Update README.md

README.md

@@ -14,3 +14,53 @@ def preprocess_data(data):
     data = data.drop(columns=sensitive_cols)
 
     return data
+
+
+
+import transformers
+import pandas as pd
+import streamlit as st
+from preprocess import preprocess_data
+
+def anonymize_text(text):
+    model_name = "distilbert-base-uncased"
+    tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
+    model = transformers.AutoModelForMaskedLM.from_pretrained(model_name)
+
+    input_ids = tokenizer.encode(text, return_tensors="pt")
+    mask_token_index = torch.where(input_ids == tokenizer.mask_token_id)[1]
+
+    token_logits = model(input_ids)[0]
+    mask_token_logits = token_logits[0, mask_token_index, :]
+
+    top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()
+
+    anonymized_text = []
+    for token in top_5_tokens:
+        token = tokenizer.decode([token])
+        anonymized_text.append(token)
+
+    return anonymized_text
+
+def run_app():
+    st.title("Text Anonymization App")
+
+    # File upload
+    st.subheader("Upload your data")
+    file = st.file_uploader("Upload CSV", type=["csv"])
+
+    if file is not None:
+        # Read the file
+        data = pd.read_csv(file)
+
+        # Preprocess the data
+        preprocessed_data = preprocess_data(data)
+
+        # Column selection
+        st.subheader("Select columns to anonymize")
+        selected_columns = []
+        for col in preprocessed_data.columns:
+            if st.checkbox(col):
+                selected_columns.append(col)
+
+        #