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import streamlit as st
from llama_cpp import Llama
from PIL import Image
import numpy as np
from transformers import AutoModel
model = AutoModel.from_pretrained("liminerity/bitmap-mistral-M7-slerp-alpaca-70m-gguf")
# Initialize the model (only once)
@st.cache_resource
def load_model():
return Llama(
model_path=model,
n_ctx=2048,
n_threads=4
)
st.title("BitMap Generator")
# Input text prompt
prompt = st.text_input("Enter your prompt for the bitmap:", "A simple circle")
if st.button("Generate Bitmap"):
if prompt:
# Generate the bitmap description
llm = load_model()
response = llm(
f"Generate a 64x64 bitmap array using 0s and 1s that represents {prompt}. "
"Only output the array, no other text.",
max_tokens=2048,
temperature=0.7
)
# Convert the response to a bitmap
try:
# Parse the response to get just the array
array_text = response['choices'][0]['text'].strip()
# Convert string to numpy array
bitmap = np.array([list(map(int, row.strip('[] ').split()))
for row in array_text.split('\n') if row.strip()])
# Scale up the bitmap for better visibility
scaled_bitmap = np.kron(bitmap, np.ones((10, 10)))
# Create an image from the array
img = Image.fromarray(np.uint8(scaled_bitmap * 255))
# Display the image
st.image(img, caption=f"Generated bitmap for: {prompt}")
except Exception as e:
st.error(f"Error creating bitmap: {str(e)}")
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