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07f5824 cb9e673 07f5824 cb9e673 07f5824 cb9e673 07f5824 cb9e673 07f5824 28de25d 07f5824 28de25d 07f5824 28de25d 07f5824 cb9e673 28de25d cb9e673 28de25d 07f5824 cb9e673 07f5824 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load model and tokenizer from Hugging Face Hub
@st.cache_resource
def load_model():
model_name = "sshleifer/tiny-gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()
return tokenizer, model, device
tokenizer, model, device = load_model()
# Initialize chat history in session
if "messages" not in st.session_state:
st.session_state.messages = []
# Chat UI
st.title("🤖 Tiny GPT-2 Chatbot")
st.markdown("Ask me anything! This bot runs locally with no API key.")
# Display chat history
for msg in st.session_state.messages:
role = "🧑💻" if msg["role"] == "user" else "🤖"
st.markdown(f"**{role}:** {msg['content']}")
# Input box
user_input = st.text_input("Type your message...", key="user_input")
if user_input:
# Append user message
st.session_state.messages.append({"role": "user", "content": user_input})
# Build prompt
prompt = "\n".join([m["content"] for m in st.session_state.messages])
# Tokenize and generate
inputs = tokenizer(prompt, return_tensors="pt").to(device)
outputs = model.generate(
**inputs,
max_new_tokens=100,
temperature=0.7,
top_p=0.95,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
reply = output_text[len(prompt):].strip().split("\n")[0]
# Append bot reply
st.session_state.messages.append({"role": "assistant", "content": reply})
# Refresh the page to show new message
st.experimental_rerun()
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