Added Cataract disease Detection using Machine learning

Cataract Eye disease detection using ML/app.py

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+import streamlit as st
+import numpy as np
+from PIL import Image
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import img_to_array
+import requests
+from bs4 import BeautifulSoup
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Load the trained model
+model = load_model('cataract_detection_model.h5')
+
+# Define the image size expected by the model
+IMG_SIZE = (224, 224)  # Change this to match your model's input size
+
+# Function to preprocess the image
+def preprocess_image(image):
+    image = image.resize(IMG_SIZE)
+    image = img_to_array(image) / 255.0
+    image = np.expand_dims(image, axis=0)
+    return image
+
+# Function to make a prediction
+def predict(image):
+    processed_image = preprocess_image(image)
+    prediction = model.predict(processed_image)
+    return 'Cataract' if prediction[0] > 0.5 else 'Normal'
+
+# Function to fetch cataract stats
+def fetch_cataract_stats():
+    stats = {
+        "Global Prevalence": "51% of global blindness",
+        "Leading Cause": "Leading cause of blindness worldwide",
+        "Treatable": "Cataract is treatable with surgery"
+    }
+    return stats
+
+# Function to fetch cataract remedies
+def fetch_cataract_remedies():
+    remedies = [
+        "Surgical removal of the cataract",
+        "Wearing sunglasses to reduce glare",
+        "Using magnifying lenses for reading",
+        "Improving lighting in your home"
+    ]
+    return remedies
+
+# Function to fetch cataract risks
+def fetch_cataract_risks():
+    risks = [
+        "Aging",
+        "Diabetes",
+        "Excessive exposure to sunlight",
+        "Smoking",
+        "Obesity",
+        "High blood pressure",
+        "Previous eye injury or inflammation"
+    ]
+    return risks
+
+# Function to plot stats about cataracts
+def plot_cataract_stats():
+    labels = ['Cataract', 'Other Causes']
+    sizes = [51, 49]
+    colors = ['#ff9999','#66b3ff']
+    explode = (0.1, 0)
+
+    fig1, ax1 = plt.subplots()
+    ax1.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%',
+            shadow=True, startangle=140)
+
+    ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
+
+    st.pyplot(fig1)
+
+# Function to fetch cataract symptoms
+def fetch_cataract_symptoms():
+    symptoms = [
+        "Clouded, blurred or dim vision",
+        "Increasing difficulty with vision at night",
+        "Sensitivity to light and glare",
+        "Seeing 'halos' around lights",
+        "Frequent changes in eyeglass or contact lens prescription",
+        "Fading or yellowing of colors",
+        "Double vision in a single eye"
+    ]
+    return symptoms
+
+# Streamlit app
+st.markdown(
+    """
+    <style>
+    .centered-heading {
+        font-size: 2.5rem;
+        color: blue;
+        text-align: center;
+        font-weight: bold;
+    }
+    .subheading {
+        font-size: 1rem;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+st.markdown('<h1 class="centered-heading">Cataract Detection</h1>', unsafe_allow_html=True)
+
+# Image upload section
+st.markdown('<h2 class="subheading">Upload an Image</h2>', unsafe_allow_html=True)
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
+    st.write("Classifying...")
+    label = predict(image)
+    if label == 'Cataract':
+        st.error(f'Prediction: {label}')
+    else:
+        st.success(f'Prediction: {label}')
+
+    # Display cataract statistics
+    st.markdown('<h2 class="subheading">Cataract Statistics</h2>', unsafe_allow_html=True)
+    stats = fetch_cataract_stats()
+    st.write(stats)
+    plot_cataract_stats()
+
+    # Display cataract remedies
+    st.markdown('<h2 class="subheading">Cataract Remedies</h2>', unsafe_allow_html=True)
+    remedies = fetch_cataract_remedies()
+    st.write(remedies)
+
+    # Display cataract risks
+    st.markdown('<h2 class="subheading">Cataract Risks</h2>', unsafe_allow_html=True)
+    risks = fetch_cataract_risks()
+    st.write(risks)
+
+    # Display cataract symptoms in tabular form
+    st.markdown('<h2 class="subheading">Cataract Symptoms</h2>', unsafe_allow_html=True)
+    symptoms = fetch_cataract_symptoms()
+    symptoms_df = pd.DataFrame(symptoms, columns=["Symptoms"])
+    st.table(symptoms_df)
+
+# Display nearby eye clinics using an embedded Google Maps link
+st.markdown('<h2 class="subheading">Nearby Eye Clinics</h2>', unsafe_allow_html=True)
+st.write("View eye clinics in Pune on Google Maps:")
+st.components.v1.iframe("https://www.google.com/maps/embed?pb=!1m18!1m12!1m3!1d3783.197742711061!2d73.83969451542263!3d18.520430787407185!2m3!1f0!2f0!3f0!3m2!1i1024!2i768!4f13.1!3m3!1m2!1s0x3bc2c07df1d121c1%3A0x562b1c8f58f3728b!2sPune%2C%20India!5e0!3m2!1sen!2sus!4v1654594546546!5m2!1sen!2sus", width=600, height=450)
+
+city = st.text_input('Enter your city')
+if st.button('Submit'):
+    map_url = f"https://www.google.com/maps/search/eye+clinics+in+{city}"
+    st.write(f"Redirecting to eye clinics in {city} on Google Maps...")
+    st.markdown(f'<meta http-equiv="refresh" content="0; url={map_url}">', unsafe_allow_html=True)

Cataract Eye disease detection using ML/readme.md

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+# Cataract Disease Detection using Machine Learning
+
+
+## Model Training and evaluation :
+
+CNN model is trained over batch size = 32 , input image size =(224,224) over 20 epocchs and achieved average validation accuracy of 87.5 %.
+
+## Dataset :
+
+https://www.kaggle.com/datasets/akshayramakrishnan28/cataract-classification-dataset/data
+
+
+## Inference : 
+
+Deployed the model with the help streamlit web application to predict the possibility of eye  cataract , cataract disease symptoms , risks , statistics , grpahs and nearby eye clinic search.
+
+## Libraries Used
+
+1. **Joblib**: For downloading the random forest model
+2. **Scikit learn**: For machine learning processing  and operations
+3. **Matplotlib**: For plotting and visualizing the detection results.
+4. **Pandas**: For image manipulation.
+5. **NumPy**: For efficient numerical operations.
+6. **Seaborn** : for advanced data visualizations
+7. **plotly** : for 3D data visualizations .
+8. **Streamlit** : for creating gui of the web application.
+9. **Tensorflow** : for image based manipulation operations.
+
+
+## How to Use
+
+1. **Clone the Repository**: 
+    ```sh
+    git clone url_to_this_repository
+    ```
+
+2. **Install Dependencies**: 
+    ```sh
+    pip install -r requirements.txt
+    ```
+
+3. **Run the Model**: 
+    (download the model cataract_detection_model.h5 from below link and put in same directoy :
+      https://drive.google.com/file/d/17Um-KcGOBz8G3s6s4nJj1lJ-C8BPT8rN/view?usp=sharing)
+
+    ```python
+    streamlit run app.py
+    ```
+
+4. **View Results**: The script will allow you to predict the possbility of cataract.
+
+**DEMO** :
+
+https://github.com/user-attachments/assets/0db9e7ca-48cd-4dc5-8bc9-c97c9fa766b1
+
+
+
+

Cataract Eye disease detection using ML/requirement.txt

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+pillow
+streamlit
+pandas==2.0.3
+numpy==1.25.2
+scikit-learn==1.2.2
+beautifulsoup4
+tensorflow==2.15.0
+requests