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hf_requirements.txt

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+# Hugging Face Spaces Requirements
+# Minimal dependencies for deployment
+
+# PyTorch (CPU version for HF Spaces)
+--extra-index-url https://download.pytorch.org/whl/cpu
+torch==2.1.0
+torchvision==0.16.0
+
+# Core
+pillow>=10.0.0
+numpy>=1.24.0
+
+# Model Interpretability
+grad-cam>=1.4.0
+
+# Web UI
+streamlit>=1.28.0

streamlit_app.py

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+"""
+Streamlit Web UI for Pneumonia Detection.
+
+Run with: streamlit run app/streamlit_app.py
+"""
+
+import sys
+from pathlib import Path
+
+# Add project root to path
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+import streamlit as st
+import torch
+from PIL import Image
+import time
+
+from src.config import CHECKPOINT_PATH, CLASS_NAMES
+from src.model import create_model, get_device
+from src.predict import load_model, predict_image
+from src.gradcam import generate_gradcam
+
+# =============================================================================
+# Page Configuration
+# =============================================================================
+
+st.set_page_config(
+    page_title="Pneumonia Detection",
+    page_icon="🫁",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# =============================================================================
+# Custom CSS
+# =============================================================================
+
+st.markdown("""
+<style>
+    .main-header {
+        font-size: 2.5rem;
+        font-weight: bold;
+        color: #1E88E5;
+        text-align: center;
+        margin-bottom: 0.5rem;
+    }
+    .sub-header {
+        font-size: 1.1rem;
+        color: #666;
+        text-align: center;
+        margin-bottom: 2rem;
+    }
+    .prediction-box {
+        padding: 1.5rem;
+        border-radius: 10px;
+        text-align: center;
+        margin: 1rem 0;
+    }
+    .prediction-normal {
+        background-color: #E8F5E9;
+        border: 2px solid #4CAF50;
+    }
+    .prediction-pneumonia {
+        background-color: #FFEBEE;
+        border: 2px solid #F44336;
+    }
+    .confidence-text {
+        font-size: 1.2rem;
+        font-weight: bold;
+    }
+    .metric-card {
+        background-color: #f8f9fa;
+        padding: 1rem;
+        border-radius: 8px;
+        text-align: center;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# =============================================================================
+# Model Loading (Cached)
+# =============================================================================
+
+@st.cache_resource
+def load_model_cached():
+    """Load model once and cache it."""
+    device = get_device()
+    model = create_model(pretrained=False, freeze_backbone=False, device=device)
+    model = load_model(model, CHECKPOINT_PATH, device)
+    return model, device
+
+
+# =============================================================================
+# Sidebar
+# =============================================================================
+
+with st.sidebar:
+    st.image("https://img.icons8.com/fluency/96/lungs.png", width=80)
+    st.title("About")
+
+    st.markdown("""
+    This application uses deep learning to detect **pneumonia** from chest X-ray images.
+
+    **Model:** EfficientNet-B0
+    **Accuracy:** 90.5%
+    **Recall:** 98.2%
+    """)
+
+    st.divider()
+
+    st.subheader("How to Use")
+    st.markdown("""
+    1. Upload a chest X-ray image
+    2. Click **Analyze Image**
+    3. View prediction and Grad-CAM
+    """)
+
+    st.divider()
+
+    st.subheader("Model Metrics")
+    col1, col2 = st.columns(2)
+    with col1:
+        st.metric("Accuracy", "90.5%")
+        st.metric("Precision", "88.0%")
+    with col2:
+        st.metric("Recall", "98.2%")
+        st.metric("F1 Score", "92.8%")
+
+    st.divider()
+
+    st.markdown("""
+    **Links:**
+    [GitHub Repository](#) | [Live Demo](#)
+
+    ---
+    *Built with PyTorch & Streamlit*
+    """)
+
+# =============================================================================
+# Main Content
+# =============================================================================
+
+# Header
+st.markdown('<p class="main-header">🫁 Pneumonia Detection from Chest X-Rays</p>', unsafe_allow_html=True)
+st.markdown('<p class="sub-header">Upload a chest X-ray image to detect pneumonia using AI</p>', unsafe_allow_html=True)
+
+# Load model
+try:
+    model, device = load_model_cached()
+    model_loaded = True
+except Exception as e:
+    st.error(f"Failed to load model: {e}")
+    model_loaded = False
+
+if model_loaded:
+    # Create columns for layout
+    col1, col2 = st.columns([1, 1])
+
+    with col1:
+        st.subheader("📤 Upload Image")
+
+        uploaded_file = st.file_uploader(
+            "Choose a chest X-ray image",
+            type=["jpg", "jpeg", "png"],
+            help="Supported formats: JPG, JPEG, PNG"
+        )
+
+        # Sample images section
+        st.markdown("---")
+        st.markdown("**Or try a sample image:**")
+
+        sample_col1, sample_col2 = st.columns(2)
+
+        use_sample = None
+        with sample_col1:
+            if st.button("🟢 Normal Sample", width="stretch"):
+                use_sample = "normal"
+        with sample_col2:
+            if st.button("🔴 Pneumonia Sample", width="stretch"):
+                use_sample = "pneumonia"
+
+        # Load sample image if selected
+        if use_sample == "normal":
+            sample_path = Path("data/raw/test/NORMAL/IM-0001-0001.jpeg")
+            if sample_path.exists():
+                uploaded_file = sample_path
+        elif use_sample == "pneumonia":
+            sample_path = Path("data/raw/test/PNEUMONIA/person1_virus_6.jpeg")
+            if sample_path.exists():
+                uploaded_file = sample_path
+
+    with col2:
+        st.subheader("🔍 Analysis Results")
+        results_placeholder = st.empty()
+
+    # Process image if uploaded
+    if uploaded_file is not None:
+        # Load image
+        if isinstance(uploaded_file, Path):
+            image = Image.open(uploaded_file).convert("RGB")
+            st.session_state['image_source'] = str(uploaded_file)
+        else:
+            image = Image.open(uploaded_file).convert("RGB")
+            st.session_state['image_source'] = uploaded_file.name
+
+        # Display uploaded image
+        with col1:
+            st.image(image, caption="Uploaded X-Ray", width="stretch")
+
+        # Analyze button
+        with col1:
+            analyze_button = st.button("🔬 Analyze Image", type="primary", width="stretch")
+
+        if analyze_button:
+            with col2:
+                with st.spinner("Analyzing image..."):
+                    # Run prediction
+                    start_time = time.time()
+                    pred_class, confidence = predict_image(model, image, device)
+                    inference_time = (time.time() - start_time) * 1000
+
+                    # Generate Grad-CAM
+                    cam_image, _, _, original = generate_gradcam(model, image, device)
+
+                # Display results
+                if pred_class == "PNEUMONIA":
+                    st.markdown(f"""
+                    <div class="prediction-box prediction-pneumonia">
+                        <h2 style="color: #F44336; margin: 0;">⚠️ PNEUMONIA DETECTED</h2>
+                        <p class="confidence-text">Confidence: {confidence:.1%}</p>
+                    </div>
+                    """, unsafe_allow_html=True)
+                else:
+                    st.markdown(f"""
+                    <div class="prediction-box prediction-normal">
+                        <h2 style="color: #4CAF50; margin: 0;">✅ NORMAL</h2>
+                        <p class="confidence-text">Confidence: {confidence:.1%}</p>
+                    </div>
+                    """, unsafe_allow_html=True)
+
+                # Metrics row
+                m1, m2, m3 = st.columns(3)
+                with m1:
+                    st.metric("Prediction", pred_class)
+                with m2:
+                    st.metric("Confidence", f"{confidence:.1%}")
+                with m3:
+                    st.metric("Time", f"{inference_time:.0f}ms")
+
+                # Grad-CAM visualization
+                st.markdown("---")
+                st.subheader("🔥 Grad-CAM Visualization")
+                st.caption("Highlighted regions show areas that influenced the prediction")
+
+                gcol1, gcol2 = st.columns(2)
+                with gcol1:
+                    st.image(original, caption="Original", width="stretch")
+                with gcol2:
+                    st.image(cam_image, caption="Grad-CAM Heatmap", width="stretch")
+
+                # Disclaimer
+                st.warning("""
+                **Disclaimer:** This tool is for educational purposes only and should not be used
+                for medical diagnosis. Always consult a qualified healthcare professional.
+                """)
+
+else:
+    st.error("Model could not be loaded. Please check the model file exists.")
+
+# =============================================================================
+# Footer
+# =============================================================================
+
+st.markdown("---")
+st.markdown(
+    "<p style='text-align: center; color: #888;'>Built with ❤️ using PyTorch, EfficientNet-B0, and Streamlit</p>",
+    unsafe_allow_html=True
+)