src/streamlit_app.py

import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "openai/gpt-oss-20b"  # Replace with the actual GPT-OSS 20B model ID
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Set up the Streamlit app interface
st.title("GPT-OSS 20B Chatbot")
st.markdown("### Chat with GPT-OSS 20B. Ask your question below!")

# Create a conversation box
if 'history' not in st.session_state:
    st.session_state.history = []

# Function to display the conversation
def display_conversation():
    for message in st.session_state.history:
        if message['role'] == 'user':
            st.markdown(f"**You**: {message['text']}")
        else:
            st.markdown(f"**GPT-OSS 20B**: {message['text']}")

# Handle user input
user_input = st.text_input("Enter your prompt:")

if user_input:
    # Store user input in the session history
    st.session_state.history.append({"role": "user", "text": user_input})

    # Tokenize user input and generate response
    inputs = tokenizer(user_input, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=500)

    # Decode the model's response
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)

    # Store the model's response in the history
    st.session_state.history.append({"role": "gpt", "text": response})

    # Display updated conversation
    display_conversation()