Added bmi predictor app

app.py

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+import streamlit as st
+
+import os
+import numpy as np
+import pandas as pd
+from glob import glob
+import pickle
+
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_squared_error, mean_absolute_error
+from scipy.stats import pearsonr
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+
+from facenet_pytorch import MTCNN, InceptionResnetV1
+
+import warnings
+
+warnings.filterwarnings("ignore")
+
+device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+
+# If required, create a face detection pipeline using MTCNN:
+mtcnn = MTCNN(
+    image_size=160, margin=40, min_face_size=20,
+    thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True,
+    device=device
+)
+
+mtcnn2 = MTCNN(
+    image_size=160, margin=40, min_face_size=20,
+    thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=False,
+    device=device
+)
+
+# Create an inception resnet (in eval mode):
+resnet = InceptionResnetV1(pretrained='vggface2').eval().to(device)
+
+# Define the transformation to preprocess the images
+preprocess = transforms.Compose([
+    transforms.Resize((160, 160)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+])
+
+def extract_features(img):
+    img = img.convert('RGB')
+    face = mtcnn(img)
+    if face is None:
+        face = preprocess(img)
+    
+    img = torch.stack([face]).to(device)
+    
+    with torch.no_grad():
+        features = resnet(img)
+    
+    return features[0].cpu().numpy()
+
+
+with open("/app/models/lr.p", "rb") as f:
+    lr = pickle.load(f)
+
+img_file_buffer = st.camera_input("Take a picture")
+
+if img_file_buffer is not None:
+    # To read image file buffer as a PIL Image:
+    img = Image.open(img_file_buffer)
+    
+    detected_image = Image.fromarray(mtcnn2(img).numpy().transpose(1, 2, 0).astype(np.uint8))
+    st.image(detected_image, caption="Detected Face")
+    
+    embeddings = extract_features(img)
+    bmi = round(lr.predict([embeddings])[0], 2)
+    st.write(f"Your BMI is {bmi}")

requirements.txt

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+facenet-pytorch==2.5.3
+imgaug==0.4.0
+matplotlib==3.7.1
+numpy==1.23.5
+opencv-python==4.7.0.72
+pandas==1.5.3
+scikit-learn==1.2.2
+scipy==1.10.1
+seaborn==0.12.2
+streamlit==1.22.0
+torch==2.0.1
+torchvision==0.15.2
+tqdm==4.65.0