Words in vocab are already lowercase

app.py

@@ -2,10 +2,10 @@ import os
 import random
 
 import gradio as gr
-import seaborn as sns
 import matplotlib.pyplot as plt
 import numpy as np
 import polars as pl
+import seaborn as sns
 import torch
 from huggingface_hub import hf_hub_download
 from PIL import Image
@@ -35,16 +35,6 @@ def load_model():
     return model
 
 
-model = load_model()
-
-# Transform configuration
-transform = v2.Compose([
-    v2.Resize((224, 224)),
-    v2.ToDtype(torch.float32, scale=True),
-    v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-])
-
-
 # Load vocabulary
 def load_vocabulary():
     hf_hub_download("ScribbleItAI/efficientnet-b0",
@@ -64,6 +54,14 @@ def compute_word_weights(vocabulary):
     return words, weights
 
 
+model = load_model()
+
+transform = v2.Compose([
+    v2.Resize((224, 224)),
+    v2.ToDtype(torch.float32, scale=True),
+    v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
 vocabulary = load_vocabulary()
 words, weights = compute_word_weights(vocabulary)
 
@@ -93,8 +91,6 @@ def process_image(image, current_word):
         })
 
     predictions_df = pl.DataFrame(predictions)
-    predictions_df = predictions_df.with_columns(
-        pl.col("word").str.to_lowercase())
     predictions_df = predictions_df.group_by("word").agg(
         pl.col("prob").max().alias("prob"))
     predictions_df = predictions_df.sort("prob", descending=True).head(10)