adding app.py

.streamlit/config.toml

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+[theme]
+
+# Primary accent for interactive elements
+primaryColor = '#E694FF'
+
+# Background color for the main content area
+backgroundColor = '#00172B'
+
+# Background color for sidebar and most interactive widgets
+secondaryBackgroundColor = '#0083B8'
+
+# Color used for almost all text
+textColor = '#FFFFFF'
+
+# Font family for all text in the app, except code blocks
+# Accepted values (serif | sans serif | monospace) 
+# Default: "sans serif"
+font = "sans serif"

app.py

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+from tensorflow.keras.models import load_model
+import streamlit as st
+import requests
+from streamlit_lottie import st_lottie
+import cv2
+import numpy as np
+from PIL import Image
+# Load the ensemble model using tf.keras.models.load_model()
+loaded_ensemble_model = load_model('ensemble_model.h5')
+
+# st.markdown('<h1 style="color:red;">Ensemble Image classification model for Alzheimer</h1>', unsafe_allow_html=True)
+# st.markdown('<h2 style="color:gray;">The image classification model classifies brain scan image into following categories:</h2>', unsafe_allow_html=True)
+# st.markdown('<h3 style="color:gray;"> Moderate,Mild,Very Mild, NonDemented</h3>', unsafe_allow_html=True)
+
+## Find more emojis here: https://www.webfx.com/tools/emoji-cheat-sheet/
+# st.set_page_config(page_title="My Webpage", page_icon=":tada:", layout="wide")
+
+
+def load_lottieurl(url):
+    r = requests.get(url)
+    if r.status_code != 200:
+        return None
+    return r.json()
+
+
+# Use local CSS
+def local_css(file_name):
+    with open(file_name) as f:
+        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
+
+
+local_css("style/styles.css")
+
+# ---- LOAD ASSETS ----
+lottie_coding = load_lottieurl("https://lottie.host/6b8742e7-e4f5-4403-8bc0-1d5c2fae6e55/ah80z8X1Hu.json")
+img_contact_form = Image.open("images/yt_contact_form.png")
+img_lottie_animation = Image.open("images/yt_lottie_animation.png")
+
+# ---- HEADER SECTION ----
+with st.container():
+    st.subheader("Hi, Good Morning! :wave:")
+    st.title("Welcomme To Alzheimer Disease Detection Web App")
+    st.write(
+        "Alzheimer's disease is a progressive brain disorder that causes memory loss, cognitive decline, and changes in behavior."
+    )
+    st.write("[Learn More >](https://en.wikipedia.org/wiki/Alzheimer%27s_disease)")
+
+# ---- WHAT I DO ----
+with st.container():
+    st.write("---")
+    left_column, right_column = st.columns(2)
+    with left_column:
+        st.header("Description:")
+        st.write("##")
+        st.write(
+            """
+            We have devided the alzheimers disease into four stages namely:
+            - Non-Demented: Individuals with no signs of dementia.
+            - Mild Demented: Individuals with mild cognitive impairment.
+            - Moderate Demented: Individuals with moderate cognitive impairment.
+            - Very Mild Demented: Individuals with very mild cognitive impairment."
+            We are aiming at providing the early detection of the disease using this web application, which will help the early diagnosis.
+            """
+        )
+        # st.write("[aisa likh jo link ko specify kre](kuch bhi link daal de)")
+    with right_column:
+        st_lottie(lottie_coding, height=300, key="coding")
+
+    
+
+# # ---- PROJECTS ----
+# with st.container():
+#     st.write("---")
+#     st.header("My Projects")
+#     st.write("##")
+#     image_column, text_column = st.columns((1, 2))
+#     with image_column:
+#         st.image(img_lottie_animation)
+#     with text_column:
+#         st.subheader("Integrate Lottie Animations Inside Your Streamlit App")
+#         st.write(
+#             """
+#             Learn how to use Lottie Files in Streamlit!
+#             Animations make our web app more engaging and fun, and Lottie Files are the easiest way to do it!
+#             In this tutorial, I'll show you exactly how to do it
+#             """
+#         )
+#         st.markdown("[Watch Video...](https://youtu.be/TXSOitGoINE)")
+# with st.container():
+#     image_column, text_column = st.columns((1, 2))
+#     with image_column:
+#         st.image(img_contact_form)
+#     with text_column:
+#         st.subheader("How To Add A Contact Form To Your Streamlit App")
+#         st.write(
+#             """
+#             Want to add a contact form to your Streamlit website?
+#             In this video, I'm going to show you how to implement a contact form in your Streamlit app using the free service ‘Form Submit’.
+#             """
+#         )
+#         st.markdown("[Watch Video...](https://youtu.be/FOULV9Xij_8)")
+
+
+  #---   INPUT-OUTPUT ---
+
+        # upload= st.file_uploader('Insert image for classification', type=['png','jpg'])
+        # c1, c2= st.columns(2)
+        # if upload is not None:
+        #     im= Image.open(upload)
+        #     im = im.convert('RGB')
+        #     img= np.asarray(im)
+        #     image= cv2.resize(img,(150, 150))
+        #     img_array = image.reshape(1,150,150,3)
+        #     c1.header('Input Image')
+        #     c1.image(im)
+        #     loaded_ensemble_model = load_model('ensemble_model.h5')
+            
+        #     pred = loaded_ensemble_model.predict([img_array,img_array,img_array])
+        #     labels = {0:'MildDemented',1:'ModerateDemented',2:'NonDemented',3:'VeryMildDemented'}
+        #     c2.header('Output')
+        #     c2.subheader('Predicted class :')
+        #     c2.write(labels[pred.argmax()])
+        #     c2.subheader('With :')
+        #     c2.write(f'{int(pred.max()*100)}% assurity')
+
+  # === chat gpt ===
+
+        # ... (Previous code)
+
+# ---   INPUT-OUTPUT ---
+# upload = st.file_uploader('Insert image for classification', type=['png', 'jpg'])
+
+# if upload is not None:
+#     im = Image.open(upload)
+#     im = im.convert('RGB')
+#     img = np.asarray(im)
+#     image = cv2.resize(img, (150, 150))
+#     img_array = image.reshape(1, 150, 150, 3)
+
+#     st.header('Input Image')
+#     st.image(im, width=176, height=208, use_column_width=True)  # Center the image
+
+#     loaded_ensemble_model = load_model('ensemble_model.h5')
+#     pred = loaded_ensemble_model.predict([img_array, img_array, img_array])
+#     labels = {0: 'MildDemented', 1: 'ModerateDemented', 2: 'NonDemented', 3: 'VeryMildDemented'}
+
+#     st.header('Output')
+#     col1, col2 = st.columns(2)
+#     with col1:
+#         st.subheader('Predicted class:')
+#         st.write(labels[pred.argmax()])
+#     with col2:
+#         st.subheader('With:')
+#         st.write(f'{int(pred.max() * 100)}% assurity')
+        # ... (Previous code)
+
+
+
+# ---   INPUT-OUTPUT ---
+# upload = st.file_uploader('###### **Insert image for classification**', type=['png', 'jpg'])
+
+st.header('Insert image for classification:')
+
+upload = st.file_uploader('', type=['png', 'jpg'])
+
+
+if upload is not None:
+    im = Image.open(upload)
+    im = im.convert('RGB')
+    img = np.asarray(im)
+    image = cv2.resize(img, (150, 150))
+    img_array = image.reshape(1, 150, 150, 3)
+
+    # Resize the image to 176x208 pixels
+    resized_im = im.resize((176, 208))
+
+    st.header('Input Image')
+
+    # Display the resized image
+    st.image(resized_im, use_column_width=False, width=176, caption='Uploaded Image')
+
+    loaded_ensemble_model = load_model('ensemble_model.h5')
+    pred = loaded_ensemble_model.predict([img_array, img_array, img_array])
+    labels = {0: 'MildDemented', 1: 'ModerateDemented', 2: 'NonDemented', 3: 'VeryMildDemented'}
+
+    st.header('Output')
+    col1, col2 = st.columns(2)
+    with col1:
+        st.subheader('Predicted class:')
+        st.write(labels[pred.argmax()])
+    with col2:
+        st.subheader('With:')
+        st.write(f'{int(pred.max() * 100)}% assurity')
+
+
+
+
+
+
+# ---- CONTACT ----
+with st.container():
+    st.write("---")
+    st.header("For any querries or suggestions!")
+    st.write("##")
+
+    # Documention: https://formsubmit.co/ !!! CHANGE EMAIL ADDRESS !!!
+    contact_form = """
+    <form action="https://formsubmit.co/sainimohit572000@gmail.com" method="POST">
+        <input type="hidden" name="_captcha" value="false">
+        <input type="text" name="name" placeholder="Your name" required>
+        <input type="email" name="email" placeholder="Your email" required>
+        <textarea name="message" placeholder="Your message here" required></textarea>
+        <button type="submit">Send</button>
+    </form>
+    """
+    left_column, right_column = st.columns(2)
+    with left_column:
+        st.markdown(contact_form, unsafe_allow_html=True)
+    with right_column:
+        st.empty()

ensemble_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e6b753fc568789b49bd0769ebc59c1ecdf31637274a1d33c915b43135d85a63e
+size 441343312

requirement.txt

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+tensorflow
+opencv-python
+streamlit_lottie

style/styles.css

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+/* CSS Snippet from W3schools: https://www.w3schools.com/howto/howto_css_contact_form.asp */
+/* Style inputs with type="text", select elements and textareas */
+input[type=message], input[type=email], input[type=text], textarea {
+    width: 100%; /* Full width */
+    padding: 12px; /* Some padding */ 
+    border: 1px solid #ccc; /* Gray border */
+    border-radius: 4px; /* Rounded borders */
+    box-sizing: border-box; /* Make sure that padding and width stays in place */
+    margin-top: 6px; /* Add a top margin */
+    margin-bottom: 16px; /* Bottom margin */
+    resize: vertical /* Allow the user to vertically resize the textarea (not horizontally) */
+  }
+  
+  /* Style the submit button with a specific background color etc */
+  button[type=submit] {
+    background-color: #04AA6D;
+    color: white;
+    padding: 12px 20px;
+    border: none;
+    border-radius: 4px;
+    cursor: pointer;
+  }
+  
+  /* When moving the mouse over the submit button, add a darker green color */
+  button[type=submit]:hover {
+    background-color: #45a049;
+  }
+  
+  
+  /* Hide Streamlit Branding */
+  #MainMenu {visibility: hidden;}
+  footer {visibility: hidden;}
+  header {visibility: hidden;}