Update app.py

app.py

@@ -1,36 +1,39 @@
-import tensorflow as tf
-import keras
 import gradio as gr
 import numpy as np
 import cv2
-import os
+import tensorflow as tf
 
-model=tf.keras.models.load_model('model.h5')
+# Carregar o modelo
+model = tf.keras.models.load_model('model.h5')
 
-def predict_pneumonia(image):
+def preprocess_image(image):
     resized_img = cv2.resize(image, (180, 180))
-    img_array = np.array(resized_img).reshape((1, 180, 180, 3))  
+    img_array = np.array(resized_img).reshape((1, 180, 180, 3))
+    return img_array
 
+def predict_pneumonia(image):
+    img_array = preprocess_image(image)
     prediction = model.predict(img_array)[0][0]
-    pneumonia_percent = prediction*1
-    normal_percent = (1 - prediction)*1
-    return {"Pneumonia ": pneumonia_percent, "Normal ": normal_percent}
+    pneumonia_percent = prediction * 100
+    normal_percent = (1 - prediction) * 100
+    return {"Pneumonia": pneumonia_percent, "Normal": normal_percent}
 
 inputs = gr.inputs.Image(shape=(180, 180))
 outputs = gr.outputs.Label(num_top_classes=2)
-gradio_interface = gr.Interface(fn=predict_pneumonia, inputs=inputs, outputs=outputs, 
-                                title="Classification of pneumonia in chest X-ray",                                
-                                #description = "A simple app to classify chest X-ray images into normal and pneumonia and show the percentage of each",                                                           
-                                examples = ["person1946_bacteria_4875.jpeg", "person1952_bacteria_4883.jpeg", "NORMAL2-IM-1427-0001.jpeg", "NORMAL2-IM-1431-0001.jpeg"],
-                                article = "<p style='text-align: center'>Lior Cohen & Arad Peleg | Final Project 2023</p>"
-                                "<p style='text-align: center'>Supervisor: Dr. Dima Alberg</p>",
-                                theme = gr.themes.Monochrome(),)
 
-# gr.themes.Soft() כחול
-# gr.themes.Monochrome() שחור
-# gr.themes.Glass() אפור
+gradio_interface = gr.Interface(
+    fn=predict_pneumonia,
+    inputs=inputs,
+    outputs=outputs,
+    title="Classificação de Pneumonia em Raios-X de Tórax",
+    description="Esta aplicação classifica imagens de raios-X de tórax em pneumonia e normal.",
+    examples=[
+        ["person1946_bacteria_4875.jpeg"],
+        ["person1952_bacteria_4883.jpeg"],
+        ["NORMAL2-IM-1427-0001.jpeg"],
+        ["NORMAL2-IM-1431-0001.jpeg"]
+    ],
+    theme="default"
+)
 
 gradio_interface.launch()
-# share=True
-# live=True
-# enable_queue=True