First Trial

app.py

@@ -1,7 +1,75 @@
-import gradio as gr
+import os
+import sys
+import pandas as pd
+import numpy as np
+from tqdm import tqdm
 
-def greet(name):
-    return "Hello " + name + "!!"
+import matplotlib.pyplot as plt
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from models import *
+
+import warnings
+warnings.filterwarnings("ignore")
+
+
+# preparing paths for functions
+current_directory = os.getcwd()
+all_images_path = '.'
+
+# get model directory
+model_name = '0508_saved_model_small_sample_vgg16_base.h5'
+model_folder = 'models'
+model_path = os.path.join(current_directory, model_folder, model_name)
+
+
+def model_init():
+    # set model
+    mode = 'predict' #'train' or 'predict'
+    model_type = 'vgg16'
+    model_tag = 'base'
+    model_id = '{:s}_{:s}'.format(model_type, model_tag)
+    # set params
+    bs = 8
+    epochs = 20
+    freeze_backbone = True
+    
+    # init model
+    model = FacePrediction(img_dir=all_images_path, model_type=model_type)
+    model.define_model(freeze_backbone=freeze_backbone)
+    # model.model.summary()
+    
+    # use our own load model function to load
+    model.load_weights(model_path)
+    return model
+    
+def model_predict_bmi(image):
+    # Save the uploaded image to a temporary file
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
+        tmp.write(image.read())
+        tmp_path = tmp.name
+    
+    model = model_init()
+    bmi = model.predict_external(tmp_path, show_img=False)
+    output_bmi = float(bmi[0][0])
+    output_bmi = np.round(output_bmi, 3)
+    
+    # Remove the temporary file
+    os.remove(tmp_path)
+    
+    return output_bmi
+
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=model_predict_bmi,
+    inputs="upload",
+    outputs="text",
+    title="BMI Prediction",
+    description="Upload an image and get the predicted BMI.",
+    examples=["lion.jpg", "cheetah.jpg"])
+
+# Run the interface
 iface.launch()