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import os
os.system("pip install transformers==4.37.0")
os.system("pip install torch==2.0.1")
os.system("pip install accelerate")
import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer
# Set device
device = "cpu"
# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen1.5-0.5B-Chat",
torch_dtype="auto",
).to(device)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-0.5B-Chat")
# Create a chatbot interface
st.title("Chatbot")
st.write("Ask me anything!")
# Initialize messages
messages = [
{"role": "system", "content": "You are a helpful assistant."},
]
# Display chat history
for message in messages:
if message["role"] == "system":
st.write(f"*System*: {message['content']}")
elif message["role"] == "user":
st.write(f"*You*: {message['content']}")
elif message["role"] == "assistant":
st.write(f"*Assistant*: {message['content']}")
# Get user input
user_input = st.text_input("Your message")
print("received!")
# Generate response
if user_input:
messages.append({"role": "user", "content": user_input})
print("good!")
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
print("good!")
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
print("good!")
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print("good!")
messages.append({"role": "assistant", "content": response})
# Display response
st.write(f"*Assistant*: {response}")
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