Proper session handling.

app.py

@@ -49,10 +49,8 @@ import typing
 import PIL
 
 
-TempDirectory      = None
-Sessions           = {}
-LastResults        = None
-SelectedECGIndex   = 0
+TempDirectory = None
+Sessions      = {}
 
 
 # ###### Print log message ##################################################
@@ -61,31 +59,15 @@ def log(logstring):
           ': ' + logstring + '\x1b[0m'));
 
 
-# ###### Make a unique session ID ###########################################
-SessionCounterLock = threading.Lock()
-SessionCounter     = 0
-def generateSessionID():
-   global SessionCounterLock
-   global SessionCounter
-
-   SessionCounterLock.acquire()
-   SessionCounter = SessionCounter + 1
-   sessionID      = SessionCounter
-   SessionCounterLock.release()
-   print(f'SessionID={sessionID}')
-
-   return sessionID
-
-
-
 # ###### DeepFakeECG Plus Session (session with web browser) ################
 class Session:
 
    # ###### Constructor #####################################################
    def __init__(self):
-      self.Lock    = threading.Lock()
-      self.Counter = 0
-      self.Results = None
+      self.Lock     = threading.Lock()
+      self.Counter  = 0
+      self.Results  = None
+      self.Selected = 0
 
    # ###### Increment counter ###############################################
    def increment(self):
@@ -115,27 +97,19 @@ def incrementCounter(request: gradio.Request):
    log(f'ERROR: Session "{request.session_hash}" is not initialized!')
 
 
-# ###### Get last results ###################################################
-def getLastResults() -> list:
-   return LastResults
-
-
-# ###### Get last result ####################################################
-def getLastResult(index: int) -> torch.Tensor:
-   if LastResults != None:
-      return LastResults[index]
-   return None
-
 
 # ###### Generate ECGs ######################################################
-def predict(numberOfECGs       = 1,
-            # ecgLengthInSeconds = 10,
-            ecgTypeString      = 'ECG-12',
-            generatorModel     = 'Default',
-           ) -> list:
+def predict(numberOfECGs:         int = 1,
+            # ecgLengthInSeconds: int = 10,
+            ecgTypeString:        str = 'ECG-12',
+            generatorModel:       str = 'Default',
+            request:              gradio.Request = None) -> list:
 
    ecgLengthInSeconds = 10
 
+   log(f'Session "{request.session_hash}": Generate EGCs!')
+
+
    # ====== Set ECG type ====================================================
    ecgType = deepfakeecg.DATA_ECG12
    if ecgTypeString == 'ECG-8':
@@ -151,19 +125,19 @@ def predict(numberOfECGs       = 1,
    # print(matplotlib.ticker.Locator.MAXTICKS)
 
    # ====== Generate the ECGs ===============================================
-   global LastResults
-   LastResults = deepfakeecg.generateDeepfakeECGs(numberOfECGs,
-                                                  ecgType            = ecgType,
-                                                  ecgLengthInSeconds = ecgLengthInSeconds,
-                                                  ecgScaleFactor     = 6,
-                                                  outputFormat       = deepfakeecg.OUTPUT_TENSOR,
-                                                  showProgress       = False,
-                                                  runOnDevice        = runOnDevice)
+   Sessions[request.session_hash].Results = \
+      deepfakeecg.generateDeepfakeECGs(numberOfECGs,
+                                       ecgType            = ecgType,
+                                       ecgLengthInSeconds = ecgLengthInSeconds,
+                                       ecgScaleFactor     = 6,
+                                       outputFormat       = deepfakeecg.OUTPUT_TENSOR,
+                                       showProgress       = False,
+                                       runOnDevice        = runOnDevice)
 
    # ====== Create a list of image/label tuples for gradio.Gallery ==========
    plotList  = []
    ecgNumber = 1
-   for result in LastResults:
+   for result in Sessions[request.session_hash].Results:
 
       # ====== Plot ECG =====================================================
       result = result.t().detach().cpu().numpy()
@@ -199,15 +173,13 @@ def predict(numberOfECGs       = 1,
 
 
 # ###### Select ECG in the gallery ##########################################
-def select(event: gradio.SelectData):
+def select(event:   gradio.SelectData,
+           request: gradio.Request):
    # Get selection index from Gallery select() event:
    # https://github.com/gradio-app/gradio/issues/1976#issuecomment-1726018500
 
-   global SelectedECGIndex
-   SelectedECGIndex = event.index
-   print(f'Selected #{SelectedECGIndex}!')
-
-   # return event.value
+   Sessions[request.session_hash].Selected = event.index
+   log(f'Session "{request.session_hash}": Selected ECG #{Sessions[request.session_hash].Selected + 1}')
 
 
 # ###### Produce CSV file from Tensor #######################################
@@ -230,25 +202,23 @@ def dataToCSV(data, outputFileName, ecgType = deepfakeecg.DATA_ECG12) -> sys.pat
 
 
 # ###### Download CSV #######################################################
-def downloadCSV(request: gradio.Request) -> None:
+def downloadCSV(request: gradio.Request):
    log(f'Session "{request.session_hash}": Download CSV file')
 
 
 # ###### Download PDF #######################################################
-def downloadPDF(request: gradio.Request) -> None:
+def downloadPDF(request: gradio.Request):
    log(f'Session "{request.session_hash}": Download PDF file')
 
 
 # ###### Analyze the selected ECG ###########################################
-def analyze(request: gradio.Request) -> None:
+def analyze(request: gradio.Request):
 
-   log(f'Session "{request.session_hash}": Analyze #{SelectedECGIndex}!')
+   log(f'Session "{request.session_hash}": Analyze ECG #{Sessions[request.session_hash].Selected + 1}!')
 
-   data = getLastResult(SelectedECGIndex)
+   data = Sessions[request.session_hash].Results[Sessions[request.session_hash].Selected]
    print(data)
 
-   return None
-
 
 
 # ###### Main program #######################################################
@@ -326,10 +296,6 @@ with gradio.Blocks(css = css, theme = gradio.themes.Glass(secondary_hue=gradio.t
    # Session clean-up, to be called when page is closed/refreshed
    gui.unload(cleanUpSession)
 
-   # ====== Unique session ID for this instance =============================
-   sessionID = gradio.State(0)
-   gui.load(generateSessionID, outputs = [ sessionID ])
-
    # ====== Header ==========================================================
    big_block = gradio.HTML("""
 <div class="header">