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bring back the app.py
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app.py
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import streamlit as st
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from PIL import Image
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def main():
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st.set_page_config(page_title="DR Assistive Tool", layout="centered")
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st.markdown("<h1 style='text-align: center; color: #2E86C1;'>DR Assistive Tool</h1>", unsafe_allow_html=True)
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st.markdown("""
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<h4 style='text-align: center; color: grey;'>
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An AI-powered assistant for early detection of Diabetic Retinopathy
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</h4>
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""", unsafe_allow_html=True)
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st.markdown("""---""")
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st.markdown("""
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### 🧠 What is DR Assistive Tool?
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**DR Assistive Tool** is a user-friendly web application built for early and accurate detection of **Diabetic Retinopathy (DR)**—a diabetes-related eye disease that can lead to blindness if not diagnosed in time. This tool uses **AI and deep learning models** trained on retinal images to help healthcare professionals, students, and researchers identify the severity stage of DR.
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By simply uploading a retinal image, the model will:
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- Analyze the image using advanced machine learning techniques
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- Predict the stage of Diabetic Retinopathy (No DR, Mild, Moderate, Severe, Proliferative DR)
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- Provide a quick, reliable, and cost-efficient second opinion for screening
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The app also offers evaluation options to test the model's performance using real datasets, ensuring its credibility and practical usability.
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""")
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st.markdown("""
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---
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### 🛠 Features:
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- 📤 **Upload retinal images** for DR stage prediction
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- 📊 **Evaluate** the model using real test datasets
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- 📁 **View results** with interpretation and confidence score
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- 📈 **Analyze model performance** using accuracy, precision, confusion matrix, and more (in evaluation mode)
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---
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### 📌 How to Use:
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1. Use the **sidebar** to select a feature (e.g., Predict or Evaluate).
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2. In **Evaluate**, to check model accuracy.
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3. In **Predict**, choose an DR image in sample images to predict or upload a high-quality retinal image (JPEG or PNG).
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⚠️ *Note: This tool is not a diagnostic tool. This is an assistive tool for Doctors.*
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""")
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if __name__ == '__main__':
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main()
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