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app.py

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+import streamlit as st 
+from streamlit_option_menu import option_menu
+import tensorflow as tf
+#importation des librairies 
+#from tensorflow import keras
+import base64
+#import torch
+from keras.models import load_model
+from PIL import ImageOps, Image
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+#from util import classify
+
+def classify(image, model, class_names):
+    """
+    This function takes an image, a model, and a list of class names and returns the predicted class and confidence
+    score of the image.
+
+    Parameters:
+        image (PIL.Image.Image): An image to be classified.
+        model (tensorflow.keras.Model): A trained machine learning model for image classification.
+        class_names (list): A list of class names corresponding to the classes that the model can predict.
+
+    Returns:
+        A tuple of the predicted class name and the confidence score for that prediction.
+    """
+    # convert image to (224, 224)
+    image = ImageOps.fit(image, (224, 224), Image.Resampling.LANCZOS)
+
+    # convert image to numpy array
+    image_array = np.asarray(image)
+
+    # normalize image
+    normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
+
+    # set model input
+    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+    data[0] = normalized_image_array
+
+    # make prediction
+    prediction = model.predict(data)
+    # index = np.argmax(prediction)
+    index = 0 if prediction[0][0] > 0.95 else 1
+    class_name = class_names[index]
+    confidence_score = prediction[0][index]
+
+    return class_name, confidence_score,index
+model=load_model('./models/model.h5')
+with open('./models/names.txt', 'r') as f:
+    class_names = [a[:-1].split(' ')[1] for a in f.readlines()]
+    f.close()
+st.set_page_config(layout='wide')
+st.markdown("""
+        <style>
+               .block-container {
+                    padding-top: 2rem;
+                    padding-bottom: 0rem;
+                    padding-left: 1rem;
+                    padding-right: 1rem;
+                }
+        </style>
+        """, unsafe_allow_html=True)
+
+
+
+#st.header(f"LA QUALITE DE L'AIR A")
+#st.title(f"NAIROBI,KENYA")
+#st.header("Pollution de l'Air en NAIROBI")
+    
+header , menu = st.columns(2)
+with header:
+    st.image('static/image/cif2.png')
+
+with menu:
+    # option_menu(menu_title=None,
+    #     options=['Visualisation','Prédiction'],
+    #     icons=["house","book",'envelope'],
+    #     default_index=0,
+    #     orientation="horizontal"
+    #     )
+    selecte=option_menu(None, ["Home", "About"], 
+    icons=['house', 'cloud-upload'], 
+    menu_icon="cast", default_index=0, orientation="horizontal",
+    styles={
+        "container": {"padding": "0!important", "background-color": "#ffffff","font-family": "Impact, Haettenschweiler, 'Arial Narrow Bold', sans-serif"},
+        "icon": {"color": "red", "font-size": "25px" }, 
+        "nav-link": {"font-size": "20px", "text-align": "left", "margin":"0px", "--hover-color": "#eee"},
+        "nav-link-selected": {"background-color": "#F9C949","color":"white"},
+        "menu-title":{"color":"#424143"}
+    }
+)
+if selecte == "Home":
+    st.title(f"A propos de la Fraude à l'Assurance Automobile")
+    sect1_col1,sect1_col2, sect1_col3 = st.columns(3)
+    for col in (sect1_col1,sect1_col2, sect1_col3):
+        col.container()
+
+    with open('static/css/style.css') as f:
+            st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
+    with sect1_col2.container(height=360):
+        
+        # st.markdown(
+        #     """
+        # <style>
+        # [data-testid="stMetricValue"]{
+        #     font-siz: 50px;
+        #     color: #2FB56B;
+        #     font-weight:bold;
+        #     text-align:center;
+        # }
+        # [data-testid="metric-container"] {
+        #     background-color: #EEEEEE;
+        #     border: 2px solid #CCCCCC;
+        #     padding: 5% 5% 5% 10%;
+        #     border-radius: 5px;
+        # }
+        # </style>
+        # """,
+        # unsafe_allow_html=True,
+        # )
+        st.markdown("""
+        <style>
+        # div[data-testid="stMetric"] {
+        #     background-color: rgba(187, 216, 158, 0.59);
+        #     border: 1px solid rgba(28, 131, 225, 0.1);
+            padding:-10px;
+        #     border-radius: 5px;
+        #     color: rgb(30, 103, 119);
+        #     overflow-wrap: break-word;
+        #     font-weight:bold;
+            
+            
+        # }
+        
+        [data-testid="stMetricValue"]{
+            font-size: 45px;
+            color: #ff3131;
+            font-weight:bold;
+            text-align:center;
+            margin-top:-33px;
+            
+            
+            
+        }
+
+        /* breakline for metric text */
+        [data-testid="stMetricLabel"] {
+            word-wrap: break-word;
+            color: #ef8451;
+            font-size:40px;
+            font-weight:bold;
+                
+            }
+                    
+                    
+        [data-testid ="stVerticalBlock"]{
+            #background-color: rgba(187, 216, 158, 0.59);
+            #border: 1px solid rgba(28, 131, 225, 0.1);
+            text-align:center;
+        }
+        [data-v-5af006b8]{
+            background-color:black;
+        }
+        </style>
+    """
+    , unsafe_allow_html=True)
+        st.write("Le secteur de l'assurance est confronté à un dilème:")
+        #st.write(" au KENYA par an ")
+        st.caption("Distinguer les demandes d'indmnisations authentiques des des demandes trompeuses")
+
+    with sect1_col1.container(height=360):
+        st.write("L'émergence de l'IA générative a contribué à: ",)
+        st.caption("l'augmentation des demandes d'indemnisations frauduleuses")
+
+    with sect1_col3.container(height=360):
+        script = """<div id= 'conte'></div>"""
+        st.subheader("Cout de la fraude à l'assurance ")
+        st.write("Le cout de la Fraud à l'assurance automobile est estimé à")
+        st.metric("", "Plus de 10% ")
+        st.write("de la somme totale des sinistres")
+     
+    
+    st.title(f"Vérifiez l'Authenticité des images de vos Clients")
+    st.markdown("Distinguez les images frauduleus des images non frauduleuses")
+    with st.container(height=400):
+        st.markdown(
+            """
+            <style>
+                .st-emotion-cache-g7r313 {
+                    width: 700px;
+                    margin-left:25%;
+                    margin-rigth:25%;
+
+                }
+                .st-emotion-cache-1kyxreq{
+                    flex-direction:column;
+                }
+                .st-emotion-cache-1v0mbdj{
+                    align-items:center !important;
+                }
+            </style>
+            """, unsafe_allow_html=True
+            )
+        file = st.file_uploader("Choisissez une image",type=["png","jpg"])
+        
+        
+        if file is not None:
+            image = Image.open(file).convert('RGB')
+            st.image(image, use_column_width=True)
+
+            
+            class_name, conf_score ,index = classify(image, model, class_names)
+
+            
+            if index == 0:
+                st.image('static/image/not_fraud.JPG')
+            else:
+                st.image('static/image/Fraud-alert-2023.JPG')
+            st.write("### score: {}%".format(int(conf_score * 1000) / 10))
+    
+    footer = st.container()
+    with footer:
+        st.markdown("---")
+        st.markdown(
+            """
+            <style>
+                p {
+                    font-size: 16px;
+                    text-align: center;
+                }
+                a {
+                    text-decoration: none;
+                    color: #00a;
+                    font-weight: 600;
+                }
+            </style>
+            <p>
+                &copy; Designed by <a href="https://linkedin.com/in/mohamedyosef101">ONDOA Michelle & NGNINTEDEM Marlyne</a>.
+            </p>
+            """, unsafe_allow_html=True
+            )
+if selecte == "About":
+    
+    st.title("A propos du modèle")
+    st.markdown(
+            """
+            <style>
+                /*Les Titres*/
+            .st-emotion-cache-10trblm {
+                font-size: 1.5rem;
+                color: #424143;
+                font-weight: 300;
+                text-transform: uppercase;
+                line-height: 1.235;
+                font-family: Impact, Haettenschweiler, 'Arial Narrow Bold', sans-serif;
+                margin-left:0% !important; 
+                margin-right: 5% !important;
+            }
+            
+        
+            [data-testid="stMetricValue"]{
+                font-size: 45px;
+                color: #ff3131;
+                font-weight:bold;
+                text-align:center;
+                margin-top:-33px;
+                
+                
+                
+            }
+
+            /* breakline for metric text */
+            [data-testid="stMetricLabel"] {
+                word-wrap: break-word;
+                color: #ef8451;
+                font-size:40px;
+                font-weight:bold;
+                    
+                }
+            .st-emotion-cache-16idsys >p{
+                font-size:30px;
+            }
+                        
+            [data-testid ="stVerticalBlock"]{
+                #background-color: rgba(187, 216, 158, 0.59);
+                #border: 1px solid rgba(28, 131, 225, 0.1);
+                text-align:center;
+            }
+            [data-v-5af006b8]{
+                background-color:black;
+            }
+            .st-emotion-cache-1q7spjk{
+                font-family: Impact, Haettenschweiler, 'Arial Narrow Bold', sans-serif;
+                color: #FF3131;
+                font-size: 1.8rem;
+                font-weight: 300;
+                text-transform: uppercase;
+                line-height: 1.235;
+                margin-bottom:10px;
+            
+            }
+            </style>
+            """, unsafe_allow_html=True
+            )
+    
+    with st.container(height=1500):
+        st.title('Définition du problème')
+        st.write('Prédire si une image de voiture donnée est une demande d\'indemnisation frauduleuse ?')
+
+        st.title('Type de problème')
+
+        st.write('Problème de Classification  ')
+
+        st.title('Problème Domaine')
+
+        st.write('Vision par ordinateur')
+
+        st.title('Analyse des données')
+        st.write('L\'examination des données a montré que  les données d\'entraînement sont déséquilibrées. La différence entre la distribution des classes positives et négatives est TRÈS ÉNORME !' )
+        c1 , c2 = st.columns(2)
+        with c1:
+            st.metric("Classe Négative", "94%")
+        with c2:
+            st.metric("Classe Positive", "6%")
+
+        st.title("Modélisation")
+
+        st.caption('ResNet 18 (Pré-entraîné)')
+        st.write('Comme il s\'agit d\'un problème de vision par ordinateur, il était très clair et logique d\'essayer un réseau neuronal convolutif. Nous avons utilisé ResNet 18 avec les  poids pré-entraînés sur l\'ensemble de données ImageNet .Nous avons  remplacé la couche de sortie et  la couche d\'entrée.Le model a donné un résultat suivants:')
+        col1, col2 =st.columns(2)
+        with col1:
+            st.image('./static/image/loss.png')
+        with col2:
+            st.image('./static/image/acc.png')
+    footer = st.container()
+    with footer:
+        st.markdown("---")
+        st.markdown(
+            """
+            <style>
+                p {
+                    font-size: 16px;
+                    text-align: center;
+                }
+                a {
+                    text-decoration: none;
+                    color: #00a;
+                    font-weight: 600;
+                }
+            </style>
+            <p>
+                &copy; Designed by <a href="https://linkedin.com/in/mohamedyosef101">ONDOA Michelle & NGNINTEDEM Marlyne </a>.
+            </p>
+            """, unsafe_allow_html=True
+            )

models/model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:98d708e0813f0821e12c1efd3c4522c2263d7da8cf610b1ddd08456b66a3956d
+size 2453432

models/names.txt

@@ -0,0 +1,2 @@
+0 Non Fraud
+1 Fraud

static/css/style.css

@@ -0,0 +1,48 @@
+/* Les container*/
+.st-emotion-cache-6srzk2  {
+    background-color: #f9ca49c0;
+    border: 1px solid rgba(28, 131, 225, 0.1);
+    text-align: center;
+    font-family: Impact, Haettenschweiler, 'Arial Narrow Bold', sans-serif;
+
+    
+}
+
+.menu .container-xxl[data-v-5af006b8] {
+    background-color: transparent !important;
+}
+
+@font-face {
+    font-family: "Anton";
+    src: url(Anton-Regular.ttf);
+}
+/*Les Titres*/
+.st-emotion-cache-10trblm {
+    font-size: 1.8rem;
+    color: #424143;
+    font-weight: 300;
+    text-transform: uppercase;
+    line-height: 1.235;
+    font-family: Impact, Haettenschweiler, 'Arial Narrow Bold', sans-serif;
+    
+}
+/*section2 lement2*/
+.st-emotion-cache-1q7spjk{
+    font-family: Impact, Haettenschweiler, 'Arial Narrow Bold', sans-serif;
+    margin-left:5% !important; 
+    margin-right: 5% !important;
+    color: #FF3131;
+    font-size: 1.8rem;
+    font-weight: 300;
+    text-transform: uppercase;
+    line-height: 1.235;
+
+}
+/*Les paragraphes*/
+p {
+     
+    color: 424143;
+    padding-left:20% ; 
+    padding-right: 20%;
+    font-size: 20px;
+}

util.py

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+import base64
+
+import streamlit as st
+from PIL import ImageOps, Image
+import numpy as np
+
+
+def classify(image, model, class_names):
+    """
+    This function takes an image, a model, and a list of class names and returns the predicted class and confidence
+    score of the image.
+
+    Parameters:
+        image (PIL.Image.Image): An image to be classified.
+        model (tensorflow.keras.Model): A trained machine learning model for image classification.
+        class_names (list): A list of class names corresponding to the classes that the model can predict.
+
+    Returns:
+        A tuple of the predicted class name and the confidence score for that prediction.
+    """
+    # convert image to (224, 224)
+    image = ImageOps.fit(image, (224, 224), Image.Resampling.LANCZOS)
+
+    # convert image to numpy array
+    image_array = np.asarray(image)
+
+    # normalize image
+    normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
+
+    # set model input
+    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+    data[0] = normalized_image_array
+
+    # make prediction
+    prediction = model.predict(data)
+    # index = np.argmax(prediction)
+    index = 0 if prediction[0][0] > 0.95 else 1
+    class_name = class_names[index]
+    confidence_score = prediction[0][index]
+
+    return class_name, confidence_score