Update app.py

app.py

@@ -1,8 +1,8 @@
-import sys
-from datetime import date
 import gradio as gr
+import numpy as np
 import pandas as pd
 from pickle import load
+from datetime import date
 from radiomics import featureextractor
 
 extractor3D = featureextractor.RadiomicsFeatureExtractor("3DParams.yaml")
@@ -23,43 +23,40 @@ class TextStream:
 
 def image_classifier(image, segment):
   features3D = extractor3D.execute(imageFilepath=image, maskFilepath=segment)
-  keys = [key for key in features3D.keys()]
-  values = [value for value in features3D.values()]
-  sortedLists = sorted(list(zip(keys, values)), key=lambda x: x[0])
-  sortedKeys, sortedValues = zip(*sortedLists)    
-  original_stdout = sys.stdout
-  text_stream = TextStream()
-  sys.stdout = text_stream
-  print(*sortedValues, sep="\n")
-  sys.stdout = original_stdout
-  sortedValues = text_stream.data[4:7] + text_stream.data[15:17] + text_stream.data[22:]
-  dataframe = pd.DataFrame(
-    data=sortedValues,
-  )
-  dataframe = dataframe.transpose()
+  dict = {}
+  for key, value in zip(features3D.keys(), features3D.values()):
+    if isinstance(value, np.ndarray):
+      dict[key] = [value.tolist()]
+    else:
+      dict[key] = [value]
+  dataframe = pd.DataFrame(dict).select_dtypes(exclude=["object"]).to_numpy()
   prediction = loaded_model.predict_proba(dataframe).tolist()[0]
   return {"Grade 1": prediction[0], "Grade 2": prediction[1]}
 
 def logging(image, label_output):
-  dataframe = pd.DataFrame(data={
-    "Imagen": image,
-    "Grado 1": label_output.values()[0],
-    "Grado 2": label_output.values()[1],
-    "Observación": "",
-    "Fecha": date.today(),
-    "Acción": f"[Descarga]({image})"
-  })
-  dataframe.to_csv(path_or_buf="flagged/log.csv", sep=";")
+  grade1 = list(label_output.values())[0]
+  grade2 = list(label_output.values())[1]
+  now = date.today()
+  dictionary = {
+    "Imagen": [image],
+    "Grado 1": [grade1],
+    "Grado 2": [grade2],
+    "Observación": [""],
+    "Fecha": [now.strftime("%d/%m/%Y")],
+    "Acción": [f"\"\"\"[Descargar]({image})\"\"\""]
+  }
+  dataframe = pd.DataFrame(data=dictionary)
+  dataframe.to_csv(path_or_buf="log.csv", sep=";", mode="a", index=False)
   print(dataframe)
 
 # Logger = gr.SimpleCSVLogger()
 
 with gr.Blocks(title="Historial de diagnósticos") as ViewingHistory:
+  dataframe = pd.read_csv(filepath_or_buffer="log.csv", sep=";")
   gr.Dataframe(
+    value=dataframe,
     headers=["Imagen", "Grado 1", "Grado 2", "Observación", "Fecha", "Acción"],
     datatype=["str", "number", "number", "str", "date", "markdown"],
-    row_count=(3, "dynamic"),
-    col_count=(6, "dynamic"),
     type="pandas",
     wrap=True
   )