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import numpy as np
import streamlit as st
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import os
# Load the model and tokenizer
model_name = "EleutherAI/gpt-neo-125m"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Random dog images for error messages
random_dog = [
"0f476473-2d8b-415e-b944-483768418a95.jpg",
"1bd75c81-f1d7-4e55-9310-a27595fa8762.jpg",
# Add more images as needed
]
def reset_conversation():
'''Resets conversation'''
st.session_state.conversation = []
st.session_state.messages = []
return None
# Create sidebar controls
temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, 0.5)
max_token_value = st.sidebar.slider('Select a max_token value', 1000, 9000, 5000)
st.sidebar.button('Reset Chat', on_click=reset_conversation)
# Set the model and display its name
st.sidebar.write(f"You're now chatting with **{model_name}**")
st.sidebar.markdown("*Generated content may be inaccurate or false.*")
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Accept user input
if prompt := st.chat_input(f"Hi, I'm {model_name}, ask me a question"):
with st.chat_message("user"):
st.markdown(prompt)
st.session_state.messages.append({"role": "user", "content": prompt})
# Display assistant response
with st.chat_message("assistant"):
try:
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
inputs.input_ids,
max_new_tokens=max_token_value,
temperature=temp_values,
do_sample=True
)
assistant_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
except Exception as e:
assistant_response = "šµāš« Connection issue! Try again later. Here's a š¶:"
st.image(f'https://random.dog/{random_dog[np.random.randint(len(random_dog))]}')
st.write("Error message:")
st.write(e)
st.markdown(assistant_response)
st.session_state.messages.append({"role": "assistant", "content": assistant_response})
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