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import streamlit as st | |
from transformers import AutoModelForCausalLM, AutoTokenizer | |
# Load pre-trained model and tokenizer from Hugging Face | |
model_name = "gpt2" # You can use other models like "gpt-neo", "gpt-3", etc. | |
model = AutoModelForCausalLM.from_pretrained(model_name) | |
tokenizer = AutoTokenizer.from_pretrained(model_name) | |
# Title of the app | |
st.title("LLM Chatbot") | |
# User input for chatbot | |
user_input = st.text_input("You: ", "") | |
# Function to generate the response using the model | |
def generate_response(prompt): | |
inputs = tokenizer(prompt, return_tensors="pt") | |
outputs = model.generate(inputs.input_ids, max_length=150, num_return_sequences=1) | |
response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
return response | |
if user_input: | |
# Generate a response from the model | |
response = generate_response(user_input) | |
# Display only the bot's response | |
st.write(f"Bot: {response}") | |