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.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
+datasets/lise.mp3 filter=lfs diff=lfs merge=lfs -text
+datasets/secret.mp4 filter=lfs diff=lfs merge=lfs -text

datasets/diabetes.csv

@@ -0,0 +1,769 @@
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+1,93,70,31,0,30.4,0.315,23,0

datasets/lise.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:95cd2d443eb9d765f8a67b620369681f7b5d30e8ce729938b3f803571bdb3f7e
+size 10404534

datasets/secret.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d46bb4fc16fd7a05d05db80cd589f96211a2739fc1b307ca3cbb2298abd94594
+size 83162202

datasets/shippers.csv

@@ -0,0 +1,4 @@
+shipperID,companyName,phone
+1,Speedy Express,(503) 555-9831
+2,United Package,(503) 555-3199
+3,Federal Shipping,(503) 555-9931

models/model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a631bc379a66bfbff7213988ab66b98a66fe89228d2da8d3773dec91dbcc8bdc
+size 1398864

pages/Body_Mass_Index.py

@@ -0,0 +1,42 @@
+import streamlit as st
+
+
+st.title("Body Mass Index Project")
+
+mass = st.number_input("Enter your mass (in Kg): ")
+status = st.selectbox("Select the unity", options=["cm", "m", "ft."])
+if status == "cm":
+    try:
+        tall = st.number_input("Enter your tall (in cm) :")
+        bmi = round((mass) / (tall / 100) ** 2)
+    except ValueError as e:
+        st.error(f"Erreur: {e}")
+elif status == "m":
+    try:
+        tall = st.number_input("Enter your tall (m) :")
+        bmi = round((mass) / (tall) ** 2)
+    except ValueError as e:
+        st.error(f"Erreur: {e}")
+else:
+    try:
+        tall = st.number_input("Enter your tall (ft) :")
+        bmi = round(mass / (tall / 3.28) ** 2)
+    except ValueError as e:
+        st.error(f"Erreur: {e}")
+
+if st.button("Calculate BMI"):
+    st.text(f"your BMI is : {bmi}")
+
+    # Conditional statements based on BMI values
+if bmi < 18.5:
+    st.text("Underweight")
+elif 18.5 <= bmi < 24.9:
+    st.text("Normal weight")
+elif 25 <= bmi < 29.9:
+    st.text("Overweight")
+elif 30 <= bmi < 34.9:
+    st.text("Obesity Class 1 (Moderate)")
+elif 35 <= bmi < 39.9:
+    st.text("Obesity Class 2 (Severe)")
+else:
+    st.text("Obesity Class 3 (Very Severe)")

pages/Chart.py

@@ -0,0 +1,15 @@
+import streamlit as st
+import numpy as np
+import pandas as pd
+
+st.header("Line chart")
+data = {
+    "Expense": np.random.randint(0, 100, 20),
+    "Budget": np.random.randint(50, 150, 20),
+    "Return": np.random.randint(-10, 10, 20),
+    "Stocks": np.random.uniform(0, 1, 20),
+}
+
+chart_data = pd.DataFrame(data)
+
+st.line_chart(chart_data)

pages/Complete App.py

@@ -0,0 +1,50 @@
+import streamlit as st 
+import pandas as pd 
+import matplotlib.pyplot as plt 
+import seaborn as sns
+import os
+
+st.sidebar.image(os.getcwd()+'\\medias\\nvidia.jpg',width=100)
+st.set_option('deprecation.showPyplotGlobalUse', False)
+@st.cache_data
+def load_data(dataset):
+    df = pd.read_csv(dataset)
+    return df
+
+def main():
+    menu =['Home','Data Analysis','Data Visualisation','About']
+    choice = st.sidebar.selectbox('Select Menu',menu)
+
+    if choice == 'Home':
+        st.markdown("<h1 style='text-align:center;color: brown;'>Streamlit Diabetis App</h1>",unsafe_allow_html=True)
+        st.markdown("<h2 style='text-align:center;color: blue;'>Diabetis study in Cameroon</h2>",unsafe_allow_html=True)
+        left,middle,right = st.columns((1,1.5,1))
+        
+        st.write("This is an app that will analyse diabetes Datas with some python tools that can optimize decisions")
+        with middle:
+            st.subheader('Diabetis Informations')
+        st.write("In Cameroon, the prevalence of diabetes in adults in urban areas is currently estimated at 6 – 8%, with as much as 80% of people living with diabetes who are currently undiagnosed in the population. Further, according to data from Cameroon in 2002, only about a quarter of people with known diabetes actually had adequate control of their blood glucose levels. The burden of diabetes in Cameroon is not only high but is also rising rapidly. Data in Cameroonian adults based on three cross-sectional surveys over a 10-year period (1994–2004) showed an almost 10-fold increase in diabetes prevalence.")
+
+    if choice == 'Data Analysis':
+        st.markdown("<h2 style='text-align:center;color: green;'>Diabetis Data Analysis</h2>",unsafe_allow_html=True)
+        st.subheader('Diabetis Dataset')
+        data = load_data(os.getcwd()+'\\datasets\\diabetes.csv')
+        st.write(data.head())
+
+        if st.checkbox('Summary'):
+            st.write(data.describe())
+        if st.checkbox('Correlation'):
+            plt.figure(figsize=(12,12))
+            st.write(sns.heatmap(data.corr(),annot=True))
+            st.pyplot()
+
+
+    if choice == 'Data Visualisation':
+        fig = plt.figure(figsize=(10,4))
+        data = load_data(os.getcwd()+'\\datasets\\diabetes.csv')
+        sns.histplot(x='Age',data=data)
+        st.pyplot(fig)
+
+
+if __name__ == '__main__':
+    main()

pages/Covid_19.py

@@ -0,0 +1,27 @@
+import streamlit as st 
+import tensorflow as tf 
+import numpy as np 
+from tensorflow.keras.utils import load_img,img_to_array
+from tensorflow.keras.preprocessing import image
+from PIL import Image,ImageOps
+import os
+
+st.title('Image Classification')
+upload_file = st.file_uploader('upload a radio image',type=['jpg','jpeg','png','PNG'])
+generate_pred = st.button('predict')
+
+model = tf.keras.models.load_model(os.getcwd()+'\models\model.h5')
+classes_p = {'COVID19':0,'NORMAL':1}
+
+if upload_file:
+    st.image(upload_file,caption='Image Telechargee',use_column_width=True)
+    test_image = image.load_img(upload_file,target_size=(64,64))
+    image_array = img_to_array(test_image)
+    image_array = np.expand_dims(image_array,axis=0)
+
+    if generate_pred:
+        prediction = model.predict(image_array)
+        classes = np.argmax(prediction[0])
+        for key,value in classes_p.items():
+            if value == classes:
+                st.title('Prediction of image is {}'.format(key))

pages/Forms.py

@@ -0,0 +1,23 @@
+import streamlit as st
+
+# form
+with st.form("Form 1"):
+    col1, col2 = st.columns(2)
+    # add elements
+    first_name = col1.text_input("First Name :")
+    last_name = col2.text_input("Last Name : ")
+    st.text_input("Email address :")
+    st.text_input("Password :")
+    st.text_input("Confirm Password :")
+    day, month, year = st.columns(3)
+    day.text_input("Day")
+    month.text_input("Month")
+    year.text_input("Year")
+
+    state = st.form_submit_button()
+    if state:
+        if first_name == "" and last_name == "":
+            st.warning("Please fill above fields")
+        else:
+            st.success("Submitted successfully")
+    # st.sidebar.radio("Select a graph", options=["Line", "Bar", "Plot"])

pages/Handle_iris.py

@@ -0,0 +1,34 @@
+import streamlit as st
+import pandas as pd
+from sklearn.datasets import load_iris
+from sklearn.ensemble import RandomForestClassifier
+import numpy as np
+
+st.title("Iris App Classifier")
+
+st.write("## User input parameters")
+
+def user_input_features():
+    sepal_l = st.slider("Sepal Length",4.3,10.0,5.0)
+    sepal_w = st.slider("Sepal Width",2.0,5.0,5.0)
+    petal_l = st.slider("Petal Length",4.3,10.0,5.0)
+    petal_w = st.slider("Petal Width",2.0,5.0,5.0)
+    
+    data = {"sepal_length":sepal_l,"sepal_width":sepal_w,"petal_length":petal_l,"petal_width":petal_w}
+    features = pd.DataFrame(data,index=[0])
+    return features
+
+
+df = user_input_features()
+st.subheader("User Input Parameters")
+st.write(df)
+
+iris = load_iris()
+X = iris.data
+y = iris.target
+
+clf = RandomForestClassifier()
+clf.fit(X,y)
+prediction_proba = clf.predict_proba(df)
+
+st.subheader(f"The probability of : {np.max(prediction_proba)}")

pages/Hello.py

@@ -0,0 +1,58 @@
+import streamlit as st
+import pandas as pd
+
+st.write("Hello world")
+
+# Title of the app
+st.title("Welcome to Streamlit")
+st.header("Streamlit web app")
+st.subheader("Web app development")
+st.write("Le Mpoti avec Reiniss Darryl moi et Jovy")
+
+# Md
+st.markdown("# **Hello**")
+st.markdown("-----------")
+st.markdown("[Google](https://www.google.com/)")
+st.latex(r"\begin{pmatrix}a&b&c&d\\e&f&g&h\\i&j&k&l\\m&n&o&p\end{pmatrix}")
+
+# handle json data
+json_data = {
+    "jackson": ["billy", "jean", "in", "your", "heart"],
+    "micheal": ["billy", "jean", "in", "your", "heart"],
+}
+
+st.json(json_data)
+
+
+code = """
+    import pandas as pd
+
+    # read the data
+    df = pd.read_csv("file.csv").
+
+    # set date index
+    df.set_index("Date", inplace = True)
+
+    library(ggplot2)
+
+    # read data in R
+    data = read.csv("data.csv")
+"""
+
+st.code(code)
+
+st.metric(label="Wind Speed", value="0.25 m/s-1", delta="-1.45 m/s-1")
+
+# create a table
+
+tab = pd.read_csv("datasets/shippers.csv")
+
+st.table(tab)
+
+st.dataframe(tab)
+st.write(tab)
+
+st.image("datasets/photo.jpg", caption="La photo d'un teuch", width=500)
+
+st.audio("datasets/lise.mp3")
+st.video("datasets/secret.mp4")

pages/Input_Controls.py

@@ -0,0 +1,45 @@
+import streamlit as st
+import pandas as pd
+
+st.title("Controls")
+# checkbox
+state = st.checkbox("checkbox", value=True)
+if state:
+    st.write("You check the box")
+else:
+    pass
+
+# radio btn
+radio_btn = st.radio(
+    "In which country do you live ?", options=("USA", "Cameroon", "China")
+)
+
+# click btn
+btn = st.button("Click me")
+
+# select box
+select = st.selectbox("What is your name", options=["Papa", "Mama", "Uncle"])
+
+# multi select
+multi_select = st.multiselect(
+    "What are your nightmares", options=["Papa", "Mama", "Uncle"]
+)
+
+# upload a file
+st.title("Upload a file ...")
+st.markdown("---------------")
+img = st.file_uploader("Please upload an image", type=["jpg", "png", "jpeg"])
+video = st.file_uploader("Please upload a video", type=["mp4", "mpeg4", "avi"])
+
+# slider
+st.slider("This is a slider", min_value=0, max_value=450, value=90)
+
+# text input
+sentence = st.text_input("Enter your the sentence that you want to translate")
+
+
+# text area
+sentence = st.text_area("Enter your sentence description")
+
+# enter a date
+date = st.date_input("Enter a date")

pages/Progress_Bar.py

@@ -0,0 +1,13 @@
+import streamlit as st
+import time
+
+st.title("Progress bar")
+
+with st.expander("About this App"):
+    st.write("You can now display ")
+
+mybar = st.progress(0)
+for percent_complete in range(100):
+    time.sleep(0.05)
+    mybar.progress(percent_complete + 1)
+st.balloons()

pages/Upload_Files.py

@@ -0,0 +1,28 @@
+import streamlit as st
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+st.title("File uploader")
+
+csv_file = st.file_uploader("Please upload a file", type=["csv"])
+
+if csv_file is not None:
+    df = pd.read_csv(csv_file)
+    st.write(df)
+    st.subheader("Descriptive statistics")
+    st.write(df.describe())
+    col1, col2 = st.columns(2)
+    with col1:
+        fig = plt.figure(figsize=(12, 16))
+        sns.scatterplot(x="Rating", y="Reviews", hue="Delivery option", data=df)
+        plt.title("Rating vs. Reviews")
+        st.pyplot(fig)
+
+    with col2:
+        fig1 = plt.figure(figsize=(8, 8))
+        sns.histplot(df["Rating"])
+        plt.title("Rating")
+        st.pyplot(fig1)
+else:
+    st.info("Upload a csv")

pages/diabetes.py

@@ -0,0 +1,8 @@
+import streamlit as st 
+
+st.markdown("<h1 style='text-align:center;color: brown;'>Streamlit Diabetis App</h1>",unsafe_allow_html=True)
+
+st.markdown("<h2 style='text-align:center;color: black;'>Diabetis study in Cameroon</h2>",unsafe_allow_html=True)
+
+st.markdown("In Cameroon, the prevalence of diabetes in adults in urban areas is currently estimated at 6 – 8%, with as much as 80% of people living with diabetes who are currently undiagnosed in the population. Further, according to data from Cameroon in 2002, only about a quarter of people with known diabetes actually had adequate control of their blood glucose levels. The burden of diabetes in Cameroon is not only high but is also rising rapidly. Data in Cameroonian adults based on three cross-sectional surveys over a 10-year period (1994–2004) showed an almost 10-fold increase in diabetes prevalence.")
+st.markdown("This is an app that will analyse diabetes Datas with some python tools that can optimize decisions")