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app.py
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app.py
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import streamlit as st # Importing Streamlit library for creating the app interface
import tensorflow as tf # Importing TensorFlow for using deep learning functionalities
import numpy as np # Importing NumPy for numerical operations
from PIL import Image # Importing Image module from PIL library for image processing
# Function to load the pre-trained model and cache it for optimized performance
@st.cache(allow_output_mutation=True)
def load_model():
model = tf.keras.models.load_model('flower_model_trained.hdf5') # Loading the pre-trained model
return model # Returning the loaded model
# Function to predict the class of the input image using the loaded model
def predict_class(image, model):
image = tf.cast(image, tf.float32) # Converting the image data to float32 datatype
image = tf.image.resize(image, [180, 180]) # Resizing the input image to match the model's input shape
image = np.expand_dims(image, axis=0) # Adding an extra dimension to match the model's input requirements
prediction = model.predict(image) # Making predictions using the model
return prediction # Returning the predicted class probabilities
model = load_model() # Loading the pre-trained model
st.title('Flower Classifier') # Setting the title of the Streamlit app as 'Flower Classifier'
file = st.file_uploader("Upload an image of a flower", type=["jpg", "png"])
# Creating a file uploader component for uploading images of type JPG or PNG
if file is None:
st.text('Waiting for upload....')
# Displaying a message if no file is uploaded yet
else:
slot = st.empty()
slot.text('Running inference....')
# Displaying a message indicating the inference process is ongoing
test_image = Image.open(file)
# Opening the uploaded image using PIL's Image module
st.image(test_image, caption="Input Image", width=400)
# Displaying the uploaded image with a caption and width specified
pred = predict_class(np.asarray(test_image), model)
# Making predictions on the uploaded image using the loaded model
class_names = ['daisy', 'dandelion', 'rose', 'sunflower', 'tulip']
# Defining class names for different flower types
result = class_names[np.argmax(pred)]
# Determining the predicted class by selecting the one with the highest probability
output = 'The image is a ' + result
# Generating the output message indicating the predicted flower class
slot.text('Done')
# Displaying a message indicating the completion of inference
st.success(output)
# Displaying the output message as a success notification in the Streamlit app