diff --git a/requirements.txt b/requirements.txt
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--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,41 @@
-altair
-pandas
-streamlit
\ No newline at end of file
+altair==5.5.0
+attrs==25.4.0
+blinker==1.9.0
+cachetools==6.2.0
+certifi==2025.10.5
+charset-normalizer==3.4.3
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+jsonschema==4.25.1
+jsonschema-specifications==2025.9.1
+MarkupSafe==3.0.3
+narwhals==2.7.0
+numpy==2.2.6
+packaging==25.0
+pandas==2.3.3
+pillow==11.3.0
+plotly==6.3.1
+protobuf==6.32.1
+pyarrow==21.0.0
+pydeck==0.9.1
+python-dateutil==2.9.0.post0
+python-decouple==3.8
+pytz==2025.2
+referencing==0.36.2
+requests==2.32.5
+rpds-py==0.27.1
+six==1.17.0
+smmap==5.0.2
+streamlit==1.50.0
+streamlit-aggrid==1.1.9
+streamlit-plotly-events==0.0.6
+tenacity==9.1.2
+toml==0.10.2
+tornado==6.5.2
+typing_extensions==4.15.0
+tzdata==2025.2
+urllib3==2.5.0
+watchdog==6.0.0
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+      "original_name": "v_BodyWeightSquats_g17_c01",
+      "output_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/BodyWeightSquats__96921FA692.mp4"
+    },
+    {
+      "id": "BodyWeightSquats__CC97AE24F7",
+      "model": "runway_gen_4",
+      "class": "BodyWeightSquats",
+      "original_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/runway_gen4_videos_5/BodyWeightSquats/v_BodyWeightSquats_g09_c02/video.mp4",
+      "original_name": "v_BodyWeightSquats_g09_c02",
+      "output_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/BodyWeightSquats__CC97AE24F7.mp4"
+    },
+    {
+      "id": "BodyWeightSquats__BA7B377EFE",
+      "model": "runway_gen_4",
+      "class": "BodyWeightSquats",
+      "original_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/runway_gen4_videos_5/BodyWeightSquats/v_BodyWeightSquats_g19_c03/video.mp4",
+      "original_name": "v_BodyWeightSquats_g19_c03",
+      "output_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/BodyWeightSquats__BA7B377EFE.mp4"
+    },
+    {
+      "id": "BodyWeightSquats__DB228B2AC8",
+      "model": "runway_gen_4",
+      "class": "BodyWeightSquats",
+      "original_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/runway_gen4_videos_5/BodyWeightSquats/v_BodyWeightSquats_g11_c01/video.mp4",
+      "original_name": "v_BodyWeightSquats_g11_c01",
+      "output_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/BodyWeightSquats__DB228B2AC8.mp4"
+    },
+    {
+      "id": "BodyWeightSquats__2EFE33F25E",
+      "model": "runway_gen_4",
+      "class": "BodyWeightSquats",
+      "original_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/runway_gen4_videos_5/BodyWeightSquats/v_BodyWeightSquats_g15_c03/video.mp4",
+      "original_name": "v_BodyWeightSquats_g15_c03",
+      "output_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/BodyWeightSquats__2EFE33F25E.mp4"
+    },
+    {
+      "id": "BodyWeightSquats__46F7E2CECC",
+      "model": "runway_gen_4",
+      "class": "BodyWeightSquats",
+      "original_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/runway_gen4_videos_5/BodyWeightSquats/v_BodyWeightSquats_g05_c01/video.mp4",
+      "original_name": "v_BodyWeightSquats_g05_c01",
+      "output_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/BodyWeightSquats__46F7E2CECC.mp4"
+    },
+    {
+      "id": "BodyWeightSquats__7BA453EC47",
+      "model": "runway_gen_4",
+      "class": "BodyWeightSquats",
+      "original_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/runway_gen4_videos_5/BodyWeightSquats/v_BodyWeightSquats_g17_c01/video.mp4",
+      "original_name": "v_BodyWeightSquats_g17_c01",
+      "output_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/BodyWeightSquats__7BA453EC47.mp4"
+    },
+    {
+      "id": "BodyWeightSquats__4E63C96142",
+      "model": "Opensora_256p",
+      "class": "BodyWeightSquats",
+      "original_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/opensora_videos_256p_formatted/BodyWeightSquats/v_BodyWeightSquats_g09_c02/video.mp4",
+      "original_name": "v_BodyWeightSquats_g09_c02",
+      "output_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/BodyWeightSquats__4E63C96142.mp4"
+    },
+    {
+      "id": "BodyWeightSquats__07050FC646",
+      "model": "Opensora_256p",
+      "class": "BodyWeightSquats",
+      "original_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/opensora_videos_256p_formatted/BodyWeightSquats/v_BodyWeightSquats_g19_c03/video.mp4",
+      "original_name": "v_BodyWeightSquats_g19_c03",
+      "output_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/BodyWeightSquats__07050FC646.mp4"
+    },
+    {
+      "id": "BodyWeightSquats__95848F0A36",
+      "model": "Opensora_256p",
+      "class": "BodyWeightSquats",
+      "original_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/opensora_videos_256p_formatted/BodyWeightSquats/v_BodyWeightSquats_g11_c01/video.mp4",
+      "original_name": "v_BodyWeightSquats_g11_c01",
+      "output_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/BodyWeightSquats__95848F0A36.mp4"
+    },
+    {
+      "id": "BodyWeightSquats__EF01742FC4",
+      "model": "Opensora_256p",
+      "class": "BodyWeightSquats",
+      "original_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/opensora_videos_256p_formatted/BodyWeightSquats/v_BodyWeightSquats_g15_c03/video.mp4",
+      "original_name": "v_BodyWeightSquats_g15_c03",
+      "output_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/BodyWeightSquats__EF01742FC4.mp4"
+    },
+    {
+      "id": "BodyWeightSquats__EC084551C5",
+      "model": "Opensora_256p",
+      "class": "BodyWeightSquats",
+      "original_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/opensora_videos_256p_formatted/BodyWeightSquats/v_BodyWeightSquats_g05_c01/video.mp4",
+      "original_name": "v_BodyWeightSquats_g05_c01",
+      "output_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/BodyWeightSquats__EC084551C5.mp4"
+    },
+    {
+      "id": "BodyWeightSquats__DA460A8AB0",
+      "model": "Opensora_256p",
+      "class": "BodyWeightSquats",
+      "original_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/opensora_videos_256p_formatted/BodyWeightSquats/v_BodyWeightSquats_g17_c01/video.mp4",
+      "original_name": "v_BodyWeightSquats_g17_c01",
+      "output_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/BodyWeightSquats__DA460A8AB0.mp4"
+    },
+    {
+      "id": "BodyWeightSquats__950D98CBB2",
+      "model": "Hunyuan_360p",
+      "class": "BodyWeightSquats",
+      "original_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/hunyuan_videos_360p_formatted/BodyWeightSquats/v_BodyWeightSquats_g09_c02/video.mp4",
+      "original_name": "v_BodyWeightSquats_g09_c02",
+      "output_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/BodyWeightSquats__950D98CBB2.mp4"
+    },
+    {
+      "id": "BodyWeightSquats__F5C8FFE984",
+      "model": "Hunyuan_360p",
+      "class": "BodyWeightSquats",
+      "original_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/hunyuan_videos_360p_formatted/BodyWeightSquats/v_BodyWeightSquats_g19_c03/video.mp4",
+      "original_name": "v_BodyWeightSquats_g19_c03",
+      "output_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/BodyWeightSquats__F5C8FFE984.mp4"
+    },
+    {
+      "id": "BodyWeightSquats__28A4946E59",
+      "model": "Hunyuan_360p",
+      "class": "BodyWeightSquats",
+      "original_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/hunyuan_videos_360p_formatted/BodyWeightSquats/v_BodyWeightSquats_g11_c01/video.mp4",
+      "original_name": "v_BodyWeightSquats_g11_c01",
+      "output_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/BodyWeightSquats__28A4946E59.mp4"
+    },
+    {
+      "id": "BodyWeightSquats__29465E587A",
+      "model": "Hunyuan_360p",
+      "class": "BodyWeightSquats",
+      "original_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/hunyuan_videos_360p_formatted/BodyWeightSquats/v_BodyWeightSquats_g15_c03/video.mp4",
+      "original_name": "v_BodyWeightSquats_g15_c03",
+      "output_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/BodyWeightSquats__29465E587A.mp4"
+    },
+    {
+      "id": "BodyWeightSquats__C47BBC50FF",
+      "model": "Hunyuan_360p",
+      "class": "BodyWeightSquats",
+      "original_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/hunyuan_videos_360p_formatted/BodyWeightSquats/v_BodyWeightSquats_g05_c01/video.mp4",
+      "original_name": "v_BodyWeightSquats_g05_c01",
+      "output_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/BodyWeightSquats__C47BBC50FF.mp4"
+    },
+    {
+      "id": "BodyWeightSquats__3E134F11A3",
+      "model": "Hunyuan_360p",
+      "class": "BodyWeightSquats",
+      "original_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/hunyuan_videos_360p_formatted/BodyWeightSquats/v_BodyWeightSquats_g17_c01/video.mp4",
+      "original_name": "v_BodyWeightSquats_g17_c01",
+      "output_path": "/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/BodyWeightSquats__3E134F11A3.mp4"
+    }
+  ]
+}
\ No newline at end of file
diff --git a/src/YOUTUBE_DATA/samples.csv b/src/YOUTUBE_DATA/samples.csv
new file mode 100644
index 0000000000000000000000000000000000000000..6e1dd5811688c700feb9faeeeffc0c14c1493af7
--- /dev/null
+++ b/src/YOUTUBE_DATA/samples.csv
@@ -0,0 +1,241 @@
+id,model,class,original_path,original_name,output_path
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+JumpingJack__A555392E40,wan21,JumpingJack,/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/wan21_videos_5/JumpingJack/v_JumpingJack_g07_c01/video.mp4,v_JumpingJack_g07_c01,/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/JumpingJack__A555392E40.mp4
+JumpingJack__45AAA1F4D2,wan21,JumpingJack,/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/wan21_videos_5/JumpingJack/v_JumpingJack_g21_c01/video.mp4,v_JumpingJack_g21_c01,/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/JumpingJack__45AAA1F4D2.mp4
+JumpingJack__1B76F9CB06,wan21,JumpingJack,/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/wan21_videos_5/JumpingJack/v_JumpingJack_g16_c01/video.mp4,v_JumpingJack_g16_c01,/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/JumpingJack__1B76F9CB06.mp4
+JumpingJack__AA0A8F59DF,wan21,JumpingJack,/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/wan21_videos_5/JumpingJack/v_JumpingJack_g24_c02/video.mp4,v_JumpingJack_g24_c02,/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/JumpingJack__AA0A8F59DF.mp4
+JumpingJack__3362F62DDA,wan21,JumpingJack,/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/wan21_videos_5/JumpingJack/v_JumpingJack_g01_c01/video.mp4,v_JumpingJack_g01_c01,/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/JumpingJack__3362F62DDA.mp4
+JumpingJack__C374FF8CB6,runway_gen_4,JumpingJack,/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/videos/runway_gen4_videos_5/JumpingJack/v_JumpingJack_g13_c02/video.mp4,v_JumpingJack_g13_c02,/projectnb/ivc-ml/xthomas/RESEARCH/video_evals/video-gen-evals/YOUTUBE_DATA/JumpingJack__C374FF8CB6.mp4
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diff --git a/src/action_mean_intra.json b/src/action_mean_intra.json
new file mode 100644
index 0000000000000000000000000000000000000000..67ac14c3cab13271d539c04dde2fb9b6a739c1a9
--- /dev/null
+++ b/src/action_mean_intra.json
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\ No newline at end of file
diff --git a/src/frame_diff_ord2.json b/src/frame_diff_ord2.json
new file mode 100644
index 0000000000000000000000000000000000000000..694cfdfb2dabd0d5c99a3ecedb615b1183b748d0
--- /dev/null
+++ b/src/frame_diff_ord2.json
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+}
\ No newline at end of file
diff --git a/src/human_scores_analysis_action_mos_centered.json b/src/human_scores_analysis_action_mos_centered.json
new file mode 100644
index 0000000000000000000000000000000000000000..34cb18fc5c7931743dac690b74df3bc541959d85
--- /dev/null
+++ b/src/human_scores_analysis_action_mos_centered.json
@@ -0,0 +1,229 @@
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+}
\ No newline at end of file
diff --git a/src/human_scores_analysis_anatomy_mos_centered.json b/src/human_scores_analysis_anatomy_mos_centered.json
new file mode 100644
index 0000000000000000000000000000000000000000..10c0817b57a6ea0e4e492ff4918394942ecefb5e
--- /dev/null
+++ b/src/human_scores_analysis_anatomy_mos_centered.json
@@ -0,0 +1,229 @@
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+}
\ No newline at end of file
diff --git a/src/human_scores_analysis_appearance_mos_centered.json b/src/human_scores_analysis_appearance_mos_centered.json
new file mode 100644
index 0000000000000000000000000000000000000000..f78143e7690122cb141c11d918f6bd38b3dc549a
--- /dev/null
+++ b/src/human_scores_analysis_appearance_mos_centered.json
@@ -0,0 +1,229 @@
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+}
\ No newline at end of file
diff --git a/src/human_scores_analysis_motion_mos_centered.json b/src/human_scores_analysis_motion_mos_centered.json
new file mode 100644
index 0000000000000000000000000000000000000000..53d7b356f38f3502b24ebb20e37fe3db0a66634b
--- /dev/null
+++ b/src/human_scores_analysis_motion_mos_centered.json
@@ -0,0 +1,229 @@
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diff --git a/src/raters/asriniv1_scores_videoevals.json b/src/raters/asriniv1_scores_videoevals.json
new file mode 100644
index 0000000000000000000000000000000000000000..3984e788994d9896675de7a2e50cd0cb798ff2c9
--- /dev/null
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diff --git a/src/raters/chvskch_scores.json b/src/raters/chvskch_scores.json
new file mode 100644
index 0000000000000000000000000000000000000000..3056fe6d54300b46149e479648c9289bae748f53
--- /dev/null
+++ b/src/raters/chvskch_scores.json
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diff --git a/src/raters/xthomasFINAL_scores.json b/src/raters/xthomasFINAL_scores.json
new file mode 100644
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diff --git a/src/raters/youngsun_scores.json b/src/raters/youngsun_scores.json
new file mode 100644
index 0000000000000000000000000000000000000000..ef3568a6a781ce3fe96fe3b422143d731d76d420
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diff --git a/src/raters/zmeurer_scores.json b/src/raters/zmeurer_scores.json
new file mode 100644
index 0000000000000000000000000000000000000000..b76395228f0bb6c4a5b5831d92690bdf3d4f9282
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+  },
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+    "action": 8.1,
+    "anatomy": 9.3
+  },
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+    "anatomy": 2.6
+  },
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+    "appearance": 1.0,
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+  },
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+  },
+  "SoccerJuggling__6AA11769E2.mp4": {
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+    "appearance": 9.5,
+    "action": 1.5,
+    "anatomy": 7.7
+  },
+  "SoccerJuggling__946C23E28D.mp4": {
+    "motion": 1.7,
+    "appearance": 2.1,
+    "action": 0.8,
+    "anatomy": 3.9
+  },
+  "SoccerJuggling__9509F7214C.mp4": {
+    "motion": 0.5,
+    "appearance": 0.1,
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+    "anatomy": 0.2
+  },
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+  },
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+    "motion": 0.2,
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+  },
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+    "appearance": 3.3,
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+    "anatomy": 5.6
+  },
+  "TennisSwing__20114190FC.mp4": {
+    "motion": 3.5,
+    "appearance": 0.6,
+    "action": 2.7,
+    "anatomy": 6.1
+  },
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+  },
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+  },
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+  },
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+  },
+  "TennisSwing__C15598BD67.mp4": {
+    "motion": 0.6,
+    "appearance": 1.8,
+    "action": 0.7,
+    "anatomy": 0.4
+  },
+  "TennisSwing__C825352683.mp4": {
+    "motion": 2.9,
+    "appearance": 2.6,
+    "action": 1.7,
+    "anatomy": 4.1
+  },
+  "ThrowDiscus__004B0CA118.mp4": {
+    "motion": 5.7,
+    "appearance": 0.4,
+    "action": 2.3,
+    "anatomy": 9.0
+  },
+  "ThrowDiscus__1710B3FDF4.mp4": {
+    "motion": 2.5,
+    "appearance": 1.0,
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+  },
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+    "appearance": 1.1,
+    "action": 0.4,
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+  },
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+    "motion": 0.2,
+    "appearance": 0.5,
+    "action": 0.2,
+    "anatomy": 0.2
+  },
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+    "appearance": 0.3,
+    "action": 0.1,
+    "anatomy": 0.4
+  },
+  "ThrowDiscus__76D1727A9D.mp4": {
+    "motion": 0.7,
+    "appearance": 9.2,
+    "action": 0.3,
+    "anatomy": 8.1
+  },
+  "ThrowDiscus__BCB8753179.mp4": {
+    "motion": 1.2,
+    "appearance": 0.4,
+    "action": 0.7,
+    "anatomy": 0.5
+  },
+  "ThrowDiscus__D4640006EB.mp4": {
+    "motion": 1.6,
+    "appearance": 0.7,
+    "action": 0.4,
+    "anatomy": 0.2
+  },
+  "ThrowDiscus__E640271639.mp4": {
+    "motion": 1.3,
+    "appearance": 0.5,
+    "action": 0.2,
+    "anatomy": 0.2
+  },
+  "WallPushups__08E61004AC.mp4": {
+    "motion": 0.3,
+    "appearance": 8.3,
+    "action": 0.7,
+    "anatomy": 8.6
+  },
+  "WallPushups__0F9CBBFC7F.mp4": {
+    "motion": 9.5,
+    "appearance": 9.3,
+    "action": 3.9,
+    "anatomy": 6.9
+  },
+  "WallPushups__173E918CDE.mp4": {
+    "motion": 1.6,
+    "appearance": 7.7,
+    "action": 2.1,
+    "anatomy": 1.5
+  },
+  "WallPushups__513D8F902E.mp4": {
+    "motion": 0.6,
+    "appearance": 9.3,
+    "action": 1.0,
+    "anatomy": 0.9
+  },
+  "WallPushups__79D75D95ED.mp4": {
+    "motion": 5.9,
+    "appearance": 9.6,
+    "action": 0.8,
+    "anatomy": 2.2
+  },
+  "WallPushups__862DDBECA0.mp4": {
+    "motion": 0.7,
+    "appearance": 9.5,
+    "action": 0.4,
+    "anatomy": 0.6
+  },
+  "WallPushups__BDFCEC93E9.mp4": {
+    "motion": 0.9,
+    "appearance": 0.3,
+    "action": 0.4,
+    "anatomy": 0.6
+  },
+  "WallPushups__C8E77804B1.mp4": {
+    "motion": 3.4,
+    "appearance": 8.4,
+    "action": 0.4,
+    "anatomy": 9.3
+  },
+  "WallPushups__CA94FE8FCE.mp4": {
+    "motion": 8.0,
+    "appearance": 9.4,
+    "action": 0.5,
+    "anatomy": 9.4
+  }
+}
\ No newline at end of file
diff --git a/src/requirements.txt b/src/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d8e3c8e596acc737b67f97411abaeb0bc5bde67d
--- /dev/null
+++ b/src/requirements.txt
@@ -0,0 +1,41 @@
+altair==5.5.0
+attrs==25.4.0
+blinker==1.9.0
+cachetools==6.2.0
+certifi==2025.10.5
+charset-normalizer==3.4.3
+click==8.3.0
+gitdb==4.0.12
+GitPython==3.1.45
+idna==3.10
+Jinja2==3.1.6
+jsonschema==4.25.1
+jsonschema-specifications==2025.9.1
+MarkupSafe==3.0.3
+narwhals==2.7.0
+numpy==2.2.6
+packaging==25.0
+pandas==2.3.3
+pillow==11.3.0
+plotly==6.3.1
+protobuf==6.32.1
+pyarrow==21.0.0
+pydeck==0.9.1
+python-dateutil==2.9.0.post0
+python-decouple==3.8
+pytz==2025.2
+referencing==0.36.2
+requests==2.32.5
+rpds-py==0.27.1
+six==1.17.0
+smmap==5.0.2
+streamlit==1.50.0
+streamlit-aggrid==1.1.9
+streamlit-plotly-events==0.0.6
+tenacity==9.1.2
+toml==0.10.2
+tornado==6.5.2
+typing_extensions==4.15.0
+tzdata==2025.2
+urllib3==2.5.0
+watchdog==6.0.0
diff --git a/src/streamlit_app.py b/src/streamlit_app.py
index 99d0b84662681e7d21a08fcce44908344fa86f80..7837af84c7a70723b5b4f193f3b129e48a0edea7 100644
--- a/src/streamlit_app.py
+++ b/src/streamlit_app.py
@@ -1,40 +1,979 @@
-import altair as alt
+import json
+from pathlib import Path
+from typing import Dict, List, Tuple
+
 import numpy as np
 import pandas as pd
 import streamlit as st
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
\ No newline at end of file
+st.set_page_config(page_title="VideoEval Explorer", layout="wide")
+st.title("VideoEval — Tables + Viewer with Human Scores")
+
+# =========================
+# Paths / Config
+# =========================
+mapping_json_path = "YOUTUBE_DATA/id_map.json"
+video_dir = "YOUTUBE_DATA"
+
+def uniq_sorted(s) -> list:
+    return sorted(pd.Series(s).dropna().unique().tolist())
+
+# --- Metric & video-id normalization for raters ---
+ALIASES = {
+    "action": "human_action",
+    "anatomy": "human_anatomy",
+    "appearance": "human_appearance",
+    "motion": "human_motion",
+    "overall": "overall",
+}
+def normalize_metric_name(name: str) -> str:
+    if not isinstance(name, str):
+        return str(name)
+    key = name.strip().lower()
+    return ALIASES.get(key, name)  # fall back to original if unknown
+
+def normalize_video_id(v) -> str:
+    s = str(v)
+    # enforce ".mp4" suffix to match main df
+    return s if s.endswith(".mp4") else f"{s}.mp4"
+
+# Model metric JSONs
+REQ = {
+    "action_mean_intra": "action_mean_intra.json",
+    "frame_diff_ord2": "frame_diff_ord2.json",
+}
+
+# Human JSONs (aggregate)
+HUMAN = {
+    "human_action": "human_scores_analysis_action_mos_centered.json",
+    "human_anatomy": "human_scores_analysis_anatomy_mos_centered.json",
+    "human_appearance": "human_scores_analysis_appearance_mos_centered.json",
+    "human_motion": "human_scores_analysis_motion_mos_centered.json",
+}
+
+# Rater JSONs glob patterns (support both)
+RATER_GLOBS = ["raters*.json", "raters/*.json"]
+
+# =========================
+# Helpers
+# =========================
+def load_json(p: Path) -> dict:
+    with open(p, "r") as f:
+        d = json.load(f)
+    out = {}
+    for k, v in d.items():
+        try:
+            out[str(k)] = float(v)
+        except Exception:
+            out[str(k)] = np.nan
+    return out
+
+def build_maps_from_mapping(p: Path) -> Tuple[Dict[str, str], Dict[str, str]]:
+    """
+    Returns:
+      - v2m: video_id -> model
+      - v2base: video_id -> base_name (original 'videoName', e.g. 'v_JumpingJack_g13_c02')
+    Accepts either 'video_to_model' or 'model_to_videoName_to_id' schema.
+    """
+    v2m, v2base = {}, {}
+    if not p or not p.exists():
+        return v2m, v2base
+
+    with open(p, "r") as f:
+        m = json.load(f)
+
+    if "video_to_model" in m and isinstance(m["video_to_model"], dict):
+        v2m = dict(m["video_to_model"])
+        return v2m, v2base
+
+    if "model_to_videoName_to_id" in m and isinstance(m["model_to_videoName_to_id"], dict):
+        for model, name_to_id in m["model_to_videoName_to_id"].items():
+            for base_name, cls_id in name_to_id.items():
+                vid = f"{cls_id}.mp4"  # filenames like Class__HASH.mp4
+                v2m[vid] = model
+                v2base[vid] = base_name
+    return v2m, v2base
+
+def minmax_normalize(series: pd.Series) -> pd.Series:
+    s = pd.to_numeric(series, errors="coerce")
+    if s.notna().sum() == 0:
+        return s
+    smin, smax = s.min(skipna=True), s.max(skipna=True)
+    if not np.isfinite(smin) or not np.isfinite(smax) or smin == smax:
+        return s.apply(lambda x: 0.0 if pd.notna(x) else np.nan)
+    return (s - smin) / (smax - smin)
+
+def fmt3(x):
+    try:
+        x = float(x)
+    except (TypeError, ValueError):
+        return ""
+    if not np.isfinite(x):
+        return ""
+    return f"{x:.3f}"
+
+def filter_by(df: pd.DataFrame, cls: str, mdl: str) -> pd.DataFrame:
+    """Single-select filter (used by the two tables)."""
+    out = df
+    if cls != "(All)":
+        out = out[out["class"] == cls]
+    if mdl != "(All)":
+        out = out[out["model"] == mdl]
+    return out
+
+def filter_by_multi(df: pd.DataFrame, classes: List[str] | None, models: List[str] | None) -> pd.DataFrame:
+    """Multi-select filter used in pairwise tabs. Empty/None means 'all'."""
+    out = df
+    if classes:
+        out = out[out["class"].isin(classes)]
+    if models:
+        out = out[out["model"].isin(models)]
+    return out
+
+def pairwise_agreement(df: pd.DataFrame, model_col: str, human_col: str):
+    """
+    Pairwise ranking agreement between model_col and human_col over all rows in df.
+    Returns: (accuracy, disagree_pairs_df, total_pairs)
+
+    Directions:
+    - Model metrics: BOTH lower is better
+      * action_mean_intra  -> use 'action_mean_intra_orig' (pre-negation)
+      * frame_diff_ord2    -> use 'frame_diff_ord2' (raw)
+    - Human metrics: higher is better
+    """
+    if model_col == "action_mean_intra":
+        use_model_col = "action_mean_intra_orig"
+    elif model_col == "frame_diff_ord2":
+        use_model_col = "frame_diff_ord2"
+    else:
+        use_model_col = model_col  # fallback
+
+    vids = df["video_id"].tolist()
+    mvals = pd.to_numeric(df[use_model_col], errors="coerce").values
+    hvals = pd.to_numeric(df[human_col], errors="coerce").values
+
+    rows = []
+    total = 0
+    agree = 0
+    n = len(vids)
+
+    for i in range(n):
+        for j in range(i + 1, n):
+            mi, mj = mvals[i], mvals[j]
+            hi, hj = hvals[i], hvals[j]
+            if not (np.isfinite(mi) and np.isfinite(mj) and np.isfinite(hi) and np.isfinite(hj)):
+                continue
+            if mi == mj or hi == hj:
+                continue
+
+            # model order: LOWER is better
+            model_order = "A>B" if mi < mj else "B>A"
+            # human order: HIGHER is better
+            human_order = "A>B" if hi > hj else "B>A"
+
+            is_agree = (model_order == human_order)
+            total += 1
+            if is_agree:
+                agree += 1
+            else:
+                rows.append({
+                    "video_A": vids[i],
+                    "video_B": vids[j],
+                    f"{model_col}_A": mi,
+                    f"{model_col}_B": mj,
+                    f"{human_col}_A": hi,
+                    f"{human_col}_B": hj,
+                    "model_order": model_order,
+                    "human_order": human_order,
+                    "agree": is_agree,
+                })
+
+    acc = (agree / total) if total > 0 else np.nan
+    disagree_df = pd.DataFrame(rows)
+    return acc, disagree_df, total
+
+def cross_model_pairwise(df: pd.DataFrame, model_col: str, human_col: str):
+    """
+    Pairwise agreement only across DIFFERENT MODELS but the SAME base video.
+    Requires df to have: ['video_id','model','base_name', model_col, human_col, 'action_mean_intra_orig'].
+    Returns: (accuracy, disagree_pairs_df, total_pairs)
+
+    Directions:
+    - Model metrics: BOTH lower is better (action_mean_intra_orig, frame_diff_ord2)
+    - Human metrics: higher is better
+    """
+    if model_col == "action_mean_intra":
+        use_model_col = "action_mean_intra_orig"
+    elif model_col == "frame_diff_ord2":
+        use_model_col = "frame_diff_ord2"
+    else:
+        use_model_col = model_col
+
+    rows = []
+    total = 0
+    agree = 0
+
+    scope = df.dropna(subset=["base_name"])
+
+    for base_name, g in scope.groupby("base_name"):
+        vids = g["video_id"].tolist()
+        models = g["model"].tolist()
+        mvals = pd.to_numeric(g[use_model_col], errors="coerce").values
+        hvals = pd.to_numeric(g[human_col], errors="coerce").values
+        n = len(vids)
+        for i in range(n):
+            for j in range(i + 1, n):
+                if models[i] == models[j]:
+                    continue  # cross-model only
+                mi, mj = mvals[i], mvals[j]
+                hi, hj = hvals[i], hvals[j]
+                if not (np.isfinite(mi) and np.isfinite(mj) and np.isfinite(hi) and np.isfinite(hj)):
+                    continue
+                if mi == mj or hi == hj:
+                    continue
+
+                model_order = "A>B" if mi < mj else "B>A"  # LOWER better
+                human_order = "A>B" if hi > hj else "B>A"  # HIGHER better
+
+                is_agree = (model_order == human_order)
+                total += 1
+                if is_agree:
+                    agree += 1
+                else:
+                    rows.append({
+                        "base_name": base_name,
+                        "video_A": vids[i],
+                        "model_A": models[i],
+                        f"{model_col}_A": mi,
+                        f"{human_col}_A": hi,
+                        "video_B": vids[j],
+                        "model_B": models[j],
+                        f"{model_col}_B": mj,
+                        f"{human_col}_B": hj,
+                        "model_order": model_order,
+                        "human_order": human_order,
+                        "agree": is_agree,
+                    })
+
+    acc = (agree / total) if total > 0 else np.nan
+    disagree_df = pd.DataFrame(rows)
+    return acc, disagree_df, total
+
+def spearman_all(df: pd.DataFrame, model_cols: list, human_cols: list) -> pd.DataFrame:
+    """Spearman rho between each model metric and each human metric over all videos."""
+    records = []
+    for m in model_cols:
+        for h in human_cols:
+            sub = df[[m, h]].dropna()
+            n = len(sub)
+            rho = sub.corr(method="spearman").iloc[0, 1] if n >= 2 else np.nan
+            records.append({"model_metric": m, "human_metric": h, "rho": rho, "n": n})
+    return pd.DataFrame(records)
+
+def spearman_by_group(df: pd.DataFrame, model_cols: list, human_cols: list, group_col: str, groups: List[str]) -> pd.DataFrame:
+    """
+    Spearman rho per group (group_col ∈ {'class','model'}). If groups is empty -> all unique values.
+    Returns tidy df with columns: [group_col, model_metric, human_metric, rho, n]
+    """
+    if not groups:
+        groups = sorted([g for g in df[group_col].dropna().unique().tolist()])
+    rows = []
+    for g in groups:
+        subdf = df[df[group_col] == g]
+        if subdf.empty:
+            continue
+        for m in model_cols:
+            for h in human_cols:
+                sub = subdf[[m, h]].dropna()
+                n = len(sub)
+                rho = sub.corr(method="spearman").iloc[0, 1] if n >= 2 else np.nan
+                rows.append({group_col: g, "model_metric": m, "human_metric": h, "rho": rho, "n": n})
+    return pd.DataFrame(rows)
+
+# ---------- Rater loading & inter-rater correlations ----------
+def _filename_to_rater_name(p: Path) -> str:
+    name = p.stem  # e.g., "chvskch_scores"
+    if name.endswith("_scores"):
+        name = name[:-7]
+    return name
+
+def _looks_like_video_key(k: str) -> bool:
+    s = str(k)
+    return "__" in s or s.endswith(".mp4") or s.endswith(".MP4")
+
+def _detect_rater_structure(obj) -> str:
+    """
+    Returns one of:
+      - 'metric_to_video'       : {metric: {video_id: score}, ...}
+      - 'video_to_score'        : {video_id: score, ...}
+      - 'video_to_metric_score' : {video_id: {metric: score}, ...}
+      - 'unknown'
+    """
+    if not isinstance(obj, dict) or not obj:
+        return "unknown"
+
+    # If values are not dicts -> {video_id: score}
+    first_val = next(iter(obj.values()))
+    if not isinstance(first_val, dict):
+        return "video_to_score"
+
+    # Values are dicts. Decide by looking at the OUTER KEYS.
+    outer_keys = list(obj.keys())
+    if any(_looks_like_video_key(k) for k in outer_keys):
+        return "video_to_metric_score"        # <-- your case
+    else:
+        return "metric_to_video"
+
+def load_raters(globs: List[str]) -> Dict[str, pd.DataFrame]:
+    """
+    Returns: dict metric_name -> wide DataFrame with index=video_id ('.mp4' enforced) and columns=rater_name.
+    Supports shapes:
+      - {metric: {video_id: score}}
+      - {video_id: score}                    -> metric 'overall'
+      - {video_id: {metric: score}}          -> (your case)
+    Metric names normalized to app's human_* names.
+    """
+    metric_to_frames: Dict[str, List[pd.DataFrame]] = {}
+    files = []
+    for pat in globs:
+        files.extend(Path(".").glob(pat))
+
+    for p in files:
+        try:
+            data = json.loads(Path(p).read_text())
+        except Exception:
+            continue
+
+        rater = _filename_to_rater_name(p)
+        shape = _detect_rater_structure(data)
+
+        if shape == "metric_to_video":
+            # {metric: {video_id: score}}
+            for metric, vid_scores in data.items():
+                metric_norm = normalize_metric_name(metric)
+                s = pd.Series({normalize_video_id(k): v for k, v in vid_scores.items()},
+                              name=rater, dtype="float")
+                metric_to_frames.setdefault(metric_norm, []).append(s.to_frame())
+
+        elif shape == "video_to_score":
+            # {video_id: score} -> 'overall'
+            s = pd.Series({normalize_video_id(k): v for k, v in data.items()},
+                          name=rater, dtype="float")
+            metric_to_frames.setdefault("overall", []).append(s.to_frame())
+
+        elif shape == "video_to_metric_score":
+            # {video_id: {metric: score}}  <-- YOUR FILES
+            bucket: Dict[str, Dict[str, float]] = {}
+            for vid, mdict in data.items():
+                if not isinstance(mdict, dict):
+                    continue
+                for metric, val in mdict.items():
+                    metric_norm = normalize_metric_name(metric)
+                    bucket.setdefault(metric_norm, {})[normalize_video_id(vid)] = val
+            for metric_norm, vid_scores in bucket.items():
+                s = pd.Series(vid_scores, name=rater, dtype="float")
+                metric_to_frames.setdefault(metric_norm, []).append(s.to_frame())
+
+        # else: unknown; skip
+
+    # Merge per metric
+    metric_to_wide: Dict[str, pd.DataFrame] = {}
+    for metric, frames in metric_to_frames.items():
+        if not frames:
+            continue
+        wide = frames[0]
+        for f in frames[1:]:
+            wide = wide.join(f, how="outer")
+        wide = wide.loc[:, ~wide.columns.duplicated()]
+        metric_to_wide[metric] = wide
+
+    return metric_to_wide
+
+def inter_rater_pairs_by_group(
+    rater_wide: pd.DataFrame,
+    video_meta: pd.DataFrame,
+    by: str,
+    values: List[str] | None = None,
+    min_overlap: int = 2,
+) -> pd.DataFrame:
+    """
+    Return per-rater *pairwise* correlations within each group.
+    Rows: [by, rater, other_rater, rho, n]
+    Filters: drop NaN rho; keep only pairs with n >= min_overlap.
+    """
+    pairs = inter_rater_corr_grouped(rater_wide, video_meta, by=by, values=values)
+    if pairs.empty:
+        return pd.DataFrame(columns=[by, "rater", "other_rater", "rho", "n"])
+
+    # Ensure integer overlaps
+    pairs["n"] = pairs["n"].astype(int)
+
+    # keep only pairs with enough overlap and non-null rho
+    pairs = pairs[(pairs["n"] >= min_overlap) & pairs["rho"].notna()]
+
+    # # keep only adequate overlap, drop NaNs
+    # pairs = pairs[pairs["n"] >= min_overlap].dropna(subset=["rho"])
+    if pairs.empty:
+        return pd.DataFrame(columns=[by, "rater", "other_rater", "rho", "n"])
+
+    # expand to directed rows so each rater has their own row per counterpart
+    a = pairs.rename(columns={"rater_i": "rater", "rater_j": "other_rater"})[[by, "rater", "other_rater", "rho", "n"]]
+    b = pairs.rename(columns={"rater_j": "rater", "rater_i": "other_rater"})[[by, "rater", "other_rater", "rho", "n"]]
+    out = pd.concat([a, b], ignore_index=True)
+
+    # sort for nicer reading
+    out = out.sort_values([by, "rater", "rho"], ascending=[True, True, False]).reset_index(drop=True)
+    return out
+
+
+def inter_rater_avg_by_group(
+    rater_wide: pd.DataFrame,
+    video_meta: pd.DataFrame,
+    by: str,
+    values: List[str] | None = None,
+    min_overlap: int = 2,
+) -> pd.DataFrame:
+    """
+    Average (unweighted) inter-rater Spearman rho per rater within each group.
+    Uses same filters as inter_rater_pairs_by_group.
+    Rows: [by, rater, mean_rho, num_pairs_used]
+    """
+    pairs_long = inter_rater_pairs_by_group(
+        rater_wide, video_meta, by=by, values=values, min_overlap=min_overlap
+    )
+    if pairs_long.empty:
+        return pd.DataFrame(columns=[by, "rater", "mean_rho", "num_pairs_used"])
+
+    agg = (
+        pairs_long.groupby([by, "rater"])
+        .agg(mean_rho=("rho", "mean"), num_pairs_used=("rho", "count"))
+        .reset_index()
+    )
+    agg["mean_rho"] = agg["mean_rho"].astype(float)
+    return agg
+
+def inter_rater_corr_grouped(
+    rater_wide: pd.DataFrame,
+    video_meta: pd.DataFrame,
+    by: str,
+    values: List[str] | None = None
+) -> pd.DataFrame:
+    """
+    Spearman inter-rater correlation matrices grouped by `by` ∈ {'class','model'}.
+    Requires ≥2 raters with ≥2 overlapping videos inside each group.
+    Returns tidy df: [by, rater_i, rater_j, rho, n]
+    """
+    assert by in ("class", "model")
+
+    def corr_to_long(df_corr: pd.DataFrame, counts: pd.DataFrame, group_label: str):
+        out = []
+        for i in df_corr.index:
+            for j in df_corr.columns:
+                if i >= j:
+                    continue
+                out.append({by: group_label, "rater_i": i, "rater_j": j,
+                            "rho": df_corr.loc[i, j], "n": int(counts.loc[i, j])})
+        return out
+
+    meta = video_meta[["video_id", "class", "model"]].drop_duplicates().set_index("video_id")
+    X = rater_wide.copy()
+    X.index = X.index.map(str)  # should already be normalized to '.mp4'
+    X = X.join(meta, how="left")
+
+    if not values:
+        values = sorted([v for v in X[by].dropna().unique().tolist()])
+
+    results = []
+
+    def corr_with_counts(M: pd.DataFrame) -> Tuple[pd.DataFrame, pd.DataFrame]:
+        rho = M.corr(method="spearman", min_periods=2)
+        mask = ~M.isna()
+        counts = pd.DataFrame(index=M.columns, columns=M.columns, dtype="float")
+        for a in M.columns:
+            for b in M.columns:
+                counts.loc[a, b] = float((mask[a] & mask[b]).sum())
+        return rho, counts
+
+    for g in values:
+        sub = X[X[by] == g]
+        if sub.empty:
+            continue
+        # keep only rater columns with at least 2 ratings
+        cols = [c for c in sub.columns if c not in ("class", "model")]
+        usable = [c for c in cols if sub[c].notna().sum() >= 2]
+        if len(usable) < 2:
+            continue
+        M = sub[usable]
+        rho, counts = corr_with_counts(M)
+        # if every pair has <2 overlaps, skip
+        if (counts.values < 2).all():
+            continue
+        results.extend(corr_to_long(rho, counts, g))
+
+    out = pd.DataFrame(results)
+    if not out.empty:
+        out["rho"] = out["rho"].astype(float)
+    return out
+
+
+def _corr_with_counts_matrix(M: pd.DataFrame) -> tuple[pd.DataFrame, pd.DataFrame]:
+    """Spearman rho matrix + overlap counts matrix for a wide (videos x raters) frame."""
+    rho = M.corr(method="spearman", min_periods=2)
+    mask = ~M.isna()
+    counts = pd.DataFrame(index=M.columns, columns=M.columns, dtype="int64")
+    for a in M.columns:
+        for b in M.columns:
+            counts.loc[a, b] = int((mask[a] & mask[b]).sum())
+    return rho, counts
+
+def inter_rater_pairs_overall(rater_wide: pd.DataFrame, min_overlap: int = 2) -> pd.DataFrame:
+    """
+    Pairwise inter-rater Spearman over the entire dataset (no grouping).
+    Returns columns: [rater, other_rater, rho, n]
+    n = overlapping videos used for that pair.
+    """
+    if rater_wide is None or rater_wide.empty:
+        return pd.DataFrame(columns=["rater", "other_rater", "rho", "n"])
+
+    # keep only rater columns with at least 2 ratings
+    usable = [c for c in rater_wide.columns if rater_wide[c].notna().sum() >= 2]
+    M = rater_wide[usable]
+    if M.shape[1] < 2:
+        return pd.DataFrame(columns=["rater", "other_rater", "rho", "n"])
+
+    rho, counts = _corr_with_counts_matrix(M)
+
+    rows = []
+    for i in rho.index:
+        for j in rho.columns:
+            if i >= j:
+                continue
+            n_overlap = int(counts.loc[i, j])
+            r = rho.loc[i, j]
+            if n_overlap >= min_overlap and pd.notna(r):
+                rows.append({"rater": i, "other_rater": j, "rho": float(r), "n": n_overlap})
+                rows.append({"rater": j, "other_rater": i, "rho": float(r), "n": n_overlap})  # directed
+
+    out = pd.DataFrame(rows)
+    if not out.empty:
+        out = out.sort_values(["rater", "rho"], ascending=[True, False]).reset_index(drop=True)
+    return out
+
+def inter_rater_avg_overall(rater_wide: pd.DataFrame, min_overlap: int = 2) -> pd.DataFrame:
+    """
+    Overall (no grouping) average Spearman rho per rater.
+    Returns columns: [rater, mean_rho, num_pairs_used]
+    """
+    pairs = inter_rater_pairs_overall(rater_wide, min_overlap=min_overlap)
+    if pairs.empty:
+        return pd.DataFrame(columns=["rater", "mean_rho", "num_pairs_used"])
+    agg = (pairs.groupby("rater")
+                 .agg(mean_rho=("rho", "mean"), num_pairs_used=("rho", "count"))
+                 .reset_index()
+                 .sort_values("mean_rho", ascending=False)
+                 .reset_index(drop=True))
+    return agg
+
+# =========================
+# Load aggregate scores
+# =========================
+scores = {k: load_json(Path(p)) for k, p in REQ.items()}
+human_scores = {k: load_json(Path(p)) for k, p in HUMAN.items()}
+
+all_keys = set()
+for d in scores.values():
+    all_keys |= set(d.keys())
+for d in human_scores.values():
+    all_keys |= set(d.keys())
+
+rows = []
+for vid in sorted(all_keys):
+    rows.append({
+        "video_id": vid,
+        "class": vid.split("__")[0] if "__" in vid else "UNK",
+        "action_mean_intra": scores["action_mean_intra"].get(vid, np.nan),
+        "frame_diff_ord2": scores["frame_diff_ord2"].get(vid, np.nan),
+        "human_action": human_scores["human_action"].get(vid, np.nan),
+        "human_anatomy": human_scores["human_anatomy"].get(vid, np.nan),
+        "human_appearance": human_scores["human_appearance"].get(vid, np.nan),
+        "human_motion": human_scores["human_motion"].get(vid, np.nan),
+    })
+
+df_raw = pd.DataFrame(rows)
+
+# Keep original action_mean_intra for pairwise; flip for display ONCE (your JSON is negative)
+df_raw["action_mean_intra"] = pd.to_numeric(df_raw["action_mean_intra"], errors="coerce")
+df_raw["action_mean_intra_orig"] = df_raw["action_mean_intra"].copy()
+df_raw["action_mean_intra"] = -df_raw["action_mean_intra_orig"]  # positive / higher-better in tables
+
+# Map models + base_name via mapping.json
+v2m, v2base = build_maps_from_mapping(Path(mapping_json_path))
+df_raw["model"] = df_raw["video_id"].map(v2m).fillna("UNK")
+df_raw["base_name"] = df_raw["video_id"].map(v2base)  # may be NaN
+
+# Metric columns (display + normalization)
+metric_cols = [
+    "action_mean_intra",   # positive (after flip) for display; pairwise uses *_orig
+    "frame_diff_ord2",     # raw; lower-better
+    "human_action",
+    "human_anatomy",
+    "human_appearance",
+    "human_motion",
+]
+human_cols = ["human_action", "human_anatomy", "human_appearance", "human_motion"]
+
+# Drop rows with NO human ratings at all
+df_raw = df_raw.dropna(subset=human_cols, how="all")
+
+# Normalized view for tables
+df_norm = df_raw.copy()
+for c in metric_cols:
+    df_norm[c] = minmax_normalize(df_norm[c])
+
+# =========================
+# UI Layout
+# =========================
+left, right = st.columns([2, 1])
+
+with left:
+    tab_tables, tab_agree, tab_global, tab_cross, tab_spear, tab_ir = st.tabs(
+        ["Tables", "Agreement", "Global Pairwise (any videos)", "Cross-Model (same videoName)", "Spearman", "Inter-Rater"]
+    )
+
+    # --------------------- TABLES (A/B) ---------------------
+    with tab_tables:
+        st.subheader("Comparison Tables")
+
+        # Table A
+        st.markdown("**Table A**")
+        use_norm_a = st.checkbox("Show normalized scores (0–1) — A", value=False, key="norm_a")
+        classes = ["(All)"] + uniq_sorted(df_raw["class"])
+        models = ["(All)"] + uniq_sorted(df_raw["model"])
+        ca, ma = st.columns(2)
+        with ca:
+            chosen_c_a = st.selectbox("Class (A)", classes, key="class_a")
+        with ma:
+            chosen_m_a = st.selectbox("Model (A)", models, key="model_a")
+        df_view_a = df_norm if use_norm_a else df_raw
+        filt_a = filter_by(df_view_a, chosen_c_a, chosen_m_a).copy()
+        disp_a = filt_a.copy()
+        disp_a[metric_cols] = disp_a[metric_cols].applymap(fmt3)
+        st.dataframe(disp_a, use_container_width=True, hide_index=True)
+
+        st.markdown("---")
+
+        # Table B
+        st.markdown("**Table B**")
+        use_norm_b = st.checkbox("Show normalized scores (0–1) — B", value=False, key="norm_b")
+        cb, mb = st.columns(2)
+        with cb:
+            chosen_c_b = st.selectbox("Class (B)", classes, key="class_b")
+        with mb:
+            chosen_m_b = st.selectbox("Model (B)", models, key="model_b")
+        df_view_b = df_norm if use_norm_b else df_raw
+        filt_b = filter_by(df_view_b, chosen_c_b, chosen_m_b).copy()
+        disp_b = filt_b.copy()
+        disp_b[metric_cols] = disp_b[metric_cols].applymap(fmt3)
+        st.dataframe(disp_b, use_container_width=True, hide_index=True)
+
+    # --------------------- AGREEMENT (multi-class) ---------------------
+    with tab_agree:
+        st.subheader("Agreement (Model vs Human) — multi-class")
+
+        all_models  = uniq_sorted(df_raw["model"])
+        all_classes = uniq_sorted(df_raw["class"])
+        c1, c2 = st.columns(2)
+        with c1:
+            chosen_classes = st.multiselect("Classes (empty = All)", all_classes, default=[], key="agree_classes")
+        with c2:
+            chosen_models = st.multiselect("Models (empty = All)", all_models, default=[], key="agree_models")
+
+        model_metric = st.selectbox(
+            "Model metric",
+            ["action_mean_intra", "frame_diff_ord2"],
+            index=0,
+            key="agree_model_metric",
+            help="Pairwise uses LOWER-is-better for both model metrics (action_mean_intra uses original sign)."
+        )
+        human_metric = st.selectbox("Human metric", human_cols, index=0, key="agree_human_metric")
+
+        scope_df = filter_by_multi(df_raw, chosen_classes, chosen_models).copy()
+        if "action_mean_intra_orig" not in scope_df.columns:
+            scope_df = scope_df.merge(df_raw[["video_id", "action_mean_intra_orig"]], on="video_id", how="left")
+        req_model_col = "action_mean_intra_orig" if model_metric == "action_mean_intra" else model_metric
+        scope_df = scope_df.dropna(subset=[req_model_col, human_metric], how="any")
+
+        if len(scope_df) < 2:
+            st.info("Need at least 2 videos (with non-missing metrics) to compute pairwise agreement.")
+        else:
+            acc, disagree_df, total_pairs = pairwise_agreement(scope_df, model_metric, human_metric)
+            st.markdown(f"**Pairwise accuracy:** {fmt3(acc)} (over {total_pairs} comparable pairs).")
+            if not disagree_df.empty:
+                df_show = disagree_df.copy()
+                numeric_cols = [f"{model_metric}_A", f"{model_metric}_B", f"{human_metric}_A", f"{human_metric}_B"]
+                for col in numeric_cols:
+                    if col in df_show.columns:
+                        df_show[col] = pd.to_numeric(df_show[col], errors="coerce").map(fmt3)
+                st.markdown("**Disagreeing pairs**")
+                st.dataframe(df_show, use_container_width=True, hide_index=True)
+            else:
+                st.success("All comparable pairs agree. 🎉")
+
+    # --------------------- GLOBAL PAIRWISE (any videos, multi-class) ---------------------
+    with tab_global:
+        st.subheader("Global Pairwise (any videos) — multi-class")
+
+        all_models  = uniq_sorted(df_raw["model"])
+        all_classes = uniq_sorted(df_raw["class"])
+
+        c1, c2 = st.columns(2)
+        with c1:
+            chosen_classes_g = st.multiselect("Classes (empty = All)", all_classes, default=[], key="global_classes")
+        with c2:
+            chosen_models_g = st.multiselect("Models (empty = All)", all_models, default=[], key="global_models")
+
+        model_metric_g = st.selectbox(
+            "Model metric",
+            ["action_mean_intra", "frame_diff_ord2"],
+            index=0,
+            key="global_model_metric",
+            help="Agreement rule: BOTH model metrics use LOWER-is-better (action_mean_intra uses original sign)."
+        )
+        human_metric_g = st.selectbox(
+            "Human metric",
+            human_cols,
+            index=0,
+            key="global_human_metric"
+        )
+
+        scope_df_g = filter_by_multi(df_raw, chosen_classes_g, chosen_models_g).copy()
+        if "action_mean_intra_orig" not in scope_df_g.columns:
+            scope_df_g = scope_df_g.merge(
+                df_raw[["video_id", "action_mean_intra_orig"]],
+                on="video_id",
+                how="left"
+            )
+        req_model_col_g = "action_mean_intra_orig" if model_metric_g == "action_mean_intra" else model_metric_g
+        scope_df_g = scope_df_g.dropna(subset=[req_model_col_g, human_metric_g], how="any")
+
+        if len(scope_df_g) < 2:
+            st.info("Need at least 2 videos (with non-missing metrics) to compute pairwise agreement.")
+        else:
+            acc_g, disagree_df_g, total_pairs_g = pairwise_agreement(scope_df_g, model_metric_g, human_metric_g)
+            st.markdown(f"**Global pairwise accuracy:** {fmt3(acc_g)} (over {total_pairs_g} comparable pairs).")
+
+            if not disagree_df_g.empty:
+                df_show_g = disagree_df_g.copy()
+                numeric_cols_g = [f"{model_metric_g}_A", f"{model_metric_g}_B", f"{human_metric_g}_A", f"{human_metric_g}_B"]
+                for col in numeric_cols_g:
+                    if col in df_show_g.columns:
+                        df_show_g[col] = pd.to_numeric(df_show_g[col], errors="coerce").map(fmt3)
+                st.markdown("**Disagreeing global pairs**")
+                st.dataframe(df_show_g, use_container_width=True, hide_index=True)
+            else:
+                st.success("All comparable global pairs agree. 🎉")
+
+    # --------------------- CROSS-MODEL (same base video, multi-class) ---------------------
+    with tab_cross:
+        st.subheader("Cross-Model Agreement — same original video (base_name), multi-class")
+
+        all_classes = uniq_sorted(df_raw["class"])
+        all_models = uniq_sorted(df_raw["model"])
+
+        c1, c2 = st.columns(2)
+        with c1:
+            chosen_classes2 = st.multiselect("Classes (empty = All)", all_classes, default=[], key="cross_classes")
+        with c2:
+            chosen_models2 = st.multiselect("Models (empty = All)", all_models, default=[], key="cross_models")
+
+        model_metric2 = st.selectbox(
+            "Model metric",
+            ["action_mean_intra", "frame_diff_ord2"],
+            index=0,
+            key="cross_model_metric",
+            help="Pairwise uses LOWER-is-better for both model metrics (action_mean_intra uses original sign)."
+        )
+        human_metric2 = st.selectbox(
+            "Human metric",
+            human_cols,
+            index=0,
+            key="cross_human_metric"
+        )
+
+        scope_df2 = filter_by_multi(df_raw, chosen_classes2, chosen_models2).copy()
+        if "action_mean_intra_orig" not in scope_df2.columns:
+            scope_df2 = scope_df2.merge(df_raw[["video_id", "action_mean_intra_orig"]], on="video_id", how="left")
+        req_model_col2 = "action_mean_intra_orig" if model_metric2 == "action_mean_intra" else model_metric2
+        scope_df2 = scope_df2.dropna(subset=["base_name", req_model_col2, human_metric2], how="any")
+
+        eligible = scope_df2.groupby("base_name")["model"].nunique().reset_index(name="n_models")
+        eligible_names = set(eligible[eligible["n_models"] >= 2]["base_name"].tolist())
+        scope_df2 = scope_df2[scope_df2["base_name"].isin(eligible_names)]
+
+        if scope_df2.empty:
+            st.info("No base videos with at least two different models in the current filters.")
+        else:
+            acc2, disagree_df2, total_pairs2 = cross_model_pairwise(scope_df2, model_metric2, human_metric2)
+            st.markdown(f"**Cross-model pairwise accuracy:** {fmt3(acc2)} (over {total_pairs2} comparable cross-model pairs).")
+            if not disagree_df2.empty:
+                df_show2 = disagree_df2.copy()
+                numeric_cols2 = [f"{model_metric2}_A", f"{model_metric2}_B", f"{human_metric2}_A", f"{human_metric2}_B"]
+                for col in numeric_cols2:
+                    if col in df_show2.columns:
+                        df_show2[col] = pd.to_numeric(df_show2[col], errors="coerce").map(fmt3)
+                st.markdown("**Disagreeing cross-model pairs (same base video)**")
+                st.dataframe(df_show2, use_container_width=True, hide_index=True)
+            else:
+                st.success("All comparable cross-model pairs agree. 🎉")
+
+    # --------------------- SPEARMAN (separate views per human metric) ---------------------
+    with tab_spear:
+        st.subheader("Spearman correlations (separate by human metric)")
+
+        model_metrics_all = ["action_mean_intra", "frame_diff_ord2"]
+        # one sub-tab per human metric
+        sub_tabs = st.tabs(human_cols)
+        for tab_obj, hmetric in zip(sub_tabs, human_cols):
+            with tab_obj:
+                st.caption(f"Human metric: **{hmetric}**")
+                # Overall (only rows where the specific human metric is present)
+                spear_overall = spearman_all(df_raw, model_metrics_all, [hmetric])
+                show = spear_overall.copy()
+                show["rho"] = show["rho"].map(fmt3)
+                st.markdown("**Overall**")
+                st.dataframe(show, use_container_width=True, hide_index=True)
+
+                st.markdown("---")
+                st.markdown("**By Class**")
+                classes_all = uniq_sorted(df_raw["class"])
+                chosen_cls = st.multiselect("Classes (empty = All)", classes_all, default=[], key=f"spear_cls_{hmetric}")
+                spc = spearman_by_group(df_raw, model_metrics_all, [hmetric], "class", chosen_cls)
+                spc["rho"] = spc["rho"].map(fmt3)
+                st.dataframe(spc, use_container_width=True, hide_index=True)
+
+                st.markdown("---")
+                st.markdown("**By Model**")
+                models_all = uniq_sorted(df_raw["model"])
+                chosen_mdl = st.multiselect("Models (empty = All)", models_all, default=[], key=f"spear_mdl_{hmetric}")
+                spm = spearman_by_group(df_raw, model_metrics_all, [hmetric], "model", chosen_mdl)
+                spm["rho"] = spm["rho"].map(fmt3)
+                st.dataframe(spm, use_container_width=True, hide_index=True)
+
+    # --------------------- INTER-RATER (by class / by model ONLY) ---------------------
+    with tab_ir:
+        st.subheader("Inter-Rater Correlations (Spearman) — by Class / by Model")
+
+        metric_to_wide = load_raters(RATER_GLOBS)
+
+        # Quick diagnostics so you can see coverage
+        # Overall average inter-rater correlation (no grouping) per rater, per metric
+        if metric_to_wide:
+            st.caption("Overall inter-rater averages (across all classes/models)")
+            rows = []
+            for metric, wide in metric_to_wide.items():
+                avg = inter_rater_avg_overall(wide, min_overlap=2)
+                if not avg.empty:
+                    tmp = avg.copy()
+                    tmp.insert(0, "metric", metric)
+                    rows.append(tmp)
+            if rows:
+                overall_tbl = pd.concat(rows, ignore_index=True)
+                overall_tbl["mean_rho"] = overall_tbl["mean_rho"].map(fmt3)
+                st.dataframe(overall_tbl, use_container_width=True, hide_index=True)
+            else:
+                st.info("No rater pairs with enough overlapping videos to compute overall averages.")
+
+        if not metric_to_wide:
+            st.info("No rater files found. Expected patterns: 'raters*.json' or 'raters/*.json'.")
+        else:
+            meta = df_raw[["video_id", "class", "model"]].drop_duplicates()
+
+            metrics_available = sorted(metric_to_wide.keys())
+            chosen_metric = st.selectbox("Rater metric", metrics_available, key="ir_metric")
+
+            wide = metric_to_wide.get(chosen_metric)
+            if wide is None or wide.empty:
+                st.info("Selected metric has no rater data.")
+            else:
+                sub_by_cls, sub_by_mdl = st.tabs(["By Class", "By Model"])
+
+                with sub_by_cls:
+                    classes_all = uniq_sorted(meta["class"])
+                    chosen_cls_ir = st.multiselect("Classes (empty = All)", classes_all, default=[], key="ir_classes")
+
+                    # Per-rater PAIRWISE
+                    pairs_cls = inter_rater_pairs_by_group(wide, meta, by="class", values=chosen_cls_ir, min_overlap=2)
+                    if pairs_cls.empty:
+                        st.info("Not enough overlap to compute class-wise inter-rater correlations.")
+                    else:
+                        show_pairs = pairs_cls.copy()
+                        show_pairs["rho"] = show_pairs["rho"].map(fmt3)
+                        st.markdown("**Per-rater pairwise correlations**")
+                        st.dataframe(show_pairs, use_container_width=True, hide_index=True)
+
+                    # Per-rater AVERAGE
+                    avg_cls = inter_rater_avg_by_group(wide, meta, by="class", values=chosen_cls_ir, min_overlap=2)
+                    if avg_cls.empty:
+                        st.info("Not enough overlap to compute class-wise inter-rater averages.")
+                    else:
+                        show_avg = avg_cls.copy()
+                        show_avg["mean_rho"] = show_avg["mean_rho"].map(fmt3)
+                        st.markdown("**Per-rater average correlation**")
+                        st.dataframe(show_avg, use_container_width=True, hide_index=True)
+
+                with sub_by_mdl:
+                    models_all = uniq_sorted(meta["model"])
+                    chosen_mdl_ir = st.multiselect("Models (empty = All)", models_all, default=[], key="ir_models")
+
+                    # Per-rater PAIRWISE
+                    pairs_mdl = inter_rater_pairs_by_group(wide, meta, by="model", values=chosen_mdl_ir, min_overlap=2)
+                    if pairs_mdl.empty:
+                        st.info("Not enough overlap to compute model-wise inter-rater correlations.")
+                    else:
+                        show_pairs = pairs_mdl.copy()
+                        show_pairs["rho"] = show_pairs["rho"].map(fmt3)
+                        st.markdown("**Per-rater pairwise correlations**")
+                        st.dataframe(show_pairs, use_container_width=True, hide_index=True)
+
+                    # Per-rater AVERAGE
+                    avg_mdl = inter_rater_avg_by_group(wide, meta, by="model", values=chosen_mdl_ir, min_overlap=2)
+                    if avg_mdl.empty:
+                        st.info("Not enough overlap to compute model-wise inter-rater averages.")
+                    else:
+                        show_avg = avg_mdl.copy()
+                        show_avg["mean_rho"] = show_avg["mean_rho"].map(fmt3)
+                        st.markdown("**Per-rater average correlation**")
+                        st.dataframe(show_avg, use_container_width=True, hide_index=True)
+
+with right:
+    st.subheader("Video Viewer")
+
+    all_vids = df_raw["video_id"].tolist()
+    if len(all_vids) == 0:
+        st.info("No videos available after filtering rows with no human ratings.")
+    else:
+        selected_vid = st.selectbox("Choose a video id", sorted(all_vids))
+        if selected_vid:
+            vid_path = str(Path(video_dir) / selected_vid)
+            st.video(vid_path)
+
+            row_view = df_raw[df_raw["video_id"] == selected_vid].iloc[0]
+
+            st.markdown("### Scores")
+            st.caption("Display: action_mean_intra is flipped once for readability; pairwise uses original (lower is better).")
+
+            model_metrics = {
+                k: round(float(row_view[k]), 3)
+                for k in ["action_mean_intra", "frame_diff_ord2"]
+                if pd.notna(row_view[k])
+            }
+            human_metrics = {
+                k: round(float(row_view[k]), 3)
+                for k in ["human_action", "human_anatomy", "human_appearance", "human_motion"]
+                if pd.notna(row_view[k])
+            }
+
+            st.write("**Model metrics:**")
+            st.json(model_metrics)
+            st.write("**Human scores:**")
+            st.json(human_metrics)
\ No newline at end of file
diff --git a/src/streamlit_app2.py b/src/streamlit_app2.py
new file mode 100644
index 0000000000000000000000000000000000000000..99d0b84662681e7d21a08fcce44908344fa86f80
--- /dev/null
+++ b/src/streamlit_app2.py
@@ -0,0 +1,40 @@
+import altair as alt
+import numpy as np
+import pandas as pd
+import streamlit as st
+
+"""
+# Welcome to Streamlit!
+
+Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
+If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
+forums](https://discuss.streamlit.io).
+
+In the meantime, below is an example of what you can do with just a few lines of code:
+"""
+
+num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
+num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
+
+indices = np.linspace(0, 1, num_points)
+theta = 2 * np.pi * num_turns * indices
+radius = indices
+
+x = radius * np.cos(theta)
+y = radius * np.sin(theta)
+
+df = pd.DataFrame({
+    "x": x,
+    "y": y,
+    "idx": indices,
+    "rand": np.random.randn(num_points),
+})
+
+st.altair_chart(alt.Chart(df, height=700, width=700)
+    .mark_point(filled=True)
+    .encode(
+        x=alt.X("x", axis=None),
+        y=alt.Y("y", axis=None),
+        color=alt.Color("idx", legend=None, scale=alt.Scale()),
+        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
+    ))
\ No newline at end of file