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.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+CustomCNN_model.keras filter=lfs diff=lfs merge=lfs -text
+DenseNet121_model.keras filter=lfs diff=lfs merge=lfs -text
+MobileNetV2_model.keras filter=lfs diff=lfs merge=lfs -text
+VGG16_model.keras filter=lfs diff=lfs merge=lfs -text

CustomCNN_history.pkl

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CustomCNN_model.keras

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DenseNet121_history.pkl

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DenseNet121_model.keras

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MobileNetV2_history.pkl

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MobileNetV2_model.keras

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VGG16_history.pkl

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VGG16_model.keras

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app.py

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+import os
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+
+import streamlit as st
+import numpy as np
+import os
+import pickle
+import zipfile
+import matplotlib.pyplot as plt
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+
+# --- CONFIG ---
+st.set_page_config(page_title="Leukemia Subtype Detector", layout="centered")
+
+st.markdown("""
+    <div style='background-color:#57068c;padding:20px;border-radius:10px'>
+    <h2 style='color:white;text-align:center'>🧬 Leukemia Subtype Detection</h2>
+    <p style='color:white;text-align:center'>
+        Uses an ensemble of 4 models: <b>DenseNet121</b>, <b>MobileNetV2</b>, <b>VGG16</b>, and <b>Custom CNN</b>.<br>
+        Predicts: <i>Benign</i>, <i>Pre</i>, <i>Pro</i>, <i>Early</i>.
+    </p>
+    </div>
+""", unsafe_allow_html=True)
+
+# --- CONSTANTS ---
+IMG_HEIGHT, IMG_WIDTH = 224, 224
+CLASS_NAMES = ['Benign', 'Pre', 'Pro', 'Early']
+SAVE_DIR = 'saved_leukemia_ensemble'
+MODEL_ZIPS = {
+    "DenseNet121": "DenseNet121_model.zip",
+    "MobileNetV2": "MobileNetV2_model.zip",
+    "VGG16": "VGG16_model.zip",
+    "CustomCNN": "CustomCNN_model.zip"
+}
+ENSEMBLE_WEIGHTS = {
+    "DenseNet121": 0.28,
+    "MobileNetV2": 0.30,
+    "VGG16": 0.22,
+    "CustomCNN": 0.20
+}
+HISTORY_PATHS = {
+    name: os.path.join(SAVE_DIR, f"{name}_history.pkl") for name in MODEL_ZIPS
+}
+
+# --- UTIL FUNCTION ---
+def extract_model_if_needed(zip_path, output_path):
+    if not os.path.exists(output_path):
+        with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+            zip_ref.extractall(os.path.dirname(output_path))
+
+# --- LOAD MODELS ---
+@st.cache_resource
+def load_all_models():
+    models = {}
+    for name, zip_file in MODEL_ZIPS.items():
+        zip_path = os.path.join(SAVE_DIR, zip_file)
+        keras_path = zip_path.replace(".zip", ".keras")
+
+        extract_model_if_needed(zip_path, keras_path)
+
+        if os.path.exists(keras_path):
+            models[name] = load_model(keras_path, compile=False)
+        else:
+            st.warning(f"❌ Model not found: {keras_path}")
+    return models
+
+models = load_all_models()
+
+# --- UPLOAD IMAGE ---
+uploaded_file = st.file_uploader("📁 Upload a blood smear image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file:
+    st.image(uploaded_file, caption="Uploaded Image", use_container_width=True)
+    
+    if st.button("🔍 Enter"):
+        with st.spinner("⏳ Please wait while results are being computed..."):
+            try:
+                img = image.load_img(uploaded_file, target_size=(IMG_HEIGHT, IMG_WIDTH))
+                img_array = image.img_to_array(img) / 255.0
+                img_array = np.expand_dims(img_array, axis=0)
+
+                st.markdown("### 🧪 Model Predictions:")
+                col1, col2 = st.columns(2)
+                individual_preds = {}
+
+                for i, (name, model) in enumerate(models.items()):
+                    pred = model.predict(img_array)
+                    individual_preds[name] = pred
+                    cls = CLASS_NAMES[np.argmax(pred)]
+                    conf = pred[0][np.argmax(pred)]
+                    with [col1, col2][i % 2]:
+                        st.info(f"**{name}** ➜ `{cls}` ({conf:.2%})")
+
+                ensemble_pred = sum(ENSEMBLE_WEIGHTS[name] * pred for name, pred in individual_preds.items())
+                final_class = CLASS_NAMES[np.argmax(ensemble_pred)]
+                final_conf = float(np.max(ensemble_pred))
+
+                st.markdown(f"""
+                    <div style="background-color:#c6f6d5;padding:15px;border-radius:10px">
+                    <h4 style="color:#2f855a">✅ Ensemble Prediction: <b>{final_class}</b></h4>
+                    <p style="font-size:16px;color:#22543d">Confidence: <b>{final_conf:.2%}</b></p>
+                    </div>
+                """, unsafe_allow_html=True)
+
+                st.bar_chart({CLASS_NAMES[i]: float(ensemble_pred[0][i]) for i in range(4)})
+
+            except Exception as e:
+                st.error(f"⚠️ Error: {e}")
+else:
+    st.info("👈 Please upload an image to begin.")
+
+# --- OPTIONAL TRAINING VISUALIZATION ---
+st.markdown("---")
+st.subheader("📈 Model Training History")
+
+if st.checkbox("Show training curves"):
+    for name, path in HISTORY_PATHS.items():
+        if os.path.exists(path):
+            with open(path, "rb") as f:
+                hist = pickle.load(f)
+                acc = hist['accuracy']
+                val_acc = hist['val_accuracy']
+                loss = hist['loss']
+                val_loss = hist['val_loss']
+
+                fig, ax = plt.subplots(1, 2, figsize=(12, 4))
+                ax[0].plot(acc, label='Train Acc')
+                ax[0].plot(val_acc, label='Val Acc')
+                ax[0].set_title(f'{name} Accuracy')
+                ax[0].legend()
+
+                ax[1].plot(loss, label='Train Loss')
+                ax[1].plot(val_loss, label='Val Loss')
+                ax[1].set_title(f'{name} Loss')
+                ax[1].legend()
+
+                st.pyplot(fig)
+        else:
+            st.warning(f"No training history found for {name}")