app.py

import streamlit as st
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig

# Load model and tokenizer
model_name = "abooze/ft-deepseek-llm-7b-chat-dpo-pairs"
st.title("💬 RealMind AI")
st.markdown("Chat with RealMind AI!")

@st.cache_resource
def load_model():
    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        torch_dtype= torch.float32,
        device_map="auto",
        trust_remote_code=True
    )
    gen_config = GenerationConfig.from_pretrained(model_name)
    gen_config.pad_token_id = gen_config.eos_token_id
    return tokenizer, model, gen_config

tokenizer, model, gen_config = load_model()

# Session state to hold chat history
# if "messages" not in st.session_state:
#     st.session_state.messages = [
#         {"role": "system", "content": "You are a helpful assistant."}
#     ]

# # Display chat history
# for msg in st.session_state.messages:
#     if msg["role"] != "system":
#         st.chat_message(msg["role"]).write(msg["content"])

# Chat input
user_input = st.chat_input("Ask something...")
if user_input:
    # Add user input to message history
    st.session_state.messages = [{"role": "user", "content": user_input}]
    st.chat_message("user").write(user_input)

    with st.spinner("Generating response..."):
        input_ids = tokenizer.apply_chat_template(
            st.session_state.messages,
            return_tensors="pt",
            add_generation_prompt=True
        ).to(model.device)

        outputs = model.generate(
            input_ids=input_ids,
            max_new_tokens=512,
            temperature=0.7,
            top_p=0.95,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id
        )

        # Decode and extract response
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        # Extract only the assistant's last reply
        assistant_reply = response.split("<|assistant|>\n")[-1].strip()

    st.chat_message("assistant").write(assistant_reply)
    st.session_state.messages.append({"role": "assistant", "content": assistant_reply})