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@@ -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
+src/bmi_model_gender.keras filter=lfs diff=lfs merge=lfs -text

src/bmi_model_gender.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dfcdb0ee5d382409c1724689e32aff643eb44aa98972f3c2c7b17e90baa2a276
+size 149871248

src/streamlit_app.py

@@ -1,40 +1,91 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import numpy as np
+from PIL import Image
+import cv2
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import img_to_array
+from tensorflow.keras.applications.vgg19 import preprocess_input
+from mtcnn import MTCNN
+import joblib
+import tensorflow.keras.backend as K
+
+# === Custom Metric ===
+def pearson_corr(y_true, y_pred):
+    x = y_true - K.mean(y_true)
+    y = y_pred - K.mean(y_pred)
+    return K.sum(x * y) / (K.sqrt(K.sum(K.square(x))) * K.sqrt(K.sum(K.square(y))) + K.epsilon())
+
+# === Download model
+import gdown
+url = "https://drive.google.com/file/d/1XevL2OQH6i6vTRnK7GUc49x4OD6IEFxz/view?usp=sharing"  # NOT the share link
+output = "bmi_model_gender.keras"
+gdown.download(url, output, quiet=False)
+
+# === Load model and scaler ===
+# model = tf.keras.models.load_model("bmi_model_gender.keras", custom_objects={'pearson_corr': pearson_corr})
+model = tf.keras.models.load_model(
+    output,
+    custom_objects={'pearson_corr': pearson_corr}  # Include if used
+)
+scaler = joblib.load("./Data/label_scaler.pkl")
+
+# === Sidebar ===
+st.sidebar.title("Settings")
+gender_option = st.sidebar.radio("Select Gender", ['Male', 'Female'])
+gender_value = 1.0 if gender_option.lower() == 'male' else 0.0
+
+# === Main UI ===
+st.title("Face-based BMI Prediction App")
+st.markdown("Upload a facial image to estimate BMI using a deep learning model.")
+
+# === Face Detector ===
+detector = MTCNN()
+
+def detect_and_align_face(image):
+    image_rgb = np.array(image.convert('RGB'))
+    results = detector.detect_faces(image_rgb)
+    if not results:
+        return None, "❌ No face detected."
+
+    box = results[0]['box']
+    keypoints = results[0]['keypoints']
+    x, y, w, h = box
+    x, y = max(0, x), max(0, y)
+
+    left_eye = keypoints['left_eye']
+    right_eye = keypoints['right_eye']
+    dx, dy = right_eye[0] - left_eye[0], right_eye[1] - left_eye[1]
+    angle = np.degrees(np.arctan2(dy, dx))
+    center = np.mean([left_eye, right_eye], axis=0)
+    eyes_center = (float(center[0]), float(center[1]))
+
+    M = cv2.getRotationMatrix2D(eyes_center, angle, 1.0)
+    aligned = cv2.warpAffine(image_rgb, M, (image_rgb.shape[1], image_rgb.shape[0]), flags=cv2.INTER_CUBIC)
+
+    face = aligned[y:y+h, x:x+w]
+    face_resized = cv2.resize(face, (224, 224))
+    return face_resized, None
+
+# === Upload ===
+uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png", "bmp"], label_visibility="collapsed")
+
+# === Result & Image Display ===
+if uploaded_file:
+    image = Image.open(uploaded_file)
+    face_img, error = detect_and_align_face(image)
+
+    if error:
+        st.error(error)
+    else:
+        img_array = img_to_array(face_img).astype(np.float32)
+        img_array = preprocess_input(img_array)
+        img_batch = np.expand_dims(img_array, axis=0)
+        gender_batch = np.array([[gender_value]], dtype=np.float32)
+
+        bmi_scaled = model.predict([img_batch, gender_batch])[0][0]
+        bmi = scaler.inverse_transform([[bmi_scaled]])[0][0]
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+        st.success(f"🎯 **Predicted BMI: {bmi:.2f}**")
+        st.image(image, caption="Uploaded Image", use_container_width=True)
+else:
+    st.markdown("📤 *Upload an image above to receive a prediction.*")