import streamlit as st from llama_cpp import Llama # Load the model once per session if 'llm' not in st.session_state: st.session_state.llm = Llama.from_pretrained( repo_id="Divyansh12/check", filename="unsloth.F16.gguf", verbose=True, n_ctx=32768, n_threads=2, chat_format="chatml" ) # Define the function to get responses from the model def respond(message, history): messages = [] for user_message, assistant_message in history: if user_message: messages.append({"role": "user", "content": user_message}) if assistant_message: messages.append({"role": "assistant", "content": assistant_message}) messages.append({"role": "user", "content": message}) response = "" # Stream the response from the model response_stream = st.session_state.llm.create_chat_completion( messages=messages, stream=True, max_tokens=512, # Use a default value for simplicity temperature=0.7, # Use a default value for simplicity top_p=0.95 # Use a default value for simplicity ) # Collect the response chunks for chunk in response_stream: if len(chunk['choices'][0]["delta"]) != 0 and "content" in chunk['choices'][0]["delta"]: response += chunk['choices'][0]["delta"]["content"] return response # Return the full response # Streamlit UI st.title("Simple Chatbot") st.write("### Interact with the chatbot!") # User input field user_message = st.text_area("Your Message:", "") # Chat history if 'history' not in st.session_state: st.session_state.history = [] # Button to send the message if st.button("Send"): if user_message: # Check if user has entered a message # Get the response from the model response = respond(user_message, st.session_state.history) # Add user message and model response to history st.session_state.history.append((user_message, response)) # Clear the input field after sending user_message = "" # Reset user_message to clear input # Display the chat history st.write("### Chat History") for user_msg, assistant_msg in st.session_state.history: st.write(f"**User:** {user_msg}") st.write(f"**Assistant:** {assistant_msg}")