import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
@st.cache_resource
def load_model_and_tokenizer():
    model_name = "microsoft/DialoGPT-medium"  # Replace with your chosen model
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name)
    return tokenizer, model

tokenizer, model = load_model_and_tokenizer()

# Streamlit App
st.title("General Chatbot")
st.write("A chatbot powered by an open-source model from Hugging Face.")

# Initialize the conversation
if "conversation_history" not in st.session_state:
    st.session_state["conversation_history"] = []

# Input box for user query
user_input = st.text_input("You:", placeholder="Ask me anything...", key="user_input")

if st.button("Send") and user_input:
    # Append user input to history
    st.session_state["conversation_history"].append({"role": "user", "content": user_input})
    
    # Tokenize and generate response
    input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt")
    chat_history_ids = model.generate(input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
    response = tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)

    # Append model response to history
    st.session_state["conversation_history"].append({"role": "assistant", "content": response})

# Display the conversation
for message in st.session_state["conversation_history"]:
    if message["role"] == "user":
        st.write(f"**You:** {message['content']}")
    else:
        st.write(f"**Bot:** {message['content']}")