deepseek_app.py

# import streamlit as st
# import ollama

# desired_model = "deepseek-r1:1.5b"

# st.title = "GoldenBrain Chat Based On DeepSeek"

# def generate_response(question):
#     response = ollama.chat(desired_model,
#                            messages=[
#                                {"role": "user",
#                                 "content": question}
#                            ])
#     st.info(response['message']["content"])
    
    
# with st.form(key='my_form'):
#     text = st.text_area("Enter your question here and click on the submit button")
#     submit_button = st.form_submit_button(label='Submit')
#     if submit_button:
#         generate_response(text)


import streamlit as st
import ollama

# Set the desired model
desired_model = "deepseek-r1:1.5b"

# Set the title of the Streamlit app
st.title=("GoldenBrain Chat Based On DeepSeek")  # Fixed: Use st.title() correctly

# Initialize session state to store the conversation history
if "conversation" not in st.session_state:
    st.session_state.conversation = []

# Initialize session state for dark/light mode
if "dark_mode" not in st.session_state:
    st.session_state.dark_mode = False

# Function to toggle dark/light mode
def toggle_dark_mode():
    st.session_state.dark_mode = not st.session_state.dark_mode
    # Inform the user that themes cannot be toggled dynamically
    st.warning("Dynamic theme switching is not supported by Streamlit. Please configure the theme in the Streamlit settings.")

# Add a button to toggle dark/light mode
st.button("Toggle Dark/Light Mode", on_click=toggle_dark_mode)

# Function to stream the response from the model
def stream_response(question):
    response = ollama.chat(
        desired_model,
        messages=[{"role": "user", "content": question}],
        stream=True  # Enable streaming
    )
    full_response = ""
    placeholder = st.empty()  # Placeholder to stream the response
    for chunk in response:
        chunk_content = chunk['message']['content']
        full_response += chunk_content
        placeholder.markdown(f"**GoldenBrain:** {full_response}")  # Update the placeholder with the latest response
    return full_response

# Display the conversation history
for message in st.session_state.conversation:
    with st.chat_message(message["role"]):  # Use st.chat_message for better chat UI
        st.markdown(message["content"])

# Input for a new question
if prompt := st.chat_input("Enter your question here"):
    # Add the user's question to the conversation history
    st.session_state.conversation.append({"role": "user", "content": prompt})
    
    # Display the user's question
    with st.chat_message("user"):
        st.markdown(prompt)
    
    # Stream the response from the model
    with st.chat_message("assistant"):
        response = stream_response(prompt)
        st.markdown(response)  # Display the response only once
    
    # Add the model's response to the conversation history
    # st.session_state.conversation.append({"role": "assistant", "content": response})

# JavaScript to auto-scroll to the bottom of the page
st.markdown(
    """
    <script>
    window.addEventListener('load', function() {
        window.scrollTo(0, document.body.scrollHeight);
    });
    </script>
    """,
    unsafe_allow_html=True
)