app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
from huggingface_hub import login

# Replace 'your_token_here' with your actual Hugging Face token


# Load the model and tokenizer
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

# Set up the Streamlit app
st.title("Chat with LLaMA")
st.write("Enter your message below:")

# Create a text input for user message
user_input = st.text_input("Your Message:")

# Initialize a session state to keep track of the conversation
if 'conversation' not in st.session_state:
    st.session_state.conversation = []

if st.button("Send"):
    if user_input:
        # Append user input to the conversation history
        st.session_state.conversation.append({"role": "user", "content": user_input})

        # Create input for the model
        conversation_history = "\n".join(
            [f"{msg['role']}: {msg['content']}" for msg in st.session_state.conversation]
        )

        # Tokenize the input
        input_ids = tokenizer(conversation_history, return_tensors="pt").input_ids

        # Generate a response
        with torch.no_grad():
            output = model.generate(input_ids, max_new_tokens=100, do_sample=True)
        
        # Decode the generated response
        response = tokenizer.decode(output[0], skip_special_tokens=True)
        
        # Append model response to the conversation
        st.session_state.conversation.append({"role": "assistant", "content": response})

        # Display the conversation
        for msg in st.session_state.conversation:
            if msg['role'] == 'user':
                st.write(f"**User:** {msg['content']}")
            else:
                st.write(f"**Assistant:** {msg['content']}")

# Optional: Clear the conversation
if st.button("Clear Conversation"):
    st.session_state.conversation = []