diff --git a/model.py b/model.py
index 4ae8b70..f9f9259 100644
--- a/model.py
+++ b/model.py
@@ -7,50 +7,73 @@
 from langchain.llms import CTransformers
 from langchain.chains import ConversationalRetrievalChain
 
-st.title("Conversational Retrieval System")
+def add_vertical_space(spaces=1):
+    for _ in range(spaces):
+        st.sidebar.markdown("---")
 
-DB_FAISS_PATH = "vectorstore/db_faiss"
-TEMP_DIR = "temp"
+def main():
+    st.set_page_config(page_title="Llama-2-GGML CSV Chatbot")
+    st.title("Llama-2-GGML CSV Chatbot")
 
-# Create temp directory if it doesn't exist
-if not os.path.exists(TEMP_DIR):
-    os.makedirs(TEMP_DIR)
+    st.sidebar.title("About")
+    st.sidebar.markdown('''
+        The Llama-2-GGML CSV Chatbot uses the **Llama-2-7B-Chat-GGML** model.
+        
+        ### 🔄Bot evolving, stay tuned!
+        
+        ## Useful Links 🔗
+        
+        - **Model:** [Llama-2-7B-Chat-GGML](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/tree/main) 📚
+        - **GitHub:** [ThisIs-Developer/Llama-2-GGML-CSV-Chatbot](https://github.com/ThisIs-Developer/Llama-2-GGML-CSV-Chatbot) 💬
+    ''')
 
-# Sidebar for uploading CSV file
-uploaded_file = st.sidebar.file_uploader("Upload CSV file", type=['csv'])
+    DB_FAISS_PATH = "vectorstore/db_faiss"
+    TEMP_DIR = "temp"
 
-if uploaded_file is not None:
-    file_path = os.path.join(TEMP_DIR, uploaded_file.name)
-    with open(file_path, "wb") as f:
-        f.write(uploaded_file.getvalue())
+    if not os.path.exists(TEMP_DIR):
+        os.makedirs(TEMP_DIR)
 
-    st.write(f"Uploaded file: {uploaded_file.name}")
-    st.write("Processing CSV file...")
+    uploaded_file = st.sidebar.file_uploader("Upload CSV file", type=['csv'])
 
-    loader = CSVLoader(file_path=file_path, encoding="utf-8", csv_args={'delimiter': ','})
-    data = loader.load()
+    add_vertical_space(1)
+    st.sidebar.write('Made by [@ThisIs-Developer](https://huggingface.co/ThisIs-Developer)')
 
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
-    text_chunks = text_splitter.split_documents(data)
+    if uploaded_file is not None:
+        file_path = os.path.join(TEMP_DIR, uploaded_file.name)
+        with open(file_path, "wb") as f:
+            f.write(uploaded_file.getvalue())
 
-    st.write(f"Total text chunks: {len(text_chunks)}")
+        st.write(f"Uploaded file: {uploaded_file.name}")
+        st.write("Processing CSV file...")
 
-    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
-    docsearch = FAISS.from_documents(text_chunks, embeddings)
-    docsearch.save_local(DB_FAISS_PATH)
+        loader = CSVLoader(file_path=file_path, encoding="utf-8", csv_args={'delimiter': ','})
+        data = loader.load()
 
-    llm = CTransformers(model="models/llama-2-7b-chat.ggmlv3.q4_0.bin",
-                        model_type="llama",
-                        max_new_tokens=512,
-                        temperature=0.1)
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
+        text_chunks = text_splitter.split_documents(data)
 
-    qa = ConversationalRetrievalChain.from_llm(llm, retriever=docsearch.as_retriever())
+        st.write(f"Total text chunks: {len(text_chunks)}")
 
-    st.write("Enter your query:")
-    query = st.text_input("Input Prompt:")
-    if query:
-        chat_history = []
-        result = qa({"question": query, "chat_history": chat_history})
-        st.write("Response:", result['answer'])
+        embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
+        docsearch = FAISS.from_documents(text_chunks, embeddings)
+        docsearch.save_local(DB_FAISS_PATH)
 
-    os.remove(file_path)  # Remove the temporary file after processing
+        llm = CTransformers(model="models/llama-2-7b-chat.ggmlv3.q4_0.bin",
+                            model_type="llama",
+                            max_new_tokens=512,
+                            temperature=0.1)
+
+        qa = ConversationalRetrievalChain.from_llm(llm, retriever=docsearch.as_retriever())
+
+        st.write("Enter your query:")
+        query = st.text_input("Input Prompt:")
+        if query:
+            with st.spinner("Processing your question..."):
+                chat_history = []
+                result = qa({"question": query, "chat_history": chat_history})
+                st.write("Response:", result['answer'])
+
+        os.remove(file_path)
+
+if __name__ == "__main__":
+    main()