diff --git a/files.py b/files.py
index 3d4661485..ab999fd62 100644
--- a/files.py
+++ b/files.py
@@ -61,7 +61,7 @@ def filter_file(file, supabase, vector_store):
             return False
 
 def url_uploader(supabase, openai_key, vector_store):
-    url = st.text_area("## Add an url",placeholder="https://www.quivr.app")
+    url = st.text_area("**Add an url**",placeholder="https://www.quivr.app")
     button = st.button("Add the URL to the database")
     if button:
         html = get_html(url)
diff --git a/question.py b/question.py
index c3e13f96d..3aa270499 100644
--- a/question.py
+++ b/question.py
@@ -22,26 +22,53 @@ def count_tokens(question, model):
 
 
 def chat_with_doc(model, vector_store: SupabaseVectorStore):
+
+    if 'chat_history' not in st.session_state:
+        st.session_state['chat_history'] = []
+    
+    
     question = st.text_area("## Ask a question")
-    button = st.button("Ask")
-    count_button = st.button("Count Tokens", type='secondary')
+    columns = st.columns(3)
+    with columns[0]:
+        button = st.button("Ask")
+    with columns[1]:
+        count_button = st.button("Count Tokens", type='secondary')
+    with columns[2]:
+        clear_history = st.button("Clear History", type='secondary')
+    
+    for speaker, text in st.session_state['chat_history']:
+            st.markdown(f"**{speaker}:** {text}")
+    
+    if clear_history:
+        st.session_state['chat_history'] = []
+        st.experimental_rerun()
+
     if button:
+        qa = None
         if model.startswith("gpt"):
             logger.info('Using OpenAI model %s', model)
             qa = ConversationalRetrievalChain.from_llm(
                 OpenAI(
                     model_name=st.session_state['model'], openai_api_key=openai_api_key, temperature=st.session_state['temperature'], max_tokens=st.session_state['max_tokens']), vector_store.as_retriever(), memory=memory, verbose=True)
-            result = qa({"question": question})
-            logger.info('Result: %s', result)
-            st.write(result["answer"])
         elif anthropic_api_key and model.startswith("claude"):
             logger.info('Using Anthropics model %s', model)
             qa = ConversationalRetrievalChain.from_llm(
                 ChatAnthropic(
                     model=st.session_state['model'], anthropic_api_key=anthropic_api_key, temperature=st.session_state['temperature'], max_tokens_to_sample=st.session_state['max_tokens']), vector_store.as_retriever(), memory=memory, verbose=True, max_tokens_limit=102400)
-            result = qa({"question": question})
-            logger.info('Result: %s', result)
-            st.write(result["answer"])
+        
+        
+        st.session_state['chat_history'].append(("You", question))
+
+        # Generate model's response and add it to chat history
+        model_response = qa({"question": question})
+        logger.info('Result: %s', model_response)
+
+        st.session_state['chat_history'].append(("Quivr", model_response["answer"]))
+
+        # Display chat history
+        for speaker, text in st.session_state['chat_history']:
+            st.empty()
+            st.markdown(f"**{speaker}:** {text}")
 
     if count_button:
         st.write(count_tokens(question, model))