Create app.py

app.py

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+import cv2
+import os
+import random
+import gradio as gr
+import numpy as np
+from tensorflow.keras.preprocessing.image import img_to_array
+from tensorflow.keras.models import load_model
+
+model = load_model('covid-model.h5')
+ex=['./examples/' + path for path in os.listdir('examples')]
+random.shuffle(ex)
+
+def predict_image(image_path):
+    try:
+        
+        img = cv2.imread(image_path)
+        img_array = img_to_array(img)
+        img_resized = cv2.resize(img_array, (224, 224))
+        prediction = model.predict(np.expand_dims(img_resized / 255.0, axis=0))
+        prediction = 'Normal' if prediction >= 0.5 else 'Covid'
+        return f'Prediction : {prediction}'
+    except Exception as e:
+        print(f"Error predicting image: {e}")
+
+# Define the interface
+def app():
+    title = "COVID-19 Detection using X-Ray"
+
+    gr.Interface(
+        title=title,
+        fn=predict_image,
+        inputs=gr.Image(type="filepath"),
+        outputs=gr.Textbox(),
+        examples=ex,
+    ).launch()
+
+# Run the app
+if __name__ == "__main__":
+    app()