Update app.py

app.py

@@ -34,10 +34,7 @@ import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
 # Input data files are available in the read-only "../input/" directory
 # For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
 
-import os
-for dirname, _, filenames in os.walk('/kaggle/input'):
-    for filename in filenames:
-        print(os.path.join(dirname, filename))
+
 
 # You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All"
 # You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
@@ -69,7 +66,7 @@ import gc
 import os
 
 config = {
-    'model': '/kaggle/input/transformers-model-downloader-pytorch-tf2-0/microsoft/deberta-v3-base',
+    'model': 'kaitehtzeng/primary_app/microsoft/deberta-v3-base',
     'dropout': 0.2,
     'max_length': 512,
     'batch_size':3,
@@ -84,7 +81,7 @@ Comparing two essays. <br>
 One predicted written by students, one predicted written by LLM
 """
 
-train_essays = pd.read_csv("D:\first app\train_essays.csv")
+train_essays = pd.read_csv("kaitehtzeng/primary_app/train_essays.csv")
 
 
 import transformers
@@ -141,7 +138,7 @@ class MeanPooling(nn.Module):
         return mean_embedding
 
 model = mymodel(config).to(device=config['device'])
-model.load_state_dict(torch.load('/kaggle/input/fine-tune-model/my_model.pth'))
+model.load_state_dict(torch.load('kaitehtzeng/primary_app/my_model.pth'))
 model.eval()
 
 #preds = []
@@ -184,7 +181,7 @@ def trial(text):
     else:
         return "Student"
 
-!pip install -q gradio==3.45.0
+pip install -q gradio==3.45.0
 
 import gradio as gr