Update app.py

app.py

@@ -1,53 +1,36 @@
+import tensorflow as tf
+import keras
 import gradio as gr
-import torch
-from torchvision import transforms
-
-# Load the model
-model = torch.hub.load('pytorch/vision:v0.9.0', 'resnet101', pretrained=True)
-model.eval()
-
-# Define class names
-class_names = ["normal", "pneumonia"]
-
-# Define preprocessing function
-preprocess = transforms.Compose([
-    transforms.Resize(256),
-    transforms.CenterCrop(224),
-    transforms.ToTensor(),
-    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-])
-
-# Define predict function
-def predict(img):
-    # Preprocess the image
-    img = preprocess(img)
-
-    # Add batch dimension
-    img = img.unsqueeze(0)
-
-    # Make prediction
-    with torch.no_grad():
-        outputs = model(img)
-        _, predicted_idx = torch.max(outputs, 1)
-        confidence = torch.nn.functional.softmax(outputs, dim=1)[0]
-
-    # Format prediction results
-    prediction = {
-        class_names[0]: float(confidence[0]),
-        class_names[1]: float(confidence[1])
-    }
-
-    return prediction
-
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.inputs.Image(type="pil", label="Input Image"),
-    outputs=gr.outputs.Label(num_top_classes=2, label="Predicted Class"),
-    title="Pneumonia Detector 👁",
-    description="A ResNet101 computer vision model to detect pneumonia",
-    article="Please add a chest X-Ray image"
-)
-
-# Launch the interface
-iface.launch()
+import numpy as np
+import cv2
+import os
+
+model=tf.keras.models.load_model('model.h5')
+
+def predict_pneumonia(image):
+    resized_img = cv2.resize(image, (180, 180))
+    img_array = np.array(resized_img).reshape((1, 180, 180, 3))  
+
+    prediction = model.predict(img_array)[0][0]
+    pneumonia_percent = prediction*1
+    normal_percent = (1 - prediction)*1
+    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.launch()
+# share=True
+# live=True
+# enable_queue=True