Some progress.

app.py

@@ -35,15 +35,52 @@
 
 import deepfakeecg
 import ecg_plot
-import io
 import gradio
+import io
 import matplotlib.pyplot as plt
 import matplotlib.ticker
+import random
+import sys
+import tempfile
+import threading
 import torch
 import typing
 import PIL
 
 
+TempDirectory      = None
+LastResults        = None
+SelectedECGIndex   = 0
+
+
+# ###### 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
+
+
+# ###### 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,
@@ -68,18 +105,19 @@ def predict(numberOfECGs       = 1,
    # print(matplotlib.ticker.Locator.MAXTICKS)
 
    # ====== Generate the ECGs ===============================================
-   results = deepfakeecg.generateDeepfakeECGs(numberOfECGs,
-                                              ecgType            = ecgType,
-                                              ecgLengthInSeconds = ecgLengthInSeconds,
-                                              ecgScaleFactor     = 6,
-                                              outputFormat       = deepfakeecg.OUTPUT_TENSOR,
-                                              showProgress       = False,
-                                              runOnDevice        = runOnDevice)
+   global LastResults
+   LastResults = 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 results:
+   for result in LastResults:
 
       # ====== Plot ECG =====================================================
       result = result.t().detach().cpu().numpy()
@@ -114,6 +152,59 @@ def predict(numberOfECGs       = 1,
    return plotList
 
 
+# ###### Select ECG in the gallery ##########################################
+def select(event: gradio.SelectData):
+   # 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
+
+
+# ###### Produce CSV file from Tensor #######################################
+def dataToCSV(data, outputFileName, ecgType = deepfakeecg.DATA_ECG12) -> sys.path:
+
+   data = generatedECG.detach().cpu().numpy()
+
+   if ecgType == deepfakeecg.DATA_ECG8:
+      header = 'Timestamp,LeadI,LeadII,V1,V2,V3,V4,V5,V6'
+   elif ecgType == deepfakeecg.DATA_ECG12:
+      header = 'Timestamp,LeadI,LeadII,V1,V2,V3,V4,V5,V6,LeadIII,aVL,aVR,aVF'
+   else:
+      raise Exception('Invalid ECG type!')
+
+   numpy.savetxt(outputFileName, data,
+                  header    = header,
+                  comments  = '',
+                  delimiter = ',',
+                  fmt       = '%i')
+
+
+# ###### Download CSV #######################################################
+def downloadCSV(sessionID) -> None:
+   print(f'CSV #{SelectedECGIndex}!')
+   print(f"sessionID={sessionID}")
+
+# ###### Download PDF #######################################################
+def downloadPDF(sessionID) -> None:
+   print(f'PDF #{SelectedECGIndex}!')
+   print(f"sessionID={sessionID}")
+
+
+# ###### Analyze the selected ECG ###########################################
+def analyze() -> None:
+
+   print(f'Analyze #{SelectedECGIndex}!')
+
+   data = getLastResult(SelectedECGIndex)
+   print(data)
+
+   return None
+
+
 
 # ###### Main program #######################################################
 
@@ -180,8 +271,15 @@ img.logo-image {
 }
 """
 
+
 # ====== Create GUI =========================================================
 with gradio.Blocks(css = css, theme = gradio.themes.Glass(secondary_hue=gradio.themes.colors.blue)) as gui:
+
+   # ====== Unique session ID for this instance =============================
+   sessionID = gradio.State(0)
+   gui.load(generateSessionID, outputs = [ sessionID ])
+
+   # ====== Header ==========================================================
    big_block = gradio.HTML("""
 <div class="header">
    <div class="logo-left">
@@ -199,12 +297,19 @@ with gradio.Blocks(css = css, theme = gradio.themes.Glass(secondary_hue=gradio.t
       # sliderLengthInSeconds = gradio.Slider(5, 60, label="Length (s)", step = 5, value = 10, interactive = True)
       dropdownType           = gradio.Dropdown( [ 'ECG-12', 'ECG-8' ], label = 'ECG Type', interactive = True)
       dropdownGeneratorModel = gradio.Dropdown( [ 'Default' ], label = 'Generator Model', interactive = True)
-      buttonGenerate         = gradio.Button("Generate")
+      with gradio.Column():
+         buttonGenerate = gradio.Button("Generate ECGs!")
+         buttonAnalyze  = gradio.Button("Analyze this ECG!")
+         with gradio.Row():
+            buttonCSV = gradio.Button("Download CSV")
+            buttonPDF = gradio.Button("Download PDF")
    gradio.Markdown('## Output')
    with gradio.Row():
       outputGallery = gradio.Gallery(label = 'output', columns = [ 1 ], height = 'auto',
                                      show_label = True,
                                      preview = True)
+      outputGallery.select(select)
+   gradio.Markdown('## Analysis')
 
    # ====== Add click event handling for "Generate" button ==================
    buttonGenerate.click(predict,
@@ -215,6 +320,13 @@ with gradio.Blocks(css = css, theme = gradio.themes.Glass(secondary_hue=gradio.t
                         outputs = [ outputGallery ]
                        )
 
+   # ====== Add click event handling for "Analyze" button ===================
+   buttonAnalyze.click(analyze)
+
+   # ====== Add click event handling for download buttons ===================
+   buttonCSV.click(downloadCSV, inputs = [ sessionID ])
+   buttonPDF.click(downloadPDF, inputs = [ sessionID ])
+
    # ====== Run on startup ==================================================
    gui.load(predict,
             inputs  = [ sliderNumberOfECGs,
@@ -226,4 +338,6 @@ with gradio.Blocks(css = css, theme = gradio.themes.Glass(secondary_hue=gradio.t
 
 # ====== Run the GUI ========================================================
 if __name__ == "__main__":
-   gui.launch()
+   TempDirectory = tempfile.TemporaryDirectory('DeepFakeECGPlus')
+   gui.launch(allowed_paths = [ TempDirectory ])
+   TempDirectory.cleanup()