Update app.py

app.py

@@ -1,7 +1,48 @@
-# Load model directly
 import streamlit as st
-from transformers import AutoModel
-model = AutoModel.from_pretrained("liminerity/bitmap-mistral-M7-slerp-alpaca-70m-gguf")
+from llama_cpp import Llama
+from PIL import Image
+import numpy as np
+
+# Initialize the model (only once)
+@st.cache_resource
+def load_model():
+    return Llama(
+        model_path="models/bitmap-mistral-M7-slerp-alpaca-70m.gguf",
+        n_ctx=2048,
+        n_threads=4
+    )
 
 st.title("BitMap Generator")
-st.text(model)
+
+# 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 32x32 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)}")