Delete app.py

app.py

@@ -1,60 +0,0 @@
-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)