Upload app.py with huggingface_hub

app.py

@@ -1,28 +1,22 @@
 import os
+import os
 import shutil
 import zipfile
 import pathlib
 import pandas as pd
 import gradio as gr
 import huggingface_hub as h
-from huggingface_hub import HfApi, Repository, create_repo
+from huggingface_hub import HfApi, Repository, create_repo, login
 import autogluon.tabular
+import getpass
+import shutil
+from git import Repo as GitRepo
 
 model_repo_id = "madhavkarthi/24679-HW2-tabular-autolguon-predictor"
 zip_filename  = "autogluon_predictor_dir.zip"
 cache_dir = pathlib.Path("hf_assests")
 extract_dir = cache_dir / "predictor_native"
 
-feature_col = ["right hand notes", "left hand notes",
-               "measures", "Key Center", "marking",
-]
-target_col = "Target (Composer)"
-
-outcome_lab = {
-    0: "Beethoven",
-    1: "Mozart"
-}
-
 def prepare_predictor_dir() -> str:
     cache_dir.mkdir(parents=True, exist_ok=True)
     local_zip = h.hf_hub_download(
@@ -44,25 +38,14 @@ def prepare_predictor_dir() -> str:
 predictor_dir = prepare_predictor_dir()
 predictor = autogluon.tabular.TabularPredictor.load(predictor_dir, require_py_version_match=False)
 
-def human_label(c):
-    try:
-        ci = int(c)
-        if ci in outcome_lab:
-            return outcome_lab[ci]
-    except Exception:
-        pass
-    if c in outcome_lab:
-        return outcome_lab[c]
-    return str(c)
-
 def do_predict(right_hand_notes, left_hand_notes, measures, Key_Center, marking):
 
     row = {
         feature_col[0]: int(right_hand_notes),
         feature_col[1]: int(left_hand_notes),
         feature_col[2]: int(measures),
-        feature_col[3]: float(Key_Center),
-        feature_col[4]: float(marking),
+        feature_col[3]: int(Key_Center),
+        feature_col[4]: int(marking),
     }
     X = pd.DataFrame([row], columns=feature_col)
 
@@ -89,13 +72,6 @@ def do_predict(right_hand_notes, left_hand_notes, measures, Key_Center, marking)
 
     return pred_label, proba_dict
 
-examples = [
-    [108, 82, 16, 3, 1], #Mozart
-    [196, 136, 29, 2, 2], #Mozart
-    [96, 49, 13, 2, 4], #Beethoven
-    [104, 86, 19, 6, 5] #Beethoven
-]
-
 key_center_mapping = {
     0: "A", 1: "Bb", 2: "B", 3: "C", 4: "Db", 5: "D",
     6: "Eb", 7: "E", 8: "F", 9: "Gb", 10: "G", 11: "Ab"