Updated for V2.0

tab_manager.py

@@ -7,12 +7,14 @@ import pandas as pd
 import numpy as np
 import json
 import csv
+import string
 from itertools import cycle
 import random
 import time
 import datetime
 import zipfile
 from io import BytesIO, StringIO
+
 def completed_setup(tabs, modelID):
     with tabs[0]:
         st.write("\U0001F917 ", modelID, " has been loaded!")
@@ -29,13 +31,15 @@ def completed_setup(tabs, modelID):
         general_bias_eval_setup(tabs[0], modelID, tabs[3])
     with tabs[1]:
         task_oriented_bias_eval_setup(tabs[1],modelID, tabs[3])
+        
 def general_bias_eval_setup(tab, modelID, imagesTab):
 
     generalBiasSetupDF_EVAL = pd.DataFrame(
         {
             "GEN Eval. Variable": ["No. Images to Generate per prompt", "No. Inference Steps",
-                                   "Image Size - must be a value that is 2 to the power of N"],
-            "GEN Values": ["2", "10", "512"],
+                                   "Image Height - must be a value that is 2 to the power of N",
+                                   "Image Width - must be a value that is 2 to the power of N"],
+            "GEN Values": ["2", "10", "512", "512"],
         }
     )
     generalBiasSetupDF_TYPE = pd.DataFrame(
@@ -84,13 +88,13 @@ def general_bias_eval_setup(tab, modelID, imagesTab):
             )
         st.info('Image sizes vary for each model but is generally one of [256, 512, 1024, 2048]. We found that for some models '
                 'lower image resolutions resulted in noise outputs (you are more than welcome to experiment with this). '
-                'Consult the model card if you are unsure what image resolution to use. Rectangular '
-                'image sizes will be supported soon \U0001F601.', icon="ℹ️")
-        if not all([GENValTable["GEN Values"][0].isnumeric(), GENValTable["GEN Values"][1].isnumeric(), GENValTable["GEN Values"][2].isnumeric()]):
+                'Consult the model card if you are unsure what image resolution to use.', icon="ℹ️")
+        if not all([GENValTable["GEN Values"][0].isnumeric(), GENValTable["GEN Values"][1].isnumeric(), 
+                    GENValTable["GEN Values"][2].isnumeric(), GENValTable["GEN Values"][3].isnumeric()]):
             st.error('Looks like you have entered non-numeric values! '
                      'Please enter numeric values in the table above', icon="🚨")
         # elif not all([check_for_power_of_two(int(GENValTable["GEN Values"][2])), int(GENValTable["GEN Values"][2]) >= 8]):
-        elif int(GENValTable["GEN Values"][2]) < 8:
+        elif any(int(GENValTable["GEN Values"][2]), int(GENValTable["GEN Values"][3])) < 8:
             st.error('Please ensure that your image resolution is 1 number greater than 8. Consult the model card to find the size of the images used'
                      ' to train the model. Incompatible image resolutions may result in noisy output images', icon="🚨")
         else:
@@ -123,8 +127,9 @@ def task_oriented_bias_eval_setup(tab, modelID, imagesTab):
     biasSetupDF_EVAL = pd.DataFrame(
         {
             "TO Eval. Variable": ["No. Images to Generate per prompt", "No. Inference Steps",
-                                  "Image Size - must be a value that is 2 to the power of N"],
-            "TO Values": ["2", "10", "512"],
+                                   "Image Height - must be a value that is 2 to the power of N",
+                                   "Image Width - must be a value that is 2 to the power of N"],
+            "TO Values": ["2", "10", "512", "512"],
         }
     )
     with tab:
@@ -150,16 +155,16 @@ def task_oriented_bias_eval_setup(tab, modelID, imagesTab):
             num_rows="fixed",
         )
         st.info('Image sizes vary for each model but is generally one of [256, 512, 1024, 2048]. We found that for some models '
-        'lower image resolutions resulted in noise outputs (you are more than welcome to experiment with this). '
-        'Consult the model card if you are unsure what image resolution to use. Rectangular '
-        'image sizes will be supported soon \U0001F601.', icon="ℹ️")
+                'lower image resolutions resulted in noise outputs (you are more than welcome to experiment with this). '
+                'Consult the model card if you are unsure what image resolution to use.', icon="ℹ️")
         target = st.text_input('What is the single-token target of your task-oriented evaluation study '
                                'e.g.: "burger", "coffee", "men",  "women"')
 
-        if not all([TOValTable["TO Values"][0].isnumeric(), TOValTable["TO Values"][1].isnumeric(), TOValTable["TO Values"][2].isnumeric()]):
+        if not all([TOValTable["TO Values"][0].isnumeric(), TOValTable["TO Values"][1].isnumeric(), 
+                    TOValTable["TO Values"][2].isnumeric(), TOValTable["TO Values"][3].isnumeric()]):
             st.error('Looks like you have entered non-numeric values! '
                      'Please enter numeric values in the table above', icon="🚨")
-        elif int(TOValTable["TO Values"][2]) < 8:
+        elif any(int(TOValTable["TO Values"][2]), int(TOValTable["TO Values"][3])) < 8:
             st.error('Please ensure that your image resolution is 1 number greater than 8. Consult the model card to find the size of the images used'
                      ' to train the model. Incompatible image resolutions may result in noisy output images', icon="🚨")
         else:
@@ -236,7 +241,7 @@ def initiate_general_bias_evaluation(tab, modelID, specs, imagesTab):
         with infoColumn1:
             st.write(" ***No. Images per prompt*** = ", specs[0]["GEN Values"][0])
             st.write(" ***No. Steps*** = ", specs[0]["GEN Values"][1])
-            st.write(" ***Image Size*** = ", specs[0]["GEN Values"][2], "$\\times$", specs[0]["GEN Values"][2])
+            st.write(" ***Image Size*** = ", specs[0]["GEN Values"][2], "$\\times$", specs[0]["GEN Values"][3])
         with infoColumn2:
             st.write(" ***Objects*** = ", specs[1]["Check"][0])
             st.write(" ***Objects and Actions*** = ", specs[1]["Check"][1])
@@ -273,7 +278,8 @@ def initiate_general_bias_evaluation(tab, modelID, specs, imagesTab):
             OBJECTprogressBar = st.progress(0, text="Generating Object-related images. Please wait.")
             objectImages, objectCaptions = MINFER.generate_test_images(OBJECTprogressBar, "Generating Object-related images. Please wait.",
                                                                        objectPrompts, int(specs[0]["GEN Values"][0]),
-                                                                       int(specs[0]["GEN Values"][1]), int(specs[0]["GEN Values"][2]))
+                                                                       int(specs[0]["GEN Values"][1]), int(specs[0]["GEN Values"][2]),
+                                                                       int(specs[0]["GEN Values"][3]))
             evaluationImages+=objectImages
             evaluationCaptions+=objectCaptions[0]
             TXTObjectPrompts = ""
@@ -282,7 +288,8 @@ def initiate_general_bias_evaluation(tab, modelID, specs, imagesTab):
             OCCprogressBar = st.progress(0, text="Generating Occupation-related images. Please wait.")
             occupationImages, occupationCaptions = MINFER.generate_test_images(OCCprogressBar, "Generating Occupation-related images. Please wait.",
                                                                                occupationPrompts, int(specs[0]["GEN Values"][0]),
-                                                                               int(specs[0]["GEN Values"][1]), int(specs[0]["GEN Values"][2]))
+                                                                               int(specs[0]["GEN Values"][1]), int(specs[0]["GEN Values"][2]),
+                                                                               int(specs[0]["GEN Values"][3]))
             evaluationImages += occupationImages
             evaluationCaptions += occupationCaptions[0]
 
@@ -301,7 +308,7 @@ def initiate_general_bias_evaluation(tab, modelID, specs, imagesTab):
             user_evaluation_variables.DIST_BIAS = float(f"{user_evaluation_variables.EVAL_METRICS[2]:.4f}")
             user_evaluation_variables.HALLUCINATION = float(f"{np.mean(user_evaluation_variables.EVAL_METRICS[3]):.4f}")
             user_evaluation_variables.MISS_RATE = float(f"{np.mean(user_evaluation_variables.EVAL_METRICS[4]):.4f}")
-            user_evaluation_variables.EVAL_ID = MCOMP.get_evaluation_id('general', True)
+            user_evaluation_variables.EVAL_ID = ''.join(random.choices(string.ascii_letters + string.digits, k=16))
             user_evaluation_variables.DATE = datetime.datetime.utcnow().strftime('%d-%m-%Y')
             user_evaluation_variables.TIME = datetime.datetime.utcnow().strftime('%H:%M:%S')
             user_evaluation_variables.RUN_TIME = str(datetime.timedelta(seconds=elapsedTime)).split(".")[0]
@@ -323,7 +330,7 @@ def initiate_task_oriented_bias_evaluation(tab, modelID, specs, target, imagesTa
         infoColumn1, infoColumn2 = st.columns(2)
         st.write(" ***No. Images per prompt*** = ", specs["TO Values"][0])
         st.write(" ***No. Steps*** = ", specs["TO Values"][1])
-        st.write(" ***Image Size*** = ", specs["TO Values"][2], "$\\times$", specs["TO Values"][2])
+        st.write(" ***Image Size*** = ", specs["TO Values"][2], "$\\times$", specs["TO Values"][3])
         st.write(" ***Target*** = ", target.lower())
         st.markdown("___")
 
@@ -353,7 +360,8 @@ def initiate_task_oriented_bias_evaluation(tab, modelID, specs, target, imagesTa
                 TASKprogressBar = st.progress(0, text="Generating Task-oriented images. Please wait.")
                 TASKImages, TASKCaptions = MINFER.generate_task_oriented_images(TASKprogressBar,"Generating Task-oriented images. Please wait.",
                                                                        prompts, cocoIDs, int(specs["TO Values"][0]),
-                                                                       int(specs["TO Values"][1]), int(specs["TO Values"][2]))
+                                                                       int(specs["TO Values"][1]), int(specs["TO Values"][2]),
+                                                                       int(specs["TO Values"][3]))
 
                 EVALprogressBar = st.progress(0, text="Evaluating " + modelID + " Model Images. Please wait.")
                 user_evaluation_variables.EVAL_METRICS = GBM.evaluate_t2i_model_images(TASKImages, TASKCaptions[0], EVALprogressBar, False, "TASK")
@@ -367,7 +375,7 @@ def initiate_task_oriented_bias_evaluation(tab, modelID, specs, target, imagesTa
                 user_evaluation_variables.HALLUCINATION = float(f"{np.mean(user_evaluation_variables.EVAL_METRICS[3]):.4f}")
                 user_evaluation_variables.MISS_RATE = float(f"{np.mean(user_evaluation_variables.EVAL_METRICS[4]):.4f}")
                 user_evaluation_variables.TASK_TARGET = target.lower()
-                user_evaluation_variables.EVAL_ID = MCOMP.get_evaluation_id('task-oriented', True)
+                user_evaluation_variables.EVAL_ID = ''.join(random.choices(string.ascii_letters + string.digits, k=16))
                 user_evaluation_variables.DATE = datetime.datetime.utcnow().strftime('%d-%m-%Y')
                 user_evaluation_variables.TIME = datetime.datetime.utcnow().strftime('%H:%M:%S')
                 user_evaluation_variables.RUN_TIME = str(datetime.timedelta(seconds=elapsedTime)).split(".")[0]
@@ -377,6 +385,7 @@ def initiate_task_oriented_bias_evaluation(tab, modelID, specs, target, imagesTa
                 user_evaluation_variables.TASK_COCOIDs = cocoIDs
 
                 user_evaluation_variables.CURRENT_EVAL_TYPE = 'task-oriented'
+                
 def download_and_zip_images(zipImagePath, images, captions, imageType):
     if imageType == 'object':
         csvFileName = 'object_prompts.csv'