root="data/gallery/",
+                root_dir="data/gallery/xray",
                 file_count='single'
             )
             image_input = gr.Image()
-            image_output = gr.Label()
+            image_output = [gr.Label()]
         image_button = gr.Button("Submit")
+    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.Column(scale=1):
+                file_selected_dr = gr.FileExplorer(
+                    root_dir="data/gallery/retinopaty",
+                    file_count='single',
+                    height=300
+                )
+            with gr.Column(scale=2):
+                with gr.Row():
+                    image_i = gr.Image(
+                        show_download_button=False,
+                        show_share_button=False,
+                        sources=["upload"]
+                    )
+                    with gr.Column():
+                        image_o = [gr.Label(label="Diagnosi"), gr.Label(label="Probabilità di patologia")]
+                        image_button_dr = gr.Button("Analizza")
+                    
+
 
     binary_classification_button.click(binary_classification, inputs=binary_classification_input, outputs=binary_classification_output)
     multi_classification_button.click(multi_classification, inputs=multi_classification_input, outputs=multi_classification_output)
     image_button.click(image_classification, inputs=image_input, outputs=image_output)
+    image_button_dr.click(retina_classification, inputs=image_i, outputs=image_o)
 
     file_selected.change(file_change, inputs=file_selected, outputs=image_input)
+    file_selected_dr.change(file_change_dr, inputs=file_selected_dr, outputs=image_i)
+        
 
 demo.launch(debug=True, share=True)