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app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+import tensorflow as tf 
+
+
+load_model = tf.keras.models.load_model('./resnet50_food_model')
+
+def predict(image_path):
+    img = Image.open(image_path)
+    img = img.resize((224, 224))
+    img = np.array(img)
+    img = np.expand_dims(img, 0)
+    pred = model.predict(img)
+    if pred > 0.5:
+        label = 'non-food'
+    else:
+        label = 'food'
+    return label
+
+gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(224, 224)), outputs=gr.outputs.Label(num_top_classes=2), enable_queue=True).launch()