updated the file

app.py

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+import os
+# Setting environment variables
+os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"
+os.environ["KERAS_BACKEND"] = "jax"
+import streamlit as st
+import cv2
+import numpy as np
+from PIL import Image
+import keras
+import warnings
+warnings.filterwarnings("ignore")
+
+
+def resize_for_inference(input_image):
+    # Convert uploaded image to NumPy array
+    image = np.array(input_image)
+
+    # Convert the image to RGB format (for compatibility with GrabCut)
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    # Initialize the mask for GrabCut
+    mask = np.zeros(image.shape[:2], np.uint8)
+
+    # Define the rectangle for the GrabCut algorithm
+    height, width = image.shape[:2]
+    rect = (10, 10, width - 20, height - 20)  # Slightly smaller than the full image
+
+    # Allocate memory for the GrabCut algorithm
+    bgd_model = np.zeros((1, 65), np.float64)
+    fgd_model = np.zeros((1, 65), np.float64)
+
+    # Apply the GrabCut algorithm
+    cv2.grabCut(image_rgb, mask, rect, bgd_model, fgd_model, 5, cv2.GC_INIT_WITH_RECT)
+
+    # Convert the mask to binary (foreground is white, background is black)
+    binary_mask = np.where((mask == 2) | (mask == 0), 0, 255).astype('uint8')
+
+    # Resize the binary mask to the desired shape (960x720)
+    resized_mask = cv2.resize(binary_mask, (720, 960), interpolation=cv2.INTER_AREA)
+
+    # Further resize the mask to target size (224x224)
+    target_size = (224, 224)
+    final_resized_mask = cv2.resize(resized_mask, target_size, interpolation=cv2.INTER_AREA)
+
+    final_resized_mask = np.expand_dims(final_resized_mask, axis=-1)
+
+    return final_resized_mask
+
+# Streamlit UI
+st.title("Body Measurement Predictor")
+st.write("Upload an image to predict body measurements.")
+uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+# Check if the model is already loaded in the session state
+if 'loaded_model' not in st.session_state:
+    with st.spinner("Model is getting loaded. Please wait..."):
+        try:
+            # Load the pre-trained model only once
+            st.session_state.loaded_model = keras.saving.load_model("hf://datasciencesage/bodym_measurement_model")
+            st.success("Model loaded successfully!")
+        except Exception as e:
+            st.error(f"Error loading model: {e}")
+
+# Show the image upload box immediately
+
+if uploaded_image is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_image)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Preprocess the image and make predictions using the pre-loaded model
+    with st.spinner("DOING IMAGE PREPROCESSING.....PLEASE WAIT..."):
+        resized_image = resize_for_inference(image)
+        single_image_expanded = np.expand_dims(resized_image, axis=0)
+    
+    # Make predictions using the loaded model
+    with st.spinner("INFERENCE IS BEING DONE.....PLEASE WAIT..."):
+        single_image_expanded = np.expand_dims(resized_image, axis=0)
+
+        # Make predictions using the loaded model
+        predicted_values = st.session_state.loaded_model.predict(single_image_expanded)[0]
+    columns = ['ankle', 'arm-length', 'bicep', 'calf', 'chest',
+               'forearm', 'height', 'hip', 'leg-length', 'shoulder-breadth',
+               'shoulder-to-crotch', 'thigh', 'waist', 'wrist']
+
+    # Display the results
+
+
+    st.write("Predicted Body Measurements:")
+    for body_type, measurement in zip(columns, predicted_values):
+        st.write(f"{body_type}: {measurement:.2f} cm")