Add csv export

.gitignore

@@ -30,7 +30,6 @@ logs/
 *.log
 
 # Local configuration
-.env
 .env.local
 .env.development.local
 .env.test.local

Dockerfile

@@ -38,4 +38,4 @@ EXPOSE 7860
 WORKDIR /app
 
 # Command to run the application
-CMD ["/bin/bash", "-c", "conda run --no-capture-output -n omniseal-benchmark-backend flask run --host=0.0.0.0 --port=7860"]
+CMD ["/bin/bash", "-c", "conda run --no-capture-output -n omniseal-benchmark-backend gunicorn --chdir /app/backend -b 0.0.0.0:7870 app:app"]

backend/app.py

@@ -1,6 +1,13 @@
-from flask import Flask, send_from_directory
+from flask import Flask, Response, send_from_directory
+from flask_cors import CORS
 import os
 import logging
+import pandas as pd
+import json
+from io import StringIO
+from tools import get_old_format_dataframe  # Import your function
+import typing as tp
+import collections
 
 logger = logging.getLogger(__name__)
 if not logger.hasHandlers():
@@ -9,8 +16,10 @@ if not logger.hasHandlers():
     logger.addHandler(handler)
 
 logger.setLevel(logging.INFO)
+logger.warning("Starting the Flask app...")
 
 app = Flask(__name__, static_folder="../frontend/dist", static_url_path="")
+CORS(app)
 
 
 @app.route("/")
@@ -26,10 +35,85 @@ def data_files(filename):
     data_dir = os.path.join(os.path.dirname(__file__), "data")
     file_path = os.path.join(data_dir, filename)
     if os.path.isfile(file_path):
-        return send_from_directory(data_dir, filename)
+        # Determine file type and handle accordingly
+        df = pd.read_csv(file_path)
+        # Modify the dataframe - you'll need to define first_cols and attack_scores
+        first_cols = [
+            "snr",
+            "sisnr",
+            "stoi",
+            "pesq",
+        ]  # Define appropriate values based on your needs
+        attack_scores = [
+            "bit_acc",
+            "log10_p_value",
+            "TPR",
+            "FPR",
+        ]  # Define appropriate values based on your needs
+        categories = {
+            "speed": "Time",
+            "updownresample": "Time",
+            "echo": "Time",
+            "random_noise": "Amplitude",
+            "lowpass_filter": "Amplitude",
+            "highpass_filter": "Amplitude",
+            "bandpass_filter": "Amplitude",
+            "smooth": "Amplitude",
+            "boost_audio": "Amplitude",
+            "duck_audio": "Amplitude",
+            "shush": "Amplitude",
+            "pink_noise": "Amplitude",
+            "aac_compression": "Compression",
+            "mp3_compression": "Compression",
+        }
+
+        # This part adds on all the columns
+        df = get_old_format_dataframe(df, first_cols, attack_scores)
+
+        # Create groups based on categories
+        groups = collections.OrderedDict({"Overall": set()})
+        for k in categories.values():
+            groups[k] = set()
+
+        default_selection = set()
+        for k, v in categories.items():
+            if v not in default_selection:
+                for k in list(df.columns):
+                    if k.startswith(v):
+                        groups["Overall"].add(k)
+                        default_selection.add(k)
+
+        for col in list(df.columns):
+            for k in categories.keys():
+                if col.startswith(k):
+                    cat = categories[k]
+                    groups[cat].add(col)
+                    break
+
+        # Replace NaN values with None for JSON serialization
+        df = df.fillna(value="NaN")
+
+        # Transpose the DataFrame so each column becomes a row and column is the model
+        df = df.set_index("model").T.reset_index()
+        df = df.rename(columns={"index": "metric"})
+
+        # Convert DataFrame to JSON
+        result = {
+            "groups": {group: list(metrics) for group, metrics in groups.items()},
+            "selected": list(default_selection),
+            "rows": df.to_dict(orient="records"),
+        }
+
+        return Response(json.dumps(result), mimetype="application/json")
+        # return Response(json.dumps(result), mimetype="application/json")
+
+        # Unreachable code - this section will never execute
+        # output = StringIO()
+        # df.to_csv(output, index=False)
+        # return Response(output.getvalue(), mimetype="text/csv")
 
     return "File not found", 404
 
 
 if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860, debug=True, use_reloader=True)
+    app.run(host="0.0.0.0", port=7870, debug=True, use_reloader=True)

backend/data/sav_val_full_video_benchmark.csv

@@ -0,0 +1,34 @@
+model,attack_name,attack_variant,cat,psnr,ssim,lpips,msssim,vmaf,bit_acc,log10_p_value,TPR,FPR,decoder_time
+videoseal_0.0,Brightness,factor_0.5,Visual,12.329644482775501,0.765689172471563,0.16400888313849768,0.8749285315473875,0.6847622812499999,0.9877604258557161,-22.39911645779745,0.8020833333333334,0.0,0.04272677252689997
+videoseal_0.0,Brightness,factor_1.5,Visual,14.354039193711705,0.8878506775945425,0.1478040034805114,0.922600731253624,91.48575708333333,0.9782552175844709,-21.862761259598727,0.8333333333333334,0.0,0.042757692436377205
+videoseal_0.0,Contrast,factor_0.5,Visual,19.05058922938615,0.8292847778648138,0.14325758003784964,0.8760610688477755,0.6737453020833334,0.9880208428949118,-22.43751910161488,0.8333333333333334,0.0,0.042714941004912056
+videoseal_0.0,Contrast,factor_1.5,Visual,21.246118715557845,0.8107115399713317,0.09629796988641222,0.9281391569723686,99.27316893749999,0.9828125089406967,-22.060089082185865,0.8645833333333334,0.0,0.042758017778396606
+videoseal_0.0,Crop,size_0.55,Geometric,,,,,,0.7180989719927311,-5.084022903082193,0.0,0.0,0.041914597153663635
+videoseal_0.0,Crop,size_0.71,Geometric,,,,,,0.9520833479861418,-18.83922559512217,0.5416666666666666,0.0,0.041999004781246185
+videoseal_0.0,GaussianBlur,kernel_size_9,Visual,31.261418189283557,0.8564526836077372,0.2545727002822484,0.9441723121951023,82.53886110416666,0.9886718845615784,-22.524100619904697,0.9270833333333334,0.0,0.04526924838622411
+videoseal_0.0,Grayscale,default,Visual,25.135986236049394,1.006394298747182,0.16119166614953429,0.9570745130379995,69.34677589583333,0.5167968831956387,-0.5049506741170464,0.0,0.0,0.04505695402622223
+videoseal_0.0,H264,crf_23,Compression,38.90266357816779,0.976277074466149,0.025900887136231177,0.9912077020853758,93.4950784375,0.9773437529802322,-21.254674812157564,0.6770833333333334,0.0,0.26233071585496265
+videoseal_0.0,H264,crf_30,Compression,34.89336664070968,0.9441891511281332,0.07064529450144619,0.9780133875707785,90.57634715625,0.9298177100718021,-16.948903400945508,0.2916666666666667,0.0,0.2648838981986046
+videoseal_0.0,H264,crf_40,Compression,29.61771797047508,0.8512644742925962,0.21676112001296133,0.9216825664043427,77.12887388541667,0.7444010442122817,-6.524993251353135,0.0,0.0,0.26909273862838745
+videoseal_0.0,H264,crf_50,Compression,24.375132083999276,0.7168533718213439,0.44491561502218246,0.7669866091261307,42.544007739583336,0.5953125000620881,-1.6307919905700874,0.0,0.0,0.27051642785469693
+videoseal_0.0,H264_Crop_Brightness0,"xargs_[23, 0.71, 0.5]",Compression,,,,,,0.8328125017384688,-10.553013238178776,0.08333333333333333,0.0,0.15082495907942453
+videoseal_0.0,H264_Crop_Brightness1,"xargs_[30, 0.71, 0.5]",Compression,,,,,,0.6783854194606344,-3.9887163895481392,0.0,0.0,0.15231983363628387
+videoseal_0.0,H264_Crop_Brightness2,"xargs_[40, 0.71, 0.5]",Compression,,,,,,0.558203123199443,-1.0105757298040505,0.0,0.0,0.15606174369653067
+videoseal_0.0,H264_Crop_Brightness3,"xargs_[50, 0.71, 0.5]",Compression,,,,,,0.5205729169150194,-0.5174883076741513,0.0,0.0,0.1565847098827362
+videoseal_0.0,H264rgb,crf_23,Compression,41.47758589761431,0.9816663532207409,0.027765587966617506,0.9943965021520853,94.04945610416667,0.9869791685293118,-22.30228143979824,0.8645833333333334,0.0,0.26086734731992084
+videoseal_0.0,H264rgb,crf_30,Compression,37.1260413791376,0.9574717245995998,0.0760538221608537,0.9858291608591875,92.73585030208335,0.9799479190260172,-21.489114757416896,0.6979166666666666,0.0,0.2627016330758731
+videoseal_0.0,H264rgb,crf_40,Compression,32.388330112588456,0.8992538259675106,0.19846482132561505,0.9558645101885,86.78819740624999,0.9144531264901161,-15.596305555499745,0.2604166666666667,0.0,0.267351637283961
+videoseal_0.0,H264rgb,crf_50,Compression,27.841309726436464,0.7996925072123607,0.359958834014833,0.8841126126547655,68.91671463541667,0.7197916662941376,-5.245144452455555,0.010416666666666666,0.0,0.2688034226497014
+videoseal_0.0,HorizontalFlip,default,Geometric,,,,,,0.988541675110658,-22.509592133545524,0.8958333333333334,0.0,0.04236834992965063
+videoseal_0.0,Hue,factor_0.25,Visual,22.093641063881886,0.9400382476548353,0.2163538116340836,0.9469405251244704,61.196745177083336,0.6230468852445483,-2.1118133625895967,0.0,0.0,0.04279887676239014
+videoseal_0.0,Identity,default,None,49.978977931859426,0.9937164143969616,0.006842429180930291,0.9962141122668982,94.63625695833332,0.9893229268491268,-22.592961285890492,0.9270833333333334,0.0,0.05321664363145828
+videoseal_0.0,JPEG,quality_40,Compression,37.04748280666869,0.912163307890296,0.061407871961516015,0.9650243514527878,92.12864571875001,0.9470052197575569,-18.385397764676345,0.4791666666666667,0.0,0.04320657749970754
+videoseal_0.0,MedianFilter,kernel_size_9,Visual,29.635423336436688,0.8052526020134488,0.21484369677879536,0.9034083429723978,79.76610556249999,0.9889323009798924,-22.554467241038065,0.9270833333333334,0.0,0.045565965274969734
+videoseal_0.0,Perspective,distortion_scale_0.5,Geometric,,,,,,0.9149739692608515,-15.442059052575727,0.21875,0.0,0.044607033332188926
+videoseal_0.0,Resize,size_0.55,Geometric,,,,,,0.9891927173982064,-22.584066906143118,0.9375,0.0,0.04200796037912369
+videoseal_0.0,Resize,size_0.71,Geometric,,,,,,0.9894531350582838,-22.61095164976307,0.9375,0.0,0.04194989303747813
+videoseal_0.0,Rotate,angle_10,Geometric,,,,,,0.9381510528425375,-17.10844228502611,0.23958333333333334,0.0,0.04480337103207906
+videoseal_0.0,Rotate,angle_90,Geometric,,,,,,0.5110677148525914,-0.4356897813412122,0.0,0.0,0.04462430874506632
+videoseal_0.0,Saturation,factor_0.5,Visual,31.1184732912089,0.9963171773900589,0.0435978326034577,0.9937543322642645,82.37308011458333,0.9872395930190881,-22.325443424433285,0.8020833333333334,0.0,0.04291540135939916
+videoseal_0.0,Saturation,factor_1.5,Visual,31.443817581094304,0.9884588439017534,0.037789984742024295,0.9898110615710417,87.21965684375,0.9891927186399698,-22.574382076523154,0.9375,0.0,0.04274391631285349
+videoseal_0.0,VP9,default,Compression,37.837220910611414,0.9681100845336914,0.04557402139956442,0.9873604613045851,93.01640629166666,0.965234378973643,-20.019336430542744,0.4270833333333333,0.0,0.2505236243208249

backend/data/val2014_1k_image_benchmark.csv

@@ -0,0 +1,265 @@
+model,attack_name,attack_variant,cat,psnr,ssim,lpips,bit_acc,log10_p_value,TPR,FPR,decoder_time,qual_time,det_time,attack_time
+cine_jit,brightness,brightness_factor_1.5,Visual,14.793723794937133,0.8787051364599999,0.14193622463196515,0.9981428577303887,-4.191011628767688,0.985,0.0,0.019354299999999998,0.1177627,0.0410492,0.0022933000000000003
+cine_jit,brightness,brightness_factor_2,Visual,10.968698310852051,0.73736501481,0.2617685646489263,0.9960714295208454,-4.167917673642502,0.982,0.0,0.0202609,0.1193021,0.0413342,0.0023506
+cine_jit,center_crop,scale_0.1,Geometric,,,,0.4987143074721098,-0.3352273607556046,0.0,0.0,0.015408100000000003,0.0,0.0299736,0.0023213
+cine_jit,center_crop,scale_0.2,Geometric,,,,0.49592859322577715,-0.33112507926489454,0.0,0.0,0.0147408,0.0,0.030030800000000007,0.0022835
+cine_jit,center_crop,scale_0.5,Geometric,,,,0.5040714505165815,-0.3373113577495052,0.0,0.0,0.017443300000000002,0.0,0.03601299999999999,0.0023024000000000005
+cine_jit,collage,scale_0.1,Inpainting,-2.264131217956543,0.15222266984399999,0.7499057468175888,0.6403571749031544,-0.8613041623189308,0.002,0.0,0.018156500000000002,0.1183056,0.037305000000000005,0.0033442000000000003
+cine_jit,comb,default,Mixed,,,,0.5037857362255455,-0.34787567211106724,0.0,0.0,0.017114600000000004,0.0,0.0346486,0.015026899999999998
+cine_jit,contrast,contrast_factor_1.5,Visual,21.203966033935547,0.82492182279,0.10614364800043403,0.9979285719394684,-4.189271843250608,0.984,0.0,0.019087000000000003,0.11721569999999999,0.0398754,0.0024752999999999997
+cine_jit,contrast,contrast_factor_2,Visual,17.20670739173889,0.70639500298,0.1901860120818019,0.9970714294910431,-4.175135788491075,0.975,0.001,0.020367200000000002,0.1193308,0.0412925,0.0024322999999999997
+cine_jit,gaussian_blur,kernel_size_17,Visual,22.98837381362915,0.7345776365200001,0.46964600117504596,0.9962857156395912,-4.161671516710178,0.964,0.0,0.019586000000000003,0.1177006,0.0402553,0.0027628
+cine_jit,gaussian_blur,kernel_size_3,Visual,29.48486163520813,0.92603678758,0.1511742896437645,0.9997857143878937,-4.210891665518569,0.994,0.0,0.0193962,0.11915580000000002,0.040181600000000005,0.0028139999999999997
+cine_jit,hflip,default,Geometric,,,,0.5152857366800309,-0.3860999028091876,0.0,0.0,0.019453899999999996,0.0,0.038473799999999996,0.0021997
+cine_jit,hue,hue_factor_-0.1,Visual,26.82082160949707,0.95958051585,0.13717786891572178,0.976928575694561,-3.953951484600151,0.893,0.0,0.014568700000000002,0.11870789999999999,0.0315958,0.0038947999999999995
+cine_jit,hue,hue_factor_0.1,Visual,26.859237878799437,0.9648306783,0.1396170155685395,0.9948571441471576,-4.156042565949502,0.975,0.0,0.0165752,0.11801149999999999,0.0341875,0.003989599999999999
+cine_jit,jpeg,quality_factor_40,Compression,29.44433637237549,0.91525193599,0.19659307485073804,0.9393571620583534,-3.409343335671343,0.497,0.0,0.0185747,0.11844269999999998,0.0379668,0.0219767
+cine_jit,jpeg,quality_factor_80,Compression,32.83987800598145,0.95561534721,0.10288211115077138,0.9963571443557739,-4.158669019244395,0.93,0.0,0.0198866,0.11633569999999999,0.04084700000000001,0.0232457
+cine_jit,median_filter,kernel_size_3,Visual,,,,0.9995000002384186,-4.2061873004823465,0.992,0.0,0.0194793,0.0,0.0406742,0.0031006000000000002
+cine_jit,median_filter,kernel_size_7,Visual,,,,0.999071429014206,-4.19945762958086,0.984,0.0,0.0195634,0.0,0.0416309,0.0049805
+cine_jit,none,default,None,44.57761585617065,0.99689209203,0.022877193189691752,0.9997142858505249,-4.209715574259513,0.994,0.0,0.0380514,0.11829179999999997,0.05887730000000001,0.0021683
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+cine_jit,perspective,distortion_scale_0.1,Geometric,,,,0.5249285944625736,-0.47179957110970555,0.0,0.0,0.0186661,0.0,0.03826859999999999,0.0032442
+cine_jit,perspective,distortion_scale_0.5,Geometric,,,,0.5015000222399831,-0.349701211221236,0.0,0.0,0.0210106,0.0,0.042139899999999994,0.0032573
+cine_jit,proportion,prop_0.1,Geometric,,,,0.8256428890526295,-2.270391522598131,0.168,0.0,0.0189112,0.0,0.0397027,0.0023819999999999996
+cine_jit,resize,scale_0.1,Geometric,,,,0.9991428574919701,-4.201489896759147,0.99,0.0,0.013764600000000002,0.0,0.029183,0.0023250000000000002
+cine_jit,resize,scale_0.5,Geometric,,,,0.9996428573131562,-4.208539483000458,0.994,0.0,0.0159048,0.0,0.033760099999999994,0.0023076000000000004
+cine_jit,rotate,angle_10,Geometric,,,,0.5035714503005146,-0.3465748494178488,0.0,0.0,0.019503999999999997,0.0,0.03929080000000001,0.0026563999999999997
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+cine_jit,sharpness,sharpness_factor_1.5,Visual,36.179243997573856,0.9877379007,0.03578403472341597,0.9997142858505249,-4.209715574259513,0.993,0.0,0.018844700000000002,0.1178692,0.0396441,0.0025960000000000002
+cine_jit,sharpness,sharpness_factor_2,Visual,31.19517758369446,0.96670936118,0.06171484182775021,0.9995714287757873,-4.2076902683942485,0.991,0.0,0.019257800000000002,0.11999629999999999,0.0422377,0.0025938999999999997
+dctdwt,brightness,brightness_factor_1.5,Visual,14.646175559997559,0.85921773233,0.1388432988859713,0.5978125,-1.9191306399385262,0.099,0.0,0.0235184,0.1246352,0.047183499999999996,0.0022683
+dctdwt,brightness,brightness_factor_2,Visual,10.881604486465454,0.71897309263,0.26098551776260137,0.4898125,-0.34018891538781537,0.0,0.0,0.024112099999999997,0.12904450000000003,0.0475997,0.0023271
+dctdwt,collage,scale_0.1,Inpainting,-2.2728081817626955,0.150185196504,0.7494750100374222,0.68025,-1.8593439550505408,0.005,0.0,0.0238288,0.1306898,0.04771430000000001,0.0033881
+dctdwt,contrast,contrast_factor_1.5,Visual,21.041186895370483,0.81546665239,0.10033562627620995,0.62546875,-2.447305479356551,0.147,0.0,0.02357,0.12252060000000001,0.0470408,0.0024881
+dctdwt,contrast,contrast_factor_2,Visual,17.130134965896605,0.69788034973,0.18464771055430174,0.493,-0.3944471596834818,0.003,0.0,0.023721700000000002,0.1282683,0.0471453,0.0025323000000000003
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backend/data/voxpopuli_1k_audio_benchmark.csv

@@ -0,0 +1,317 @@
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+wavmark_fast,speed,speed_factor_1.4,TimeDomain,37.57822899800001,37.57928874778747,0.996943494842777,4.3297111325263975,0.5006875,-0.3540424867957197,0.0,0.0,0.8921921763420105
+wavmark_fast,speed,speed_factor_1.5,TimeDomain,37.695795997,37.69692880821228,0.9964801105125043,4.330922291994095,0.4806875,-0.29122948449250985,0.901,0.0,0.05374671244621277
+wavmark_fast,speed_julius,speed_1.25,TimeDomain,37.373984062999995,37.37487630081177,0.9975079716904256,4.300583213806152,0.4875,-0.32713269335590156,0.662,0.0,0.0917020308971405
+wavmark_fast,updownresample,default,TimeDomain,37.138962861,37.139748765945434,0.9989369572997093,4.287393998861313,1.0,-4.816479930623699,1.0,0.0,0.08851717376708984

backend/environment.yml

@@ -4,8 +4,11 @@ channels:
   - conda-forge
 dependencies:
   - python=3.11
+  - gunicorn
   - flask=3.0.3
   - werkzeug=3.0.3
+  - pandas
   - pip
   - pip:
       - watchdog
+  - flask-cors

backend/tools.py

@@ -0,0 +1,118 @@
+import pandas as pd
+
+
+def add_avg_as_columns(
+    benchmark_df: pd.DataFrame, attack_scores: list[str]
+) -> pd.DataFrame:
+    # average over the attack variants (inequal number of attack variants)
+    attack_avg_df = (
+        benchmark_df[["model", "attack_name"] + attack_scores]
+        .groupby(["model", "attack_name"])
+        .agg(lambda x: x.mean(skipna=False))
+        .reset_index(drop=False)
+    )
+
+    avg_df = (
+        attack_avg_df[["model"] + attack_scores]
+        .groupby(["model"])
+        .agg(lambda x: x.mean(skipna=False))
+        .reset_index(drop=False)
+    )
+    return avg_df.rename(columns={s: f"avg_{s}" for s in attack_scores})
+
+
+def add_attack_variants_as_columns(
+    df: pd.DataFrame, first_cols: list[str], attack_scores: list[str]
+) -> pd.DataFrame:
+    model_dfs = []
+    for model in df.model.unique():
+        model_view = df[df.model == model]
+        attack_dfs = []
+
+        for i, row in model_view.iterrows():
+            attack_name = row["attack_name"]
+            attack_variant = row["attack_variant"]
+
+            g_df = model_view[
+                (model_view.attack_name == attack_name)
+                & (model_view.attack_variant == attack_variant)
+            ]
+            if (
+                attack_name == "none"
+                or attack_name == "identity"
+                or attack_name == "Identity"
+            ):
+                g_df = g_df[["model"] + first_cols + attack_scores]
+            else:
+                g_df = g_df[attack_scores]
+
+            if attack_variant == "default":
+                prefix = attack_name
+            else:
+                prefix = f"{attack_name}_{attack_variant}"
+
+            g_df = g_df.rename(columns={s: f"{prefix}_{s}" for s in attack_scores})
+            attack_dfs.append(g_df.reset_index(drop=True))
+
+        model_df = pd.concat(attack_dfs, axis=1)
+        model_dfs.append(model_df)
+
+    final_df = pd.concat(model_dfs, axis=0, ignore_index=True)
+    first_cols_ = ["model"] + first_cols
+    reordered_cols = first_cols_ + list(
+        set(final_df.columns.tolist()) - set(first_cols_)
+    )
+
+    return final_df[reordered_cols]
+
+
+def add_attack_categories_as_columns(
+    benchmark_df: pd.DataFrame, attack_scores: list[str]
+) -> pd.DataFrame:
+    # average over the attack variants (inequal number of attack variants)
+    attack_avg_df = (
+        benchmark_df[["model", "attack_name", "cat"] + attack_scores]
+        .groupby(["model", "attack_name", "cat"])
+        .agg(lambda x: x.mean(skipna=False))
+        .reset_index(drop=False)
+    )
+    df = (
+        attack_avg_df.groupby(["model", "cat"])[attack_scores]
+        .agg(lambda x: x.mean(skipna=False))
+        .reset_index()
+    )
+
+    model_dfs = []
+
+    for model in df.model.unique():
+        cat_dfs = []
+
+        for cat in df.cat.unique():
+            if cat == "None":
+                continue
+
+            cat_df = df[(df.model == model) & (df.cat == cat)]
+            cat_df = cat_df[attack_scores]
+            cat_df = cat_df.rename(columns={s: f"{cat}_{s}" for s in attack_scores})
+
+            cat_dfs.append(cat_df.reset_index(drop=True))
+
+        model_dfs.append(pd.concat(cat_dfs, axis=1))
+
+    return pd.concat(model_dfs, axis=0, ignore_index=True)
+
+
+def get_old_format_dataframe(
+    benchmark_df: pd.DataFrame, first_cols: list[str], attack_scores: list[str]
+) -> pd.DataFrame:
+    benchmark_df = benchmark_df.fillna("None")
+
+    avg_df = add_avg_as_columns(benchmark_df, attack_scores)
+    attack_variants_df = add_attack_variants_as_columns(
+        benchmark_df, first_cols, attack_scores
+    )
+    categories_df = add_attack_categories_as_columns(benchmark_df, attack_scores)
+
+    final_df = pd.concat([attack_variants_df, categories_df, avg_df], axis=1)
+    final_df = final_df.loc[:, ~final_df.columns.duplicated()].copy()
+    return final_df

frontend/.env

@@ -0,0 +1 @@
+VITE_API_SERVER=""

frontend/.prettierignore

@@ -0,0 +1,8 @@
+# Prettier ignores
+node_modules
+build
+dist
+.env
+coverage
+*.pyc
+__pycache__/

frontend/.prettierrc

@@ -0,0 +1,8 @@
+{
+  "semi": false,
+  "singleQuote": true,
+  "trailingComma": "es5",
+  "printWidth": 100,
+  "tabWidth": 2,
+  "arrowParens": "always"
+}

frontend/index.html

@@ -4,7 +4,7 @@
     <meta charset="UTF-8" />
     <link rel="icon" type="image/svg+xml" href="/vite.svg" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>Flask + React App</title>
+    <title>🥇 Omni Seal Bench Watermarking Leaderboard</title>
   </head>
   <body>
     <div id="root"></div>

frontend/package.json

@@ -6,23 +6,28 @@
   "scripts": {
     "dev": "vite",
     "build": "tsc && vite build",
-    "preview": "vite preview"
+    "preview": "vite preview",
+    "format": "prettier --write \"src/**/*.{ts,tsx,js,jsx,json,css,md,html}\""
   },
   "dependencies": {
-    "react": "^18.2.0",
-    "react-dom": "^18.2.0"
+    "@tailwindcss/vite": "^4.0.0",
+    "react": "^18.3.1",
+    "react-dom": "^18.3.1",
+    "recharts": "^2.9.0"
   },
   "devDependencies": {
-    "@types/react": "^18.2.15",
-    "@types/react-dom": "^18.2.7",
-    "@typescript-eslint/eslint-plugin": "^6.0.0",
-    "@typescript-eslint/parser": "^6.0.0",
-    "@vitejs/plugin-react": "^4.0.3",
-    "autoprefixer": "^10.4.14",
-    "daisyui": "^3.5.0",
-    "postcss": "^8.4.27",
-    "tailwindcss": "^3.3.3",
-    "typescript": "^5.0.2",
-    "vite": "^4.4.5"
+    "@tailwindcss/postcss": "^4.1.8",
+    "@types/react": "^18.3.1",
+    "@types/react-dom": "^18.3.1",
+    "@typescript-eslint/eslint-plugin": "^7.7.0",
+    "@typescript-eslint/parser": "^7.7.0",
+    "@vitejs/plugin-react": "^4.2.0",
+    "autoprefixer": "^10.4.19",
+    "daisyui": "^4.10.2",
+    "postcss": "^8.4.38",
+    "prettier": "^3.2.5",
+    "tailwindcss": "^4.0.0",
+    "typescript": "^5.4.5",
+    "vite": "^5.2.10"
   }
 }

frontend/src/API.ts

@@ -1,15 +1,19 @@
+const VITE_API_SERVER_URL = import.meta.env.VITE_API_SERVER_URL || ''
+
 class API {
   static async fetchIndex(): Promise<string> {
-    const response = await fetch("/");
-    if (!response.ok) throw new Error("Failed to fetch index.html");
-    return response.text();
+    const response = await fetch(VITE_API_SERVER_URL + '/')
+    if (!response.ok) throw new Error('Failed to fetch index.html')
+    return response.text()
   }
 
   static async fetchStaticFile(path: string): Promise<string> {
-    const response = await fetch(`/${path}`);
-    if (!response.ok) throw new Error(`Failed to fetch ${path}`);
-    return response.text();
+    console.log(`Fetching static file: ${path}`)
+    console.log(`API Server URL: ${VITE_API_SERVER_URL}`)
+    const response = await fetch(`${VITE_API_SERVER_URL}/${path}`)
+    if (!response.ok) throw new Error(`Failed to fetch ${path}`)
+    return response.text()
   }
 }
 
-export default API;
+export default API

frontend/src/App.tsx

@@ -1,23 +1,148 @@
 import { useState, useEffect } from 'react'
 import API from './API'
+import DataChart from './components/DataChart'
+
+// Define types for groups and metrics
+interface Groups {
+  [group: string]: { [subgroup: string]: string[] }
+}
+
+interface Row {
+  metric: string
+  [key: string]: string | number
+}
+
+// New Filter Component
+function Filter({
+  groups,
+  selectedMetrics,
+  setSelectedMetrics,
+}: {
+  groups: Groups
+  selectedMetrics: Set<string>
+  setSelectedMetrics: (metrics: Set<string>) => void
+}) {
+  const [openGroups, setOpenGroups] = useState<{ [key: string]: boolean }>({})
+  const [openSubGroups, setOpenSubGroups] = useState<{ [key: string]: { [key: string]: boolean } }>(
+    {}
+  )
+
+  const toggleGroup = (group: string) => {
+    setOpenGroups((prev) => ({ ...prev, [group]: !prev[group] }))
+  }
+
+  const toggleSubGroup = (group: string, subGroup: string) => {
+    setOpenSubGroups((prev) => ({
+      ...prev,
+      [group]: {
+        ...prev[group],
+        [subGroup]: !prev[group]?.[subGroup],
+      },
+    }))
+  }
+
+  return (
+    <div className="w-11/12 flex flex-wrap gap-4 p-4 bg-gray-50 rounded shadow">
+      {Object.entries(groups).map(([group, subGroups]) => (
+        <div key={group} className="filter-group w-1/3 border p-2 rounded overflow-hidden">
+          <h4
+            onClick={() => toggleGroup(group)}
+            className="cursor-pointer text-lg font-semibold text-blue-600 hover:underline truncate"
+            title={group}
+          >
+            {group} {openGroups[group] ? '▼' : '▶'}
+          </h4>
+          {openGroups[group] && (
+            <div className="filter-subgroups">
+              {Object.entries(subGroups).map(([subGroup, metrics]) => (
+                <div key={subGroup} className="filter-subgroup border-t pt-2 mt-2">
+                  <h5
+                    onClick={() => toggleSubGroup(group, subGroup)}
+                    className="cursor-pointer text-md font-medium text-gray-700 hover:underline truncate"
+                    title={subGroup}
+                  >
+                    {subGroup} {openSubGroups[group]?.[subGroup] ? '▼' : '▶'}
+                  </h5>
+                  {openSubGroups[group]?.[subGroup] && (
+                    <div className="filter-metrics grid grid-cols-2 gap-2 mt-2">
+                      {metrics.map((metric) => (
+                        <div key={metric} className="flex items-center space-x-2 truncate">
+                          <input
+                            type="checkbox"
+                            checked={selectedMetrics.has(metric)}
+                            onChange={(event) => {
+                              const newSet = new Set(selectedMetrics)
+                              if (event.target.checked) {
+                                newSet.add(metric)
+                              } else {
+                                newSet.delete(metric)
+                              }
+                              setSelectedMetrics(newSet)
+                            }}
+                            className="form-checkbox h-4 w-4 text-blue-600"
+                          />
+                          <label className="text-sm text-gray-600 truncate" title={metric}>
+                            {metric.includes('_') ? metric.split('_').slice(1).join('_') : metric}
+                          </label>
+                        </div>
+                      ))}
+                    </div>
+                  )}
+                </div>
+              ))}
+            </div>
+          )}
+        </div>
+      ))}
+    </div>
+  )
+}
 
 function App() {
-  const [count, setCount] = useState<number>(0)
-  const [csvRows, setCsvRows] = useState<string[][]>([])
-  const [csvHeader, setCsvHeader] = useState<string[]>([])
-  const [loading, setLoading] = useState<boolean>(true)
+  const [count, setCount] = useState(0)
+  const [tableRows, setTableRows] = useState<Row[]>([])
+  const [tableHeader, setTableHeader] = useState<string[]>([])
+  const [chartData, setChartData] = useState<Row[]>([])
+  const [loading, setLoading] = useState(true)
   const [error, setError] = useState<string | null>(null)
+  const [groups, setGroups] = useState<Groups>({})
+  const [selectedMetrics, setSelectedMetrics] = useState<Set<string>>(new Set())
+
+  const file = 'voxpopuli_1k_audio_benchmark'
 
   useEffect(() => {
-    API.fetchStaticFile('data/ravdess_1k_audio_benchmark.csv')
-      .then((csvText) => {
-        const rows = csvText.trim().split('\n').map(row => row.split(','))
-        setCsvHeader(rows[0])
-        setCsvRows(rows.slice(1))
+    API.fetchStaticFile(`data/${file}.csv`)
+      .then((response) => {
+        const data = JSON.parse(response)
+        const rows: Row[] = data['rows']
+        const groups = data['groups'] as { [key: string]: string[] }
+
+        // Each value of groups is a list of metrics, group them by the first part of the metric before the first _
+        const groupsData = Object.entries(groups).reduce(
+          (acc, [group, metrics]) => {
+            acc[group] = metrics.reduce<{ [key: string]: string[] }>((subAcc, metric) => {
+              const [mainGroup, subGroup] = metric.split('_')
+              if (!subAcc[mainGroup]) {
+                subAcc[mainGroup] = []
+              }
+              subAcc[mainGroup].push(metric)
+              return subAcc
+            }, {})
+            return acc
+          },
+          {} as { [key: string]: { [key: string]: string[] } }
+        )
+
+        const allKeys: string[] = Array.from(new Set(rows.flatMap((row) => Object.keys(row))))
+        setSelectedMetrics(new Set(data['selected']))
+        setTableHeader(allKeys)
+        setTableRows(rows)
+        setChartData(rows)
+        setGroups(groupsData)
         setLoading(false)
       })
       .catch((err) => {
-        setError('Failed to fetch CSV: ' + err.message)
+        setError('Failed to fetch JSON: ' + err.message)
         setLoading(false)
       })
   }, [])
@@ -26,40 +151,57 @@ function App() {
     <div className="flex flex-col items-center justify-center min-h-screen bg-gray-100">
       <div className="card w-11/12 max-w-4xl bg-base-100 shadow-xl mb-8">
         <div className="card-body">
-          <h2 className="card-title">Flask + React + Docker</h2>
+          <h2 className="card-title">🥇 Omni Seal Bench Watermarking Leaderboard</h2>
           <p>Simple proof of concept with Flask backend serving a React frontend.</p>
           <div className="card-actions justify-center mt-4">
-            <button
-              className="btn btn-primary"
-              onClick={() => setCount((count: number) => count + 1)}
-            >
+            <button className="btn btn-primary" onClick={() => setCount((count) => count + 1)}>
               Count is {count}
             </button>
           </div>
         </div>
       </div>
+      <DataChart data={chartData} loading={loading} error={error} headers={tableHeader} />
+
+      <Filter
+        groups={groups}
+        selectedMetrics={selectedMetrics}
+        setSelectedMetrics={(metrics) => {
+          setSelectedMetrics(metrics)
+        }}
+      />
+
       <div className="w-11/12 max-w-4xl bg-white rounded shadow p-4 overflow-auto">
-        <h3 className="font-bold mb-2">ravdess_1k_audio_benchmark.csv</h3>
+        <h3 className="font-bold mb-2">{file}</h3>
         {loading && <div>Loading...</div>}
         {error && <div className="text-red-500">{error}</div>}
         {!loading && !error && (
           <div className="overflow-x-auto">
-            <table className="table table-zebra w-full text-xs">
+            <table>
               <thead>
                 <tr>
-                  {csvHeader.map((col, idx) => (
+                  {tableHeader.map((col, idx) => (
                     <th key={idx}>{col}</th>
                   ))}
                 </tr>
               </thead>
               <tbody>
-                {csvRows.map((row, i) => (
-                  <tr key={i}>
-                    {row.map((cell, j) => (
-                      <td key={j}>{cell}</td>
-                    ))}
-                  </tr>
-                ))}
+                {tableRows
+                  .filter((row) => selectedMetrics.has(row['metric']))
+                  .map((row, i) => (
+                    <tr key={i}>
+                      {Object.keys(row).map((column, j) => {
+                        const cell = row[column]
+
+                        return (
+                          <td key={j}>
+                            <div className="p-4">
+                              {isNaN(Number(cell)) ? cell : Number(Number(cell).toFixed(3))}
+                            </div>
+                          </td>
+                        )
+                      })}
+                    </tr>
+                  ))}
               </tbody>
             </table>
           </div>

frontend/src/components/DataChart.tsx

@@ -0,0 +1,47 @@
+import { LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, Legend, ResponsiveContainer } from 'recharts';
+
+interface DataChartProps {
+  data: any[];
+  loading: boolean;
+  error: string | null;
+  headers: string[];
+}
+
+const DataChart = ({ data, loading, error, headers }: DataChartProps) => {
+  return (
+    <div className="w-11/12 max-w-4xl bg-white rounded shadow p-4 overflow-auto mb-8">
+      <h3 className="font-bold mb-2">Data Visualization</h3>
+      {loading && <div>Loading...</div>}
+      {error && <div className="text-red-500">{error}</div>}
+      {!loading && !error && data.length > 0 && (
+        <div className="h-64 mb-4">
+          <ResponsiveContainer width="100%" height="100%">
+            <LineChart
+              data={data}
+              margin={{
+                top: 5,
+                right: 30,
+                left: 20,
+                bottom: 5,
+              }}
+            >
+              <CartesianGrid strokeDasharray="3 3" />
+              <XAxis dataKey={headers[0]} />
+              <YAxis />
+              <Tooltip />
+              <Legend />
+              {headers[1] && (
+                <Line type="monotone" dataKey={headers[1]} stroke="#8884d8" dot={false} />
+              )}
+              {headers[2] && (
+                <Line type="monotone" dataKey={headers[2]} stroke="#82ca9d" dot={false} />
+              )}
+            </LineChart>
+          </ResponsiveContainer>
+        </div>
+      )}
+    </div>
+  );
+};
+
+export default DataChart;

frontend/src/index.css

@@ -1,3 +1,2 @@
-@tailwind base;
-@tailwind components;
-@tailwind utilities;
+@import "tailwindcss";
+

frontend/src/main.tsx

@@ -6,5 +6,5 @@ import './index.css'
 ReactDOM.createRoot(document.getElementById('root')!).render(
   <React.StrictMode>
     <App />
-  </React.StrictMode>,
+  </React.StrictMode>
 )

frontend/src/vite-env.d.ts

@@ -1 +1,9 @@
 /// <reference types="vite/client" />
+
+interface ImportMetaEnv {
+  readonly VITE_API_SERVER: string;
+}
+
+interface ImportMeta {
+  readonly env: ImportMetaEnv;
+}

frontend/tailwind.config.js

@@ -1,14 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-export default {
-  content: [
-    "./index.html",
-    "./src/**/*.{js,ts,jsx,tsx}",
-  ],
-  theme: {
-    extend: {},
-  },
-  plugins: [require("daisyui")],
-  daisyui: {
-    themes: ["light"],
-  },
-}

frontend/vite.config.ts

@@ -1,11 +1,12 @@
 import { defineConfig } from 'vite'
+import tailwindcss from '@tailwindcss/vite'
 import react from '@vitejs/plugin-react'
 
 // https://vitejs.dev/config/
 export default defineConfig({
-  plugins: [react()],
+  plugins: [react(), tailwindcss()],
   build: {
     outDir: 'dist',
-    emptyOutDir: true
-  }
+    emptyOutDir: true,
+  },
 })

package-lock.json

@@ -1,40 +0,0 @@
-{
-  "name": "omniseal_dev",
-  "lockfileVersion": 3,
-  "requires": true,
-  "packages": {
-    "": {
-      "devDependencies": {
-        "@types/react": "^19.1.6",
-        "@types/react-dom": "^19.1.6"
-      }
-    },
-    "node_modules/@types/react": {
-      "version": "19.1.6",
-      "resolved": "https://registry.npmjs.org/@types/react/-/react-19.1.6.tgz",
-      "integrity": "sha512-JeG0rEWak0N6Itr6QUx+X60uQmN+5t3j9r/OVDtWzFXKaj6kD1BwJzOksD0FF6iWxZlbE1kB0q9vtnU2ekqa1Q==",
-      "dev": true,
-      "license": "MIT",
-      "dependencies": {
-        "csstype": "^3.0.2"
-      }
-    },
-    "node_modules/@types/react-dom": {
-      "version": "19.1.6",
-      "resolved": "https://registry.npmjs.org/@types/react-dom/-/react-dom-19.1.6.tgz",
-      "integrity": "sha512-4hOiT/dwO8Ko0gV1m/TJZYk3y0KBnY9vzDh7W+DH17b2HFSOGgdj33dhihPeuy3l0q23+4e+hoXHV6hCC4dCXw==",
-      "dev": true,
-      "license": "MIT",
-      "peerDependencies": {
-        "@types/react": "^19.0.0"
-      }
-    },
-    "node_modules/csstype": {
-      "version": "3.1.3",
-      "resolved": "https://registry.npmjs.org/csstype/-/csstype-3.1.3.tgz",
-      "integrity": "sha512-M1uQkMl8rQK/szD0LNhtqxIPLpimGm8sOBwU7lLnCpSbTyY3yeU1Vc7l4KT5zT4s/yOxHH5O7tIuuLOCnLADRw==",
-      "dev": true,
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