Create app.py

app.py

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+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"<span style='font-size: 24px; color: {random.choice(['#FF9800', '#FF5722', '#673AB7', '#009688'])};'>Predicted class: <strong>{predicted_class_label}</strong></span>"
+        acc = f"<span style='font-size: 24px; color: {random.choice(['#FF9800', '#FF5722', '#673AB7', '#009688'])};'>Accuracy: <strong>{accuracy*100:.02f}%</strong></span>"
+        return output + "<br>" + 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)