Add Application file

.gitignore

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+*__pycache__
+logs/
+medeyecare_venv/
+*.egg-info
+*.DS_Store

app.py

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+import streamlit as st
+import requests
+import os
+import shutil
+from pathlib import Path
+
+# Define API endpoint
+API_URL = "https://nagur-shareef-shaik-retinal-health-diagnostics.hf.space/predict"
+SPACE_URL = "https://nagur-shareef-shaik-medeyecare.hf.space/"
+
+# Ensure uploads directory exists
+UPLOAD_FOLDER = "uploads"
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+
+###### Sidebar ######
+
+# Custom CSS to make the sidebar fixed and adjust width
+st.markdown(
+    """
+    <style>
+        [data-testid="stSidebar"] {
+            width: 750px !important;  /* Adjust width as needed */
+            background-color: #f8f9fa; /* Light grey background */
+        }
+        [data-testid="stSidebarNav"] {
+            position: fixed;
+            width: 750px;
+        }
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+st.sidebar.header("Intelligent Application for Retinal Diseases Diagnosis")
+st.sidebar.image("images/rhd_img_1.jpeg", width=1250)
+
+st.sidebar.markdown("""
+### Get AI-Powered Diagnosis Report  
+
+1. **Enter Patient Details**  
+   - Name, Age, Gender, Contact Information, Address  
+   - Select Diagnosis Task (**General, Retinopathy, DME, Cataracts**)
+        - **General:** Diagnoses Diabetic Retinopathy, Cataracts, Glaucoma, & Normal conditions from retinal fundus images.
+        - **Retinopathy:** Detects 7 retinopathy severity grades (No DR, Mild NPDR, Moderate NPDR, Severe NPDR, Very Severe NPDR, PDR, and Advanced PDR) from retinal scans.
+        - **DME:** Identifies the presence of Diabetic Macular Edema (DME) from retinal scans.
+        - **Cataracts:** Diagnoses Cataracts from digital eye images.
+   - Add Medical Notes (if any)  
+
+2. **Upload Retinal Scan**  
+   - Accepts **JPG, JPEG, PNG** formats  
+   - Ensure clear, high-quality images  
+
+3. **Submit for AI Analysis**  
+   - Click **"Submit Request"** to send details to AI model  
+   - AI processes the image and generates a diagnosis  
+
+4. **View Results Instantly**  
+   - AI-powered diagnosis displayed with insights  
+   - Download or share the report if needed  
+
+""")
+
+# Create two columns
+col1, col2 = st.columns([1, 3])  # Adjust the width ratio as needed
+
+with col1:
+    st.image("images/rhd-logo.png", width=300)  # Adjust width as needed
+
+with col2:
+    st.title("Retinal Health Diagnostics")
+
+# User input fields
+task = st.selectbox("Select Task", ["general", "retinopathy", "dme", "cataracts"], index=0)
+name = st.text_input("Name")
+a, b, c = st.columns([1, 1, 1])
+with a:
+    age = st.text_input("Age")
+with b:
+    gender = st.selectbox("Gender", ["Male", "Female", "Other"], index=0)
+with c:
+    phone = st.text_input("Phone")
+email = st.text_input("Email")
+address = st.text_area("Address")
+med_notes = st.text_area("Medical Notes")
+
+# Image upload
+uploaded_file = st.file_uploader("Upload Retinal Image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file:
+    file_path = os.path.join(Path(UPLOAD_FOLDER), uploaded_file.name)
+    # Save file in Hugging Face Space storage
+    with open(file_path, "wb") as f:
+        f.write(uploaded_file.getbuffer())
+
+    # Generate the public URL of the saved image
+    image_url = f"{SPACE_URL}/{UPLOAD_FOLDER}/{uploaded_file.name}"
+    
+    st.success(f"Image uploaded! Accessible at: {image_url}")
+    st.image(image_url, caption="Uploaded Image")
+
+# Submit button
+if st.button("Submit Request"):
+    if not uploaded_file:
+        st.error("Please upload an image.")
+    else:
+        # Create request payload
+        payload = {
+            "task": task,
+            "img_path": str(file_path),
+            "name": name,
+            "age": age,
+            "gender": gender,
+            "address": address,
+            "phone": phone,
+            "email": email,
+            "med_notes": med_notes,
+        }
+        
+        try:
+            response = requests.post(API_URL, json=payload)
+            if response.status_code == 200:
+                st.success("Diagnosis Complete!")
+
+                predicted_class = response.json().get("test_results", {}).get("predicted_class", None)
+                confidence = response.json().get("test_results", {}).get("confidence", None)
+                attention_map = response.json().get("test_results", {}).get("attention_map", None)
+
+                st.image(attention_map)
+                st.write(f"Result: **{predicted_class}** with confidance {confidence*100} %")
+                # st.json(response.json())
+            else:
+                st.error(f"Error {response.status_code}: {response.text}")
+        except requests.exceptions.RequestException as e:
+            st.error(f"Request failed: {e}")

requirements.txt

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+numpy
+pandas
+matplotlib
+tqdm
+scikit-learn
+opencv-python
+tensorflow
+streamlit
+uvicorn
+fastapi

setup.py

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+from setuptools import setup, find_packages
+from typing import List
+
+HYPEN_E_DOT='-e .'
+def get_requirements(filepath:str) -> List[str]:
+    with open(filepath) as file:
+        requirements = file.readlines()
+        requirements = [req.replace("\n", "") for req in requirements]
+    if HYPEN_E_DOT in requirements:
+        requirements.remove(HYPEN_E_DOT)
+
+    return requirements
+
+setup(
+    name='medeyecare',
+    version='0.1.0',
+    description='Retinal Diseases Classification based on Guided Context Gating Attention',
+    author='Nagur Shareef Shaik',
+    author_email='shaiknagurshareef6@gmail.com',
+    packages=find_packages(),
+    install_requires=get_requirements('requirements.txt')
+)