in the following streamlit code can we have the text "Using Ollama Vision Model for Image Analysis" below the "Enter your prompt here" input box:

import streamlit as st
import ollama
from PIL import Image
import io
import json
from datetime import datetime
import os
import shutil

def delete_conversation(filename):
    # Delete JSON file
    json_path = os.path.join('conversations', filename)
    if os.path.exists(json_path):
        os.remove(json_path)
    
    # Delete associated image if it exists
    image_filename = f"image_{filename.split('_')[1].split('.')[0]}.png"
    image_path = os.path.join('conversations', image_filename)
    if os.path.exists(image_path):
        os.remove(image_path)

def save_conversation(messages, image=None):
    if not messages:  # Don't save empty conversations
        return None
        
    if not os.path.exists('conversations'):
        os.makedirs('conversations')
    
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    
    conversation_data = {
        'timestamp': timestamp,
        'messages': messages
    }
    
    filename = f'conversations/conversation_{timestamp}.json'
    with open(filename, 'w') as f:
        json.dump(conversation_data, f, indent=4)
    
    if image:
        image_filename = f'conversations/image_{timestamp}.png'
        image.save(image_filename)
    
    return filename

def load_conversation(filename):
    with open(filename, 'r') as f:
        return json.load(f)

def get_saved_conversations():
    if not os.path.exists('conversations'):
        return []
    files = [f for f in os.listdir('conversations') if f.endswith('.json')]
    conversations = []
    for file in files:
        with open(os.path.join('conversations', file), 'r') as f:
            data = json.load(f)
            conversations.append({
                'filename': file,
                'timestamp': data['timestamp'],
                'preview': data['messages'][0]['content'] if data['messages'] else 'Empty conversation'
            })
    return sorted(conversations, key=lambda x: x['timestamp'], reverse=True)

def process_image_and_text(image, text_prompt, messages_history):
    if image is not None:
        img_byte_arr = io.BytesIO()
        image.save(img_byte_arr, format=image.format)
        img_byte_arr = img_byte_arr.getvalue()
        
        try:
            response = ollama.chat(
                model='llama3.2-vision',
                messages=[*messages_history, {
                    'role': 'user',
                    'content': text_prompt,
                    'images': [img_byte_arr]
                }]
            )
            return response['message']['content']
        except Exception as e:
            return f"Error processing request: {str(e)}"
    return "Please upload an image first"

def main():
    st.set_page_config(
        page_title="Image Analysis with Ollama",
        page_icon="🔍",
        layout="wide"
    )
    
    # Initialize session states
    if 'messages' not in st.session_state:
        st.session_state.messages = []
    if 'current_image' not in st.session_state:
        st.session_state.current_image = None
    if 'uploaded_file' not in st.session_state:
        st.session_state.uploaded_file = None
    
    st.title("Image Analysis with Ollama Vision Model")
    
    # Sidebar for conversation management
    with st.sidebar:
        st.header("Conversation Management")
        
        # New conversation button
        if st.button("New Conversation"):
            if st.session_state.messages:
                save_conversation(st.session_state.messages, st.session_state.current_image)
            st.session_state.messages = []
            st.session_state.current_image = None
            st.session_state.uploaded_file = None
            st.rerun()
        
        # Clear conversation button
        if st.button("Clear Current Conversation"):
            st.session_state.messages = []
            st.session_state.current_image = None
            st.rerun()
        
        # Load and manage previous conversations
        st.header("Previous Conversations")
        conversations = get_saved_conversations()
        for conv in conversations:
            col1, col2 = st.columns([3, 1])
            with col1:
                if st.button(f"Load: {conv['timestamp']}", key=f"load_{conv['filename']}"):
                    loaded_conv = load_conversation(os.path.join('conversations', conv['filename']))
                    st.session_state.messages = loaded_conv['messages']
                    st.rerun()
            with col2:
                if st.button("🗑️", key=f"delete_{conv['filename']}"):
                    delete_conversation(conv['filename'])
                    st.rerun()
        
        # Add button to delete all conversations
        if st.button("Delete All Conversations"):
            if os.path.exists('conversations'):
                shutil.rmtree('conversations')
            st.rerun()
    
    # Main chat interface
    col1, col2 = st.columns(2)
    
    with col1:
        uploaded_file = st.file_uploader(
            "Choose an image...", 
            type=['png', 'jpg', 'jpeg'],
            key="file_uploader"
        )
        
        if uploaded_file is not None:
            image = Image.open(uploaded_file)
            st.session_state.current_image = image
            st.image(image, caption='Uploaded Image', use_container_width=True)
    
    # Display chat history
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])
    
    # Chat input
    if prompt := st.chat_input("Enter your prompt here"):
        with st.chat_message("user"):
            st.markdown(prompt)
        
        st.session_state.messages.append({"role": "user", "content": prompt})
        
        if uploaded_file is not None:
            with st.spinner('Processing...'):
                messages_history = [{"role": m["role"], "content": m["content"]} 
                                 for m in st.session_state.messages[:-1]]
                response = process_image_and_text(image, prompt, messages_history)
                
                with st.chat_message("assistant"):
                    st.markdown(response)
                
                st.session_state.messages.append({"role": "assistant", "content": response})
        else:
            st.error("Please upload an image")
    
    st.markdown("---")
    st.markdown(
        """
        <div style='text-align: center'>
            <p>Using Ollama Vision Model for Image Analysis</p>
        </div>
        """, 
        unsafe_allow_html=True
    )

if __name__ == "__main__":
    main()