Spaces:
Running
Running
File size: 3,324 Bytes
235bed0 a481416 235bed0 06308c8 235bed0 06308c8 235bed0 06308c8 235bed0 06308c8 235bed0 06308c8 235bed0 06308c8 235bed0 |
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 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
import streamlit as st
import tensorflow as tf
import numpy as np
from PIL import Image
import io
import logging
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Load the pre-trained MNIST model
@st.cache_resource
def load_model():
try:
model = tf.keras.models.load_model('mnist_cnn.h5')
logger.info("MNIST model loaded successfully")
return model
except Exception as e:
logger.error(f"Error loading model: {e}")
st.error("Failed to load the model. Please check the model file.")
return None
# Preprocess the uploaded image
def preprocess_image(image):
try:
# Convert to grayscale
img = image.convert('L')
# Resize to 28x28 (MNIST model input size)
img = img.resize((28, 28), Image.Resampling.LANCZOS)
# Convert to numpy array and normalize
img_array = np.array(img)
# Ensure the image is inverted if necessary (MNIST expects white digits on black background)
img_array = 255 - img_array # Invert colors
img_array = img_array / 255.0 # Normalize to [0, 1]
# Reshape for model input (1, 28, 28, 1)
img_array = img_array.reshape(1, 28, 28, 1)
logger.info("Image preprocessed successfully")
return img_array
except Exception as e:
logger.error(f"Error preprocessing image: {e}")
st.error("Failed to preprocess the image. Please ensure it's a valid image.")
return None
# Streamlit app
st.title("AutoWeightLogger - Number Detection")
st.write("Upload an image containing a single handwritten digit to detect the number.")
# File uploader
uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])
if uploaded_file is not None:
try:
# Display the uploaded image
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_column_width=True)
# Preprocess the image
processed_image = preprocess_image(image)
if processed_image is None:
st.stop()
# Load the model
model = load_model()
if model is None:
st.stop()
# Make prediction
with st.spinner("Detecting number..."):
prediction = model.predict(processed_image)
predicted_digit = np.argmax(prediction, axis=1)[0]
confidence = np.max(prediction) * 100
# Display result
st.success(f"Detected Number: {predicted_digit}")
st.write(f"Confidence: {confidence:.2f}%")
# Provide feedback if confidence is low
if confidence < 70:
st.warning("Low confidence in prediction. Please ensure the image contains a clear, single handwritten digit.")
except Exception as e:
logger.error(f"Error processing image: {e}")
st.error("An error occurred while processing the image. Please try again with a different image.")
else:
st.info("Please upload an image to proceed.")
# Instructions for users
st.markdown("""
### Instructions
1. Upload an image containing a single handwritten digit (0-9).
2. Ensure the digit is clear, centered, and on a plain background for best results.
3. The model expects white digits on a black background, similar to MNIST dataset images.
""") |