app.py

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

# Load the model and tokenizer
model_name = "m3rg-iitd/llamat-3-chat" #"gpt2"  # You can replace this with any model of your choice
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

st.title("Chatbot with LlaMat")
st.write("Ask me anything about material!")

# Initialize session state for chat history
if "messages" not in st.session_state:
    st.session_state.messages = []

# Function to generate response
def generate_response(prompt):
    inputs = tokenizer.encode(prompt, return_tensors="pt")
    outputs = model.generate(inputs, max_length=100, num_return_sequences=1)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# User input
user_input = st.text_input("You: ", "")

if user_input:
    st.session_state.messages.append({"role": "user", "content": user_input})
    response = generate_response(user_input)
    st.session_state.messages.append({"role": "bot", "content": response})

# Display chat history
for message in st.session_state.messages:
    if message["role"] == "user":
        st.write(f"You: {message['content']}")
    else:
        st.write(f"Bot: {message['content']}")