initial upload commit

upload 7 files including app, prediction script, eda script, keras model, image resources, and requirements.

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+output_eda_train.png filter=lfs diff=lfs merge=lfs -text
+output_eda_valid.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,10 @@
+import streamlit as st
+import eda
+import prediction
+
+navigation = st.sidebar.selectbox('Move me to this page:', ['Exploratory Data Analysis', 'Model Prediction'])
+
+if navigation == 'Exploratory Data Analysis':
+    eda.run()
+else:
+    prediction.run()

eda.py

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+import streamlit as st
+
+def run():
+    st.image('https://static.vecteezy.com/system/resources/thumbnails/025/868/984/small/freshy-various-fruits-for-summer-background-summer-festive-time-concept-generative-ai-free-photo.jpeg', use_column_width=True)
+    st.markdown('# Fruit Classification Using Artificial Neural Network Modeling')
+    st.markdown('This is a fun project to create a model that lets the computer predict a given image and classify it into any predetermined class upon this model development.')
+    st.markdown('This model allows you to upload and predict images into any of these classes:\n- Apple\n- Banana\n- Grape\n- Mango\n- Strawberry')
+    st.markdown('Image credit: Vecteezy')
+    st.markdown('---')
+    
+    st.markdown('# Exploratory Data Analysis')
+    st.markdown('This section provides a brief introduction to the dataset used for model construction in the background process.')
+    st.markdown('---')
+    st.markdown('## Link to the Main Dataset')
+    st.markdown('You can obtain the dataset via this [link](https://www.kaggle.com/datasets/utkarshsaxenadn/fruits-classification/download?datasetVersionNumber=1)')
+    st.markdown('---')
+    st.markdown('## Sample Images from Train-set')
+    st.markdown('Here are several images used for model training:')
+    st.image('output_eda_train.png')
+    st.markdown('Description: The training dataset includes three types of images:\n- Straightforward images focusing on a single fruit\n- Fruits with relevant backgrounds\n- Fruits with context and zoomed-out backgrounds, showing a wider environment')
+    st.markdown('---')
+    st.markdown('## Sample Images from Validation-set')
+    st.markdown('Here are several images used for model validation:')
+    st.image('output_eda_valid.png')
+    st.markdown('Description: The validation dataset includes three types of images, similar to those in the training set. Images in the validation set inherit characteristics from the training set.')
+
+    st.markdown('---')
+    st.markdown('Managed by: Nicku R. Perdana | [LinkedIn](https://www.linkedin.com/in/nickurendyperdana/) | [Github](https://github.com/nickuperdana)')
+    
+if __name__ == '__main__':
+    run()

prediction.py

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+import streamlit as st
+import requests
+import numpy as np
+import cv2
+import tensorflow as tf
+
+model = tf.keras.models.load_model('cnn_model.keras')
+
+def run():
+    st.image('https://static.vecteezy.com/system/resources/thumbnails/025/868/984/small/freshy-various-fruits-for-summer-background-summer-festive-time-concept-generative-ai-free-photo.jpeg', use_column_width=True)
+    st.markdown('# Fruit Classification Using Artificial Neural Network Modeling')
+    st.markdown('This is a fun project to create a model that lets the computer predict a given image and classify it into any predetermined class upon this model development.')
+    st.markdown('This model allows you to upload and predict images into any of these classes:\n- Apple\n- Banana\n- Grape\n- Mango\n- Strawberry')
+    st.markdown('Image credit: Vecteezy')
+    st.markdown('---')
+
+    st.markdown("# Let's Predict An Image!")
+
+    st.markdown('### Upload an Image.')
+    uploadMethod = st.selectbox('Before we predict an image, choose an upload method first', ['Upload image', 'Send image URL'], index=None, placeholder='Select method...')
+    
+    try:  
+        with st.form('MyForm'):
+            if uploadMethod == 'Upload image':
+                imgUpload = st.file_uploader('Upload your file here', type=['png', 'jpg', 'webp', 'jpeg'], accept_multiple_files=False)
+                submit = st.form_submit_button('Start Predicting')
+            elif uploadMethod == 'Send image URL':
+                imgUrl = st.text_input('Enter URL', value=None, placeholder='URL...')
+                # if inputUrl != None:
+                #     st.markdown('Theploaded image:')
+                #     st.image(inputUrl)
+                submit = st.form_submit_button('Start Predicting')
+
+        class_list = ['Apple', 'Banana', 'Grape', 'Melon', 'Strawberry']
+
+        st.markdown('## Prediction Result:')
+        if submit:
+            if uploadMethod == 'Upload image':
+                inf_img = cv2.imdecode(np.fromstring(imgUpload.read(), np.uint8), 1)
+                inf_img_col = cv2.cvtColor(inf_img, cv2.COLOR_BGR2RGB)
+                inf_prep_img = cv2.resize(inf_img_col, (400,400))
+                inf_scal_img = inf_prep_img / 255.0
+                inf_reshape = np.reshape(inf_scal_img, [1,400,400,3])
+                class_img = model.predict(inf_reshape, verbose=0)
+                confident_index = np.argmax(class_img)
+                class_label = class_list[confident_index]
+                listproba = list(class_img[0])
+                st.markdown('You uploaded this image:')
+                st.image(imgUpload, use_column_width=True)
+                st.markdown(f'The model predicted the image given as a class of {class_label} with a probability of {listproba[confident_index]}.')
+                st.markdown('Due to the current state of prediction accuracy, a significant occurence of misidentification is expected.')
+            elif uploadMethod == 'Send image URL':
+                if imgUrl == "":
+                    pass
+                else:
+                    try:
+                        response = requests.get(imgUrl)
+                        if response.status_code == 200:
+                            inf_url_img = cv2.imdecode(np.frombuffer(response.content, np.uint8), 1)
+                            inf_img_url_col = cv2.cvtColor(inf_url_img, cv2.COLOR_RGB2BGR)
+                            inf_prep_url_img = cv2.resize(inf_img_url_col, (400,400))
+                            inf_scal_url_img = inf_prep_url_img / 255.0
+                            inf_url_reshape = np.reshape(inf_scal_url_img, [1,400,400,3])
+                            class_url_img = model.predict(inf_url_reshape, verbose=0)
+                            confident_index_url = np.argmax(class_url_img)
+                            class_url_label = class_list[confident_index_url]
+                            listproba_url = list(class_url_img[0])
+                            st.markdown('You uploaded this image:')
+                            st.image(inf_img_url_col, use_column_width=True)
+                            st.markdown(f'The model predicted the image given as a class of {class_url_label} with a probability of {listproba_url[confident_index_url]}.')
+                            st.markdown('Due to the current state of prediction accuracy, a significant occurence of misidentification is expected.')
+                            
+                        else:
+                            st.markdown("Unable to fetch image from the given URL. Please try again")
+                            pass
+                    except:
+                        pass
+    except:
+        pass
+
+if __name__ == '__main__':
+    run()

requirements.txt

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+tensorflow==2.15.0
+streamlit
+requests
+numpy
+cv2
+matplotlib
+pandas
+seaborn
+keras