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import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# --- Fix Streamlit config issue ---
st.set_page_config(
page_title="Natural Reasoning Bot",
page_icon="🤖",
layout="centered"
)
st.title("🤖 Natural Reasoning Bot")
st.markdown("Ask science questions and get answers from your fine-tuned model.")
# --- Sidebar for parameters ---
st.sidebar.header("⚙️ Generation Settings")
temperature = st.sidebar.slider("Temperature", 0.0, 1.5, 1.0, 0.1)
top_k = st.sidebar.slider("Top-k", 0, 100, 50, 5)
top_p = st.sidebar.slider("Top-p", 0.0, 1.0, 0.95, 0.05)
# --- Load model and tokenizer ---
@st.cache_resource(show_spinner=False)
def load_model():
model = AutoModelForCausalLM.from_pretrained("./my_bot_model")
tokenizer = AutoTokenizer.from_pretrained("./my_bot_model")
return model, tokenizer
model, tokenizer = load_model()
# --- Text Input ---
question = st.text_area("🧠 Enter your science question:", height=100)
generate_btn = st.button("🔍 Generate Answer")
# --- Inference Logic ---
if generate_btn and question:
input_text = f"### Question: {question}\n### Answer:"
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
model.eval()
with torch.no_grad():
output = model.generate(
**inputs,
max_length=256,
do_sample=True,
top_p=top_p,
top_k=top_k,
temperature=temperature,
pad_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(output[0], skip_special_tokens=True)
answer = response.replace(input_text, "").strip()
st.markdown("---")
st.subheader("📤 Model Answer")
st.success(answer)
elif generate_btn:
st.warning("Please enter a question to get an answer.")
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