Update app[tapex].py

app[tapex].py

@@ -4,11 +4,19 @@ import torch
 from transformers import TapexTokenizer, BartForConditionalGeneration
 import datetime
 
-# Load the data
-df = pd.read_excel('discrepantes.xlsx')
-df.fillna(0, inplace=True)
-table_data = df.astype(str)
-print(table_data.head())
+# Load the CSV file
+df = pd.read_csv("anomalies.csv", quotechar='"')
+
+# Filter 'real' higher than 10 Million
+df= df[df['real'] >= 1000000.]
+
+# Convert 'real' column to standard float format and then to strings
+df['real'] = df['real'].apply(lambda x: f"{x:.2f}")
+
+# Fill NaN values and convert all columns to strings
+df = df.fillna('').astype(str)
+
+table_data = df
 
 # Function to generate a response using the TAPEX model
 def response(user_question, table_data):