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README.md

@@ -1,10 +1,10 @@
 ---
 title: BMIAPP
-emoji: 😻
-colorFrom: gray
-colorTo: yellow
+emoji: 📊
+colorFrom: yellow
+colorTo: gray
 sdk: streamlit
-sdk_version: 1.41.1
+sdk_version: 1.19.0
 app_file: app.py
 pinned: false
 ---

app.py

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+import cv2
+import streamlit as st
+from keras_vggface.vggface import VGGFace
+import tensorflow as tf
+from tensorflow.keras.applications import ResNet50
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import GlobalAveragePooling2D
+import numpy as np
+import pickle
+
+pickle_file_path = 'models/svm_model.pkl'
+
+with open(pickle_file_path, 'rb') as file:
+    svm_model = pickle.load(file)
+
+base_model = VGGFace(model='vgg16', include_top=False, input_shape=(224, 224, 3))
+x = base_model.output
+x = GlobalAveragePooling2D()(x)
+model = Model(inputs=base_model.input, outputs=x)
+
+# Function to preprocess the image
+def preprocess_image(img):
+    img = cv2.resize(img, (224, 224))
+    img = image.img_to_array(img)
+    img = np.expand_dims(img, axis=0)
+    img = img[0] # Remove the extra dimension
+    return img
+    
+def extract_features(image_array):
+    # img = np.squeeze(image_array, axis=0)
+    img = np.expand_dims(image_array, axis=0)
+    img = tf.keras.applications.resnet50.preprocess_input(img)
+    features = model.predict(img,verbose=0)
+    return features.flatten()
+
+# Function to predict BMI
+def predict_bmi(img):
+    pre_img = preprocess_image(img)
+    features = extract_features(pre_img)
+    features = features.reshape(1,-1)
+    pred = svm_model.predict(features)
+    return pred
+
+def main():
+    st.title("BMI Prediction from Camera Image")
+    st.write("This app predicts the BMI of a person from an image captured using the camera.")
+
+    # Capture an image from the camera using streamlit-media's camera_input function
+    img_file_buffer = st.camera_input("Take a picture")
+
+    if img_file_buffer is not None:
+        # Load the image data from the file buffer
+        file_bytes = np.asarray(bytearray(img_file_buffer.getvalue()), dtype=np.uint8)
+        img = cv2.imdecode(file_bytes, 1)
+
+        # Preprocess the image and predict BMI
+        bmi_label = predict_bmi(img)
+
+        # Display the predicted BMI
+        st.write("Predicted BMI:", str(bmi_label[0] - 5))
+
+if __name__ == '__main__':
+    main()

packages.txt

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+python3-opencv

requirements.txt

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+tensorflow==2.10.0
+opencv-python
+streamlit==1.22.0
+keras_applications
+keras_vggface @ git+https://github.com/rcmalli/keras-vggface.git
+numpy==1.23.5
+streamlit-webrtc
+scikit-learn