init

.streamlit/config.toml

@@ -0,0 +1,11 @@
+[server]
+headless = true
+enableCORS = false
+port = 8501
+enableXsrfProtection = false
+
+[browser]
+gatherUsageStats = false
+
+[global]
+disableWatchdogWarning = true

Dockerfile

@@ -9,13 +9,16 @@ RUN apt-get update && apt-get install -y \
     git \
     && rm -rf /var/lib/apt/lists/*
 
-COPY requirements.txt ./
-COPY src/ ./src/
+COPY requirements.txt /app
+COPY download_model.py /app
+COPY .streamlit/ /app/.streamlit/  
+COPY src/ /app/src/
 
-RUN pip3 install -r requirements.txt
+RUN pip3 install --no-cache-dir -r requirements.txt
+RUN python3 download_model.py
 
 EXPOSE 8501
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health || exit 1
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]

download_model.py

@@ -0,0 +1,10 @@
+from transformers import AutoModelForImageClassification, AutoImageProcessor
+
+model_name = "Asmaa111/diabetic-eye"
+save_path = "./diabetic_model"
+
+model = AutoModelForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+model.save_pretrained(save_path)
+processor.save_pretrained(save_path)

requirements.txt

@@ -1,3 +1,8 @@
 altair
 pandas
-streamlit
+streamlit
+torch
+transformers
+Pillow
+requests
+pdf2image

src/streamlit_app.py

@@ -1,40 +1,292 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import torch
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import requests
+from io import BytesIO
+from pdf2image import convert_from_bytes
+import json
+import os
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# تأكد أن Streamlit تستخدم مجلد .streamlit القابل للكتابة
+os.environ["STREAMLIT_HOME"] = "/app/.streamlit"
+
+# Load Lottie animation
+def load_lottiefile(filepath: str):
+    if filepath.startswith(('http://', 'https://')):
+        response = requests.get(filepath)
+        response.raise_for_status()
+        return response.json()
+    else:
+        with open(filepath, "r") as f:
+            return json.load(f)
+
+# Load the model and processor
+# model_path = "Asmaa111/diabetic-eye" # new version 
+model_path = "./diabetic_model" # for space
+# model_path = r"C:\Users\Milestone\dinov2-base-finetuned-eye"
+# model_path = r"AsmaaElnagger/Diabetic_RetinoPathy_detection" #  old version 
+
+
+@st.cache_resource
+def load_model():
+    
+    processor = AutoImageProcessor.from_pretrained(model_path)
+    model = AutoModelForImageClassification.from_pretrained(model_path)
+    model.eval()
+    return model, processor
+
+def predict(image, model, processor):
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = predictions.argmax().item()
+    predicted_label = model.config.id2label[predicted_class]
+    return predicted_label
+
+# App Config
+st.set_page_config(
+    page_title="Diabetic Eye Classifier",
+    page_icon="👁️",
+    layout="wide"
+)
+
+# Custom CSS with animations
+st.markdown("""
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
+<style>
+    @keyframes fadeIn {
+        from { opacity: 0; transform: translateY(20px); }
+        to { opacity: 1; transform: translateY(0); }
+    }
+    .section {
+        padding: 4rem 0;
+        animation: fadeIn 0.8s ease-out;
+    }
+    .hero {
+        background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
+        padding: 5rem 1rem;
+        border-radius: 10px;
+        margin-bottom: 3rem;
+    }
+    .feature-card {
+        padding: 2rem;
+        border-radius: 10px;
+        background: white;
+        box-shadow: 0 4px 6px rgba(0,0,0,0.1);
+        height: 100%;
+        transition: transform 0.3s ease;
+    }
+    .feature-card:hover {
+        transform: translateY(-5px);
+    }
+    .team-card {
+        text-align: center;
+        padding: 1.5rem;
+    }
+    .testimonial-card {
+        padding: 2rem;
+        background: white;
+        border-radius: 10px;
+        box-shadow: 0 4px 6px rgba(0,0,0,0.1);
+        margin-bottom: 1rem;
+    }
+    .icon {
+        font-size: 2.5rem;
+        margin-bottom: 1rem;
+        color: #4b6cb7;
+    }
+    .stButton>button {
+        transition: all 0.3s ease;
+    }
+    .stButton>button:hover {
+        transform: scale(1.02);
+    }
+    .medical-info {
+        background: #f0f8ff;
+        padding: 1.5rem;
+        border-radius: 10px;
+        margin: 1rem 0;
+    }
+    .image-section-prefix img{
+            height: 300px ! IMPORTANT;
+            max-width: 300px ! IMPORTANT;
+            text-align: center;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Hero Section with animation
+with st.container():
+    st.markdown('<div class="hero">', unsafe_allow_html=True)
+    col1, col2 = st.columns([2, 1])
+    with col1:
+        st.title("👁️ Advanced Diabetic Retinopathy Screening")
+        st.markdown("""
+        **Revolutionizing** early detection of diabetic eye disease with **AI-powered** analysis.
+        Get instant preliminary screening results from retinal images.
+        """)
+       
+    st.markdown('</div>', unsafe_allow_html=True)
+
+# Medical Information Section
+with st.container():
+    st.markdown('<div class="section">', unsafe_allow_html=True)
+    st.header("ℹ️ About Diabetic Retinopathy", anchor="about-dr")
+    
+    with st.expander("What is Diabetic Retinopathy?"):
+        st.markdown("""
+        Diabetic retinopathy is a diabetes complication that affects eyes. It's caused by damage to the blood vessels 
+        of the light-sensitive tissue at the back of the eye (retina). At first, diabetic retinopathy might cause no 
+        symptoms or only mild vision problems. Eventually, it can cause blindness.
+        """)
+    
+    with st.expander("Risk Factors"):
+        cols = st.columns(2)
+        with cols[0]:
+            st.markdown("""
+            - **Duration of diabetes**: The longer you have diabetes, the greater your risk
+            - **Poor blood sugar control**
+            - **High blood pressure**
+            - **High cholesterol**
+            """)
+        with cols[1]:
+            st.markdown("""
+            - **Pregnancy**
+            - **Tobacco use**
+            - **Being African-American, Hispanic or Native American**
+            """)
+    
+    with st.expander("Prevention Tips"):
+        st.markdown("""
+        - **Manage your diabetes**: Keep your blood sugar levels in target range
+        - **Monitor your blood sugar levels**
+        - **Keep blood pressure and cholesterol under control**
+        - **Quit smoking**
+        - **Pay attention to vision changes**
+        - **Have regular eye exams**
+        """)
+    st.markdown('</div>', unsafe_allow_html=True)
+
+# Features Section
+with st.container():
+    st.markdown('<div class="section">', unsafe_allow_html=True)
+    st.header("✨ Key Features", anchor="features")
+    cols = st.columns(3)
+    features = [
+        {"icon": "fa-brain", "title": "AI-Powered Analysis", 
+         "desc": "Our deep learning model provides accurate preliminary screening in seconds"},
+        {"icon": "fa-file-pdf", "title": "PDF Report Processing", 
+         "desc": "Upload medical reports and we'll extract and analyze the images"},
+        {"icon": "fa-shield-alt", "title": "Secure & Private", 
+         "desc": "HIPAA compliant processing with no data retention"}
+    ]
+    for i, col in enumerate(cols):
+        with col:
+            st.markdown(f"""
+            <div class="feature-card">
+                <div class="icon"><i class="fas {features[i]['icon']}"></i></div>
+                <h3>{features[i]['title']}</h3>
+                <p>{features[i]['desc']}</p>
+            </div>
+            """, unsafe_allow_html=True)
+    st.markdown('</div>', unsafe_allow_html=True)
+
+
+# How It Works Section
+with st.container():
+    st.markdown('<div class="section">', unsafe_allow_html=True)
+    st.header("🔍 How It Works", anchor="how-it-works")
+    steps = [
+        {"title": "1. Upload Image", "desc": "Provide a retinal image or PDF report"},
+        {"title": "2. AI Analysis", "desc": "Our model processes the image in seconds"},
+        {"title": "3. Get Results", "desc": "Receive preliminary screening results"}
+    ]
+    for step in steps:
+        with st.expander(step["title"]):
+            st.write(step["desc"])
+    st.markdown('</div>', unsafe_allow_html=True)
+
+
+# Classifier Section
+model, processor = load_model()
+
+with st.container():
+    st.markdown('<div class="section">', unsafe_allow_html=True)
+    st.header("📷 Try Our Classifier", anchor="classifier")
+    
+    col1, col2 = st.columns([1, 2])
+    with col1:
+        option = st.radio(
+            "Select input method:",
+            ("Upload Image/PDF", "Image URL"),
+            index=0
+        )
+
+        image = None
+        if option == "Upload Image/PDF":
+            uploaded_file = st.file_uploader(
+                "Choose file",
+                type=["jpg", "png", "jpeg", "pdf"],
+                label_visibility="collapsed"
+            )
+            if uploaded_file:
+                if uploaded_file.type == "application/pdf":
+                    images = convert_from_bytes(uploaded_file.read())
+                    if images:
+                        image = images[0]
+                else:
+                    image = Image.open(uploaded_file)
+        else:
+            url = st.text_input("Enter image URL", placeholder="https://example.com/image.jpg")
+            if url:
+                try:
+                    response = requests.get(url)
+                    image = Image.open(BytesIO(response.content))
+                except:
+                    st.error("Invalid URL or image")
+
+    with col2:
+        if image:
+            st.image(image, width=600)
+            if st.button("Analyze Image", type="primary"):
+                with st.spinner("Analyzing..."):
+                    prediction = predict(image, model, processor)
+                    st.success(f"**Prediction:** {prediction}")
+                    #st.balloons()
+        else:
+            st.info("Please upload an image or enter a URL to begin analysis")
+    st.markdown('</div>', unsafe_allow_html=True)
+
+# Medical Disclaimer
+with st.container():
+    st.markdown("""
+    <div class="medical-info">
+    <h4>Medical Disclaimer</h4>
+    <p>This tool provides preliminary screening only and is not a substitute for professional medical advice, diagnosis, or treatment. 
+    Always seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition.</p>
+    </div>
+    """, unsafe_allow_html=True)
+
+# Footer
+st.markdown("---")
+cols = st.columns(3)
+with cols[0]:
+    st.markdown("""
+    **Contact Us**  
+    <i class="fas fa-envelope"></i> [email protected]  
+    <i class="fas fa-phone"></i> (123) 456-7890
+    """, unsafe_allow_html=True)
+with cols[1]:
+    st.markdown("""
+    **Quick Links**  
+    [About DR](#about-dr)  
+    [Features](#features)  
+    [How It Works](#how-it-works)
+    """)
+with cols[2]:
+    st.markdown("""
+    © 2025 Diabetic Eye Classifier  
+    Medical AI Application
+    """)