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.gitattributes

@@ -33,3 +33,4 @@ 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
+survival_model.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,241 @@
+import streamlit as st
+import tensorflow as tf
+import numpy as np
+import joblib
+import json
+from PIL import Image
+import pandas as pd
+from lifelines import CoxPHFitter
+import huggingface_hub
+
+# ---------------------------------------------------
+# CONFIG
+# ---------------------------------------------------
+
+st.set_page_config(
+    page_title="Breast Cancer Survival Prediction",
+    page_icon="🧬",
+    layout="wide"
+)
+
+# CNN_MODEL_PATH = "best_breast_cancer_cnn.keras"
+CNN_MODEL_PATH = "hf://MohammedAH/BreastCancerPrediction"
+DNN_MODEL_PATH = "survival_model.keras"
+
+SCALER_PATH = "scaler.pkl"
+FEATURES_PATH = "features.json"
+
+DATASET_PATH = 'processed_breast_cancer_data(1).csv'
+TIME_COL = "Overall_Survival_Months"
+EVENT_COL = "Event"
+ID_COL = "Patient_ID"
+
+# ---------------------------------------------------
+# LOAD MODELS
+# ---------------------------------------------------
+
+@st.cache_resource
+def load_cnn():
+    return tf.keras.models.load_model(CNN_MODEL_PATH, compile=False)
+
+@st.cache_resource
+def load_dnn():
+    return tf.keras.models.load_model(DNN_MODEL_PATH, compile=False)
+
+# ---------------------------------------------------
+# LOAD SURVIVAL ASSETS (COMPUTE BRESLOW BASELINE)
+# ---------------------------------------------------
+
+@st.cache_resource
+def load_survival_assets():
+
+    scaler = joblib.load(SCALER_PATH)
+    features = json.load(open(FEATURES_PATH))
+
+    df = pd.read_csv(DATASET_PATH)
+
+    feature_df = df[features].copy()
+    feature_df["duration"] = df[TIME_COL]
+    feature_df["event"] = df[EVENT_COL]
+
+    cox = CoxPHFitter()
+    cox.fit(feature_df, duration_col="duration", event_col="event")
+
+    baseline = cox.baseline_cumulative_hazard_
+
+    breslow_times = baseline.index.values
+    breslow_H0 = baseline.values.flatten()
+
+    return scaler, features, breslow_times, breslow_H0
+
+
+cnn_model = load_cnn()
+dnn_model = load_dnn()
+scaler, feature_cols, breslow_times, breslow_H0 = load_survival_assets()
+
+# ---------------------------------------------------
+# IMAGE PREPROCESSING
+# ---------------------------------------------------
+
+def preprocess_image(image):
+
+    if image.mode != "L":
+        image = image.convert("L")
+
+    image = image.resize((224, 224))
+
+    img = np.array(image) / 255.0
+    img = img[np.newaxis, ..., np.newaxis]
+
+    return img
+
+# ---------------------------------------------------
+# CNN PREDICTION
+# ---------------------------------------------------
+
+def predict_cancer(image):
+
+    img = preprocess_image(image)
+
+    pred = cnn_model.predict(img, verbose=0)[0][0]
+
+    result = "Malignant" if pred > 0.5 else "Benign"
+
+    confidence = pred if pred > 0.5 else 1 - pred
+
+    return result, confidence, pred
+
+# ---------------------------------------------------
+# SURVIVAL FUNCTION
+# ---------------------------------------------------
+
+def survival_prob(risk, t):
+
+    idx = np.searchsorted(breslow_times, t, side="right") - 1
+
+    if idx < 0:
+        return 1.0
+
+    h0 = breslow_H0[idx]
+
+    return float(np.exp(-h0 * np.exp(risk)))
+
+# ---------------------------------------------------
+# SURVIVAL PREDICTION
+# ---------------------------------------------------
+
+def predict_survival(feature_values):
+
+    row = np.array([feature_values], dtype=np.float32)
+
+    row = scaler.transform(row)
+
+    risk = float(dnn_model.predict(row, verbose=0)[0][0])
+
+    s1 = survival_prob(risk, 12) * 100
+    s3 = survival_prob(risk, 36) * 100
+    s5 = survival_prob(risk, 60) * 100
+
+    return risk, s1, s3, s5
+
+# ---------------------------------------------------
+# UI
+# ---------------------------------------------------
+
+st.title("🧬 Breast Cancer AI Diagnosis & Survival System")
+
+st.markdown(
+"""
+This system integrates two AI models:
+
+• **CNN model** → detects tumor malignancy from medical images  
+• **Survival DNN** → predicts patient survival probabilities
+"""
+)
+
+tab1, tab2 = st.tabs(["🔬 Image Diagnosis", "📈 Survival Analysis"])
+
+# ---------------------------------------------------
+# TAB 1 : IMAGE PREDICTION
+# ---------------------------------------------------
+
+with tab1:
+
+    st.header("Tumor Image Classification")
+
+    uploaded = st.file_uploader(
+        "Upload Histopathology Image",
+        type=["png", "jpg", "jpeg"]
+    )
+
+    if uploaded:
+
+        image = Image.open(uploaded)
+
+        st.image(image, width=300)
+
+        if st.button("Analyze Image"):
+
+            result, conf, score = predict_cancer(image)
+
+            st.subheader("Prediction Result")
+
+            col1, col2 = st.columns(2)
+
+            col1.metric("Diagnosis", result)
+
+            col2.metric("Confidence", f"{conf*100:.2f}%")
+
+            st.write("Prediction Score:", round(score, 4))
+
+# ---------------------------------------------------
+# TAB 2 : SURVIVAL ANALYSIS
+# ---------------------------------------------------
+
+with tab2:
+
+    st.header("Patient Survival Prediction")
+
+    st.write("Enter patient clinical features")
+
+    inputs = []
+
+    cols = st.columns(3)
+
+    for i, f in enumerate(feature_cols):
+
+        value = cols[i % 3].number_input(
+            f,
+            value=0.0,
+            step=0.1
+        )
+
+        inputs.append(value)
+
+    if st.button("Predict Survival"):
+
+        risk, s1, s3, s5 = predict_survival(inputs)
+
+        st.subheader("Risk Score")
+
+        st.metric("Risk Score", round(risk, 4))
+
+        st.subheader("Survival Probability")
+
+        c1, c2, c3 = st.columns(3)
+
+        c1.metric("1-Year Survival", f"{s1:.1f}%")
+        c2.metric("3-Year Survival", f"{s3:.1f}%")
+        c3.metric("5-Year Survival", f"{s5:.1f}%")
+
+        if risk > 0:
+            st.error("High Risk Category")
+        else:
+            st.success("Low Risk Category")
+
+# ---------------------------------------------------
+# FOOTER
+# ---------------------------------------------------
+
+st.markdown("---")
+st.caption("AI-assisted clinical decision support system")

features.json

@@ -0,0 +1 @@
+["Age at Diagnosis", "Lymph nodes examined positive", "Tumor Size", "Mutation Count", "Nottingham prognostic index", "Tumor Stage_encoded", "Neoplasm Histologic Grade_encoded", "Cellularity_encoded", "ER Status_encoded", "HER2 Status_encoded", "Hormone Therapy_encoded", "Chemotherapy_encoded", "Inferred Menopausal State_encoded", "Type of Breast Surgery_encoded", "PR Status_encoded", "Integrative Cluster_target_enc", "tumor_size_log", "lymph_node_ratio", "age_stage_interaction", "favorable_biomarker", "high_risk_molecular", "high_prolif", "treatment_intensity", "early_event"]

scaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fdb09afa6aa89eb5c5a431717373bcb56645c64254757cfad2828ac0fce96032
+size 1191

survival_model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:da1f58915a573ecd0fd0126b501f39e760a628f2ecc9dd17c400dd24288b93d7
+size 265034