Update app.py

app.py

@@ -1,17 +1,12 @@
 import gradio as gr
-import torch
-from torchvision import models
-from tensorflow.keras.models import load_model
+import tensorflow as tf
 import numpy as np
 import os
 
-# os.system("tar -zxvf mycnn1.tar.gz")
-# os.system("tar -zxvf examples.tar.gz")
-
-model = load_model('model_cv.h5')
+model = tf.keras.models.load_model('model_cv.h5')
 
 def detect(img):
-    img = img.reshape(1,100,100,3)
+    img = img.reshape(1, 100, 100, 3)
     prediction = np.around(model.predict(img)[0], decimals=0)[0]
 
     if prediction == 1:
@@ -20,7 +15,7 @@ def detect(img):
     return "Pneumonia Not Detected!"
 
 
-input = gr.inputs.Image(shape=(100,100))
+input = gr.inputs.Image(shape=(100, 100))
 
 examples = ['examples/n1.jpeg', 'examples/n2.jpeg', 'examples/n3.jpeg', 'examples/n4.jpeg', 'examples/n5.jpeg',
             'examples/n6.jpeg', 'examples/n7.jpeg', 'examples/n8.jpeg', 'examples/p6.jpeg', 'examples/p7.jpeg',
@@ -31,6 +26,7 @@ title = "PneumoDetect: Pneumonia Detection from Chest X-Rays"
 iface = gr.Interface(fn=detect, inputs=input, outputs="text", examples=examples, examples_per_page=20, title=title)
 iface.launch(inline=False)
 
+
 # model_name = "Horus7/kaduce"  # Remplacez par le nom de votre modèle Hugging Face
 
 # # tokenizer = AutoTokenizer.from_pretrained(model_name)