import streamlit as st
import tensorflow.keras as keras
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
import random
model = load_model('model.h5')
# Define class labels
class_labels = ['Ahmedabad', 'Delhi', 'Kerala', 'Kolkata', 'Mumbai']
# Set the threshold for minimum accuracy
threshold = 0.3
# Create a function to process the uploaded image
def process_image(uploaded_image):
# Load and preprocess the input image
img = image.load_img(uploaded_image, target_size=(175, 175)) #150 for my model
img = image.img_to_array(img)
img = np.expand_dims(img, axis=0)
img = img / 255.0
# Make predictions on the input image
predictions = model.predict(img)
# Get the predicted class label and accuracy
predicted_class_index = np.argmax(predictions)
predicted_class_label = class_labels[predicted_class_index]
accuracy = predictions[0][predicted_class_index]
# Check if accuracy is below the threshold for all classes
if all(accuracy < threshold for accuracy in predictions[0]):
return "This location is not in our database."
else:
output = f"Predicted class: {predicted_class_label}"
acc = f"Accuracy: {accuracy*100:.02f}%"
return output + "
" + acc
# Set Streamlit app title
st.title("Location Classification")
# Add a file uploader to the app
uploaded_image = st.file_uploader("Upload an image (JPG or JPEG format)", type=["jpg", "jpeg"])
# Process the uploaded image and display the result
if uploaded_image is not None:
st.write("Uploaded image:")
st.image(uploaded_image, use_column_width=True)
# Convert the uploaded image to a file path
image_path = "./uploaded_image.jpg"
with open(image_path, "wb") as f:
f.write(uploaded_image.getvalue())
# Process the image and display the result
result = process_image(image_path)
st.markdown(result, unsafe_allow_html=True)