Duplicate from loxzdigital/Model-IO-Space

Co-authored-by: Andy Lau <laumiulun@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz 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

.streamlit/config.toml

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+[theme]
+base="dark"
+#primaryColor="#a40303"
+#backgroundColor="#FFF"
+#textColor="#ffffff"
+#secondaryBackgroundColor="#126072"
+
+[browser]
+gatherUsageStats = false

README.md

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+---
+title: Model IO Space
+emoji: ✉️
+colorFrom: pink
+colorTo: yellow
+sdk: streamlit
+sdk_version: 1.10.0
+app_file: app.py
+pinned: false
+duplicated_from: loxzdigital/Model-IO-Space
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import streamlit as st
+import numpy as np
+import pandas as pd
+import PIL
+import torch
+# import streamlit_analytics
+import torchvision.transforms as transforms
+
+import pickle
+
+# AWS 
+import boto3
+import botocore
+from botocore import UNSIGNED
+from botocore.config import Config
+
+# Plotly and Bokeh
+import plotly.graph_objects as go
+from bokeh.models.widgets import Div
+
+
+def convert_percentage(score):
+    rounded_probability = str(np.round(score*100,2)) + "%"
+    return rounded_probability
+
+def url_button(button_name,url):
+    if st.button(button_name):
+        js = """window.open('{url}')""".format(url=url) # New tab or window
+        html = '<img src onerror="{}">'.format(js)
+        div = Div(text=html)
+        st.bokeh_chart(div)
+
+def table_data():
+    # creating table data
+    field = [
+        'Data Scientist',
+        'Dataset',
+        'Algorithm',
+        'Framework',
+        'Ensemble',
+        'Domain',
+        'Model Size'
+    ]
+
+    data = [
+        'Andy Lau',
+        'Stanford Cars Dataset',
+        'Deep Learning Convolutional Neural Network: ResNet50',
+        'Pytorch',
+        'XGBoost',
+        'ResNet Image Classification',
+        '76.55 KB'
+    ]
+
+    data = {
+        'Field':field,
+        'Data':data
+    }
+
+    df = pd.DataFrame.from_dict(data)
+
+    return df
+
+
+def create_box(text,label):
+    st.markdown(f'<p style="background-color:#d2e4f6;padding: 5px 5px;border-radius:10px;font-size:24px;"><center><b>{text}</b>: {label}</center></p>', unsafe_allow_html=True)
+
+def create_table():
+    # creating table data
+    field = [
+        'Data Scientist',
+        'Dataset',
+        'Algorithm',
+        'Framework',
+        'Ensemble',
+        'Domain',
+        'Model Size'
+    ]
+
+    data = [
+        'Andy Lau',
+        'Stanford Cars Dataset',
+        'Deep Learning Convolutional Neural Network: ResNet50',
+        'Pytorch',
+        'XGBoost',
+        'ResNet Image Classification',
+        '76.55 KB'
+    ]
+
+    data = {
+        'Field':field,
+        'Data':data
+    }
+
+    df = pd.DataFrame.from_dict(data)
+
+
+    header_color = ['#0f4d60','#1c8d99']
+    cell_color = ['rgba(15,77,96,0.25)','rgba(28,141,153,0.33)']
+
+    # Create figures  
+    fig = go.Figure(data=[go.Table(
+        header=dict(values=list(df.columns),
+                    fill_color=header_color,
+                    font=dict(color='white', size=15),
+                    align='left'),
+        cells=dict(values=[df.Field, df.Data],
+                fill_color=header_color,
+                font=dict(color='white', size=15),
+                align='left'))
+    ])
+    # Make the header dissapear 
+    fig.for_each_trace(lambda t: t.update(header_fill_color = 'rgba(0,0,0,0)'))
+
+    return fig
+
+
+class SaveFeatures():
+    features=None
+    def __init__(self, m): 
+        self.hook = m.register_forward_hook(self.hook_fn)
+        self.features = None
+    def hook_fn(self, module, input, output): 
+        out = output.detach().cpu().numpy()
+        if isinstance(self.features, type(None)):
+            self.features = out
+        else:
+            self.features = np.row_stack((self.features, out))
+    def remove(self): 
+        self.hook.remove()
+
+
+def read_image_from_s3(bucket, key):
+    """Load image file from s3.
+
+    Parameters
+    ----------
+    bucket: string
+        Bucket name
+    key : string
+        Path in s3
+
+    Returns
+    -------
+    np array
+        Image array
+    """
+    s3 = boto3.resource('s3',config=Config(signature_version=UNSIGNED))
+    bucket = s3.Bucket(bucket)
+    object = bucket.Object(key)
+    response = object.get()
+    file_stream = response['Body']
+    im = PIL.Image.open(file_stream).convert('RGB')
+    return im
+
+
+
+# ---- Title Screen -----------
+
+def add_bg_from_url():
+    st.markdown(
+         f"""
+         <style>
+         .stApp {{
+             background-image: linear-gradient(#0A3144,#126072,#1C8D99);
+             background-attachment: fixed;
+             background-size: cover;
+             color: white;
+         }}
+         </style>
+         """,
+         unsafe_allow_html=True
+     )
+
+
+st.set_page_config(layout="wide")
+if 'user_counts' not in st.session_state:
+    st.session_state['user_counts'] = 0
+
+# add_bg_from_url() 
+
+# st.session_state.user_counts +=1     # Increase usercounter
+
+
+# col1, col2 = st.columns([10,1])
+# with col1:
+# st.markdown("""
+# <style>
+# .big-font {
+#     font-size:50px !important;
+# }
+# </style>
+# """, unsafe_allow_html=True)
+# st.markdown('<p class="big-font">Image Optimization: Email Industry</p>', unsafe_allow_html=True)
+st.markdown('# Image Optimization: Email Industry')
+
+# with col2:
+#     st.write(st.session_state.user_counts)
+
+# image = Image.Open('figures/ModelIO.png')
+
+# col1, col2, col3 = st.columns([1,1,1])
+
+# with col2:
+#     img = PIL.Image.open('figures/IO.png')
+#     st.image(img)
+# with col2:
+    # html3 = f"""
+    #         <div class="total-dc"">
+    #             <p>Total DC: £<p>
+    #             <p>TEST<p>
+    #         </div>
+
+    #         """
+    # st.markdown(html3, unsafe_allow_html=True)
+    # st.markdown('#### Data Scientist')
+
+stats_col1, stats_col2, stats_col3, stats_col4 = st.columns([1,1,1,1])
+
+# with stats_col1:
+#     # st.markdown(' **Production**: Ready',unsafe_allow_html=True)
+#     create_box('Production','Ready')
+# with stats_col2:
+#     create_box('Accuracy','91%')
+# with stats_col3:
+#     create_box('Speed','2.18 ms')
+# with stats_col4:
+#     # st.markdown(' **Industry**: Email Marketing')
+#     create_box('Industry','Email Marketing')
+
+# st.markdown("""
+# <style>
+# div[data-testid="metric-container"] {
+#    background-color: rgba(28, 131, 225, 0.1);
+#    border: 1px solid rgba(28, 131, 225, 0.1);
+#    padding: 5% 5% 5% 10%;
+#    border-radius: 5px;
+#    color: rgb(30, 103, 119);
+#    overflow-wrap: break-word;
+# }
+
+# /* breakline for metric text         */
+# div[data-testid="metric-container"] > label[data-testid="stMetricLabel"] > div {
+#    overflow-wrap: break-word;
+#    white-space: break-spaces;
+#    color: red;
+# }
+# </style>
+# """
+# , unsafe_allow_html=True)
+with stats_col1:
+    st.metric(label="Production", value="Ready")
+with stats_col2:
+    st.metric(label="Accuracy", value="91%")
+
+with stats_col3:
+    st.metric(label="Speed", value="2.18 ms")
+
+with stats_col4:
+    st.metric(label="Industry", value="Email")
+
+
+# ---- Model Information -----------
+# info_col1, info_col2, info_col3 = st.columns([1,1,1])
+with st.sidebar:
+    with st.expander('Model Description', expanded=False):
+        img = PIL.Image.open('figures/IO.png')
+        st.image(img)
+        st.markdown('Adding an image to an email campaign that will provide optimal engagement metrics can be challenging. How do you know which image to upload to your HTML, that will make an impact or significantly move the needle? And why would this image garner the best engagement? This model seeks to help campaign engineers understand which images affect their user engagement rate the most. The specific model is implemented using ResNet 18 and ResNet 34 for image embeddings extraction, and then we used these image embeddings as further inputs into a Gradient Boosted Tree model to generate probabilities on a user-specified target variable. The base model was adapted to car images and accurately predicted the user engagement rates with 91% accuracy. This model is adaptable for any large-scale marketing campaign using images. This model will identify the best images for optimal engagement for an email marketing campaign and serve engagement metrics prior to campaign launch. The model serves up several different images in milliseconds, so the campaign engineer understands which image to select in the campaign for optimized engagement.')
+
+    with st.expander('Model Information', expanded=False):
+        hide_table_row_index = """
+            <style>
+            thead tr th:first-child {display:none}
+            tbody th {display:none}
+            </style>
+            """
+        st.markdown(hide_table_row_index, unsafe_allow_html=True)
+        st.table(table_data())
+
+    url_button('Model Homepage','https://www.loxz.com/#/models/IO')
+    url_button('Full Report','https://resources.loxz.com/reports/image-optimization-model')
+    url_button('Amazon Market Place','https://aws.amazon.com/marketplace')
+
+
+
+
+uploaded_file = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
+
+if uploaded_file is not None:
+    upload_img = PIL.Image.open(uploaded_file)
+else:
+    upload_img = None
+
+
+# Drop down menu
+target_variables = ['Open Rate',
+                    'Click Through Open Rate',
+                    'Revenue Generated per Email',
+                    'Conversion Rate']
+campaign_types = ['Abandoned Cart',
+                  'Newsletter',
+                  'Promotional',
+                  'Survey',
+                  'Transactional',
+                  'Webinar',
+                  'Engagement', 
+                  'Review_Request', 
+                  'Product_Announcement']
+
+industry_types =['Energy',
+                 'Entertainment',
+                 'Finance and Banking',
+                 'Healthcare',
+                 'Hospitality',
+                 'Real Estate', 'Retail', 'Software and Technology']
+
+
+target = st.selectbox('Target Variables',target_variables, index=0)
+campaign = st.selectbox('Campaign Types',campaign_types, index=0)
+industry = st.selectbox('Industry Types',industry_types, index=0)
+
+
+if st.button('Generate Predictions'):
+    if upload_img is None:
+        st.error('Please upload an image')
+    else:
+        placeholder = st.empty()
+        placeholder.write("Loading Data...")
+
+        # Starting Predictions
+
+        data = pd.read_csv('data/wrangled_data_v2.csv', index_col=0)
+        data_mod = data.copy()
+        data_mod = data[(data.campain_type == campaign) & (data.industry == industry)]
+
+        embeddings_df = pd.read_csv('data/embeddings_df.csv',index_col=0)
+        embeddings_df = embeddings_df.iloc[data.index]
+
+
+        # Transform to tensor 
+        # transforming user input PIL Image to tensor
+        # single_img_path = list(uploaded_image.value.keys())[0]
+        single_image = upload_img.convert('RGB') # converting grayscale images to RGB
+        # st.image(single_image, caption='Uploaded Image', width=300)
+
+        my_transforms = transforms.Compose([
+        transforms.Resize((224,224)),
+        transforms.ToTensor()
+        ])
+
+        image_tensor = my_transforms(single_image).unsqueeze(0) # transforming into tensor, unsqueeze to match input batch size dimensions
+
+        placeholder.write('Loading Model...')
+
+        model_path = 'model/my_checkpoint1.pth'
+        model = torch.load(model_path,map_location=torch.device('cpu'))
+        model.eval()
+        image_imbeddings = SaveFeatures(list(model._modules.items())[-1][1])
+
+        with torch.no_grad():
+            outputs = model(image_tensor)  # switched for cpu: image_tensor.cuda() (no cuda)
+        img_embeddings = image_imbeddings.features[0]
+
+
+        xgb_model = pickle.load(open("model/xgb_grid_model.pkl", "rb"))
+        col_names = ['Abarth', 'Cab', 'Convertible', 'Coupe', 'GS', 'Hatchback', 'IPL', 'Minivan', 'R', 'SRT-8', 'SRT8', 'SS', 'SUV', 'Sedan', 'SuperCab', 'Superleggera', 'Type-S', 'Van', 'Wagon', 'XKR', 'Z06', 'ZR1']
+        img_df = pd.DataFrame([img_embeddings], columns=col_names)
+
+        #####
+        # Getting Probabilities for Subsetted Dataframe
+        full_df_probs = xgb_model.predict_proba(embeddings_df)
+        full_df_probs = [i[1] for i in full_df_probs]
+        prob_series = pd.Series(full_df_probs, index= embeddings_df.index)
+
+        # 2 from each 
+        top_10 = prob_series.sort_values(ascending=False)[:20]
+        random_4_from_top_10 = top_10.sample(replace=False,n=1)
+        
+        # 2 from top 10 to 100
+        top_10_100 = prob_series.sort_values(ascending=False)[20:100]
+        random_4_from_top_10_100 = top_10_100.sample(replace=False,n=1)
+
+        alternate_probs = pd.concat([random_4_from_top_10, random_4_from_top_10_100], axis=0)
+
+        ######
+        # Making predictions on user input and displaying results:
+        img_pred = xgb_model.predict(img_df)[0]
+        img_proba = xgb_model.predict_proba(img_df)[0][1]
+        max_prob_dict = {}
+        max_prob_dict['current_image'] = img_proba
+        for i in range(len(alternate_probs)):
+            max_prob_dict['Alternate Image '+ str(i+1)] = alternate_probs.values[i]
+
+        st.write('Below are the probabilities if alternate recommended images were used')
+
+        st.subheader('Original Image Probability')
+        st.image(upload_img,caption = convert_percentage(img_proba),width=300)
+
+
+        img_index_1 = alternate_probs.index[0]
+        img_path_1 = data.iloc[img_index_1][0]
+
+        img_index_2 = alternate_probs.index[1]
+        img_path_2 = data.iloc[img_index_2][0]
+
+        bucket = 'lozx-public-data'
+        file_base = 'Model-IO/'
+        im_1 = read_image_from_s3(bucket, file_base + img_path_1)
+        im_2 = read_image_from_s3(bucket, file_base + img_path_2)
+
+
+        alt_col1, alt_col2 = st.columns([1,1])
+        with alt_col1:
+            st.subheader("Alternate Image 1")
+            st.image(im_1, caption=convert_percentage(alternate_probs.values[0]),width=300);
+        with alt_col2:
+            st.subheader("Alternate Image 2")
+            st.image(im_2, caption=convert_percentage(alternate_probs.values[1]), width=300);
+
+
+        placeholder.empty()

model/my_checkpoint1.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:449641440be8207a96c214f5d58e360dec822debddb244b3905c12b5fc174166
+size 85342809

model/xgb_grid_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c2869adf4748e3ddebcb57cd67c6adfdb19b2fb223c3bb5c6dfdd5bca69888d5
+size 66920

requirements.txt

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+torch
+torchvision
+xgboost==0.90
+boto3
+botocore
+plotly
+bokeh==2.4.1
+scikit-learn