Delete app.py

app.py

@@ -1,140 +0,0 @@
-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}")