Create app.py

app.py

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+import cv2
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+import gradio as gr
+
+# Load the trained model
+model = tf.keras.models.load_model("pneumonia_model.h5")
+
+# Define the prediction function
+def predict_xray(image):
+    # Convert PIL image to OpenCV format (numpy array)
+    image = np.array(image)
+    
+    # Resize image to 150x150 (as per your training)
+    image = cv2.resize(image, (150, 150))
+    
+    # Reshape and normalize
+    image = image.reshape(1, 150, 150, 3) / 255.0  # Normalization (if used in training)
+    
+    # Make prediction
+    prediction = model.predict(image)
+    prediction = prediction.argmax()  # Get class with highest probability
+    
+    # Class labels (adjust based on your dataset)
+    labels = ["Normal", "Pneumonia"]
+    
+    return labels[prediction]
+
+# Create Gradio UI
+iface = gr.Interface(
+    fn=predict_xray,
+    inputs=gr.Image(type="pil"),  # Accepts image input
+    outputs="text",  # Returns class label
+    title="Pneumonia Detection",
+    description="Upload a chest X-ray image, and the model will predict if the patient has pneumonia or is normal."
+)
+
+# Launch the app
+iface.launch()