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app.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Mon Apr 17 08:43:48 2023
+
+@author: mritchey
+"""
+# import keras
+import streamlit as st
+from PIL import Image
+import pandas as pd
+import numpy as np
+
+model_type = st.sidebar.selectbox(
+    'Select Model', ('VGG16', 'VGG19', 'ResNet50V2', 'MobileNetV2'))
+models = {'VGG16': 'vgg16', 'VGG19': 'vgg16', 'ResNet50V2': 'resnet_v2',
+          'MobileNetV2': 'mobilenet_v2'}
+model_type2 = models[model_type]
+
+top_n = st.sidebar.selectbox('Number of Results', (3, 5, 10))
+
+exec(f'from keras.applications.{model_type2} import {model_type}')
+exec(
+    f'from keras.applications.{model_type2} import preprocess_input, decode_predictions')
+model = eval(f'{model_type}(weights="imagenet")')
+
+img_path = st.file_uploader("Upload Picture")
+
+
+img = Image.open(img_path)
+st.image(img)
+
+img = img.resize((224, 224))  # Resize to match VGG16 input size
+x = np.array(img)
+x = np.expand_dims(x, axis=0)
+x = preprocess_input(x)
+
+# Make predictions on the image
+preds = model.predict(x)
+# Convert the predictions to human-readable labels
+decoded_preds = decode_predictions(preds, top=top_n)[0]
+
+df = pd.DataFrame(decoded_preds)
+df.columns = ['label', 'Object', 'Percent Certainty']
+df.index = df.index+1
+df = df[['Object', 'Percent Certainty']]
+df['Percent Certainty'] = df['Percent Certainty'].apply(
+    lambda x: '{:.2%}'.format(x))
+
+st.dataframe(df)

requirements.txt

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+numpy
+pandas
+Pillow
+streamlit
+keras