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main.py

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+import streamlit as st
+from langchain_experimental.data_anonymizer import PresidioAnonymizer, PresidioReversibleAnonymizer
+from langchain_groq import ChatGroq
+# from langchain.chat_models import ChatGroq 
+from dotenv import load_dotenv
+import os
+# Load environment variables
+load_dotenv()
+
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+
+# Initialize anonymizer
+anonymizer = PresidioReversibleAnonymizer()
+llm = ChatGroq(model_name="llama-3.3-70b-versatile")  # Choose an available model
+
+st.title("Call on Doc Data Anonymization")
+
+# User Input
+user_input = st.text_area("Enter your text:", "My name is John Doe and my phone number is 123-456-7890.")
+
+if st.button("Process"):
+    # Anonymization
+    anonymized_text = anonymizer.anonymize(user_input)
+    st.subheader("1. Original Text:")
+    st.write(user_input)
+    
+    st.subheader("2. Anonymized Text:")
+    st.write(anonymized_text)
+    
+    # Get LLM response
+    response = llm.predict(anonymized_text)
+    st.subheader("3. LLM Response:")
+    st.write(response)
+    
+    # De-anonymization
+    deanonymized_response = anonymizer.deanonymize(response)
+    st.subheader("4. De-anonymized Response:")
+    st.write(deanonymized_response)

requirements.txt

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+pip install groq langchain langchain-core langchain-community streamlit presidio-analyzer presidio-anonymizer
+
+pip install langchain-groq
+
+pip install Faker