Added app.py for Pneumonia Detection

app.py

@@ -0,0 +1,36 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.preprocessing import image
+
+# Load the trained model
+model = tf.keras.models.load_model("pneumonia_model.keras")
+
+# Define the class labels
+class_labels = ["Normal", "Pneumonia"]
+
+# Function to preprocess image and make a prediction
+def predict_pneumonia(img):
+    img = img.resize((150, 150))  # Resize to match model input size
+    img = image.img_to_array(img)
+    img = np.expand_dims(img, axis=0)  # Add batch dimension
+    img = img / 255.0  # Normalize pixel values
+
+    prediction = model.predict(img)[0]
+    result = class_labels[int(prediction > 0.5)]  # Classify based on threshold
+
+    return f"Prediction: {result}"
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=predict_pneumonia,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Pneumonia Detection from Chest X-ray",
+    description="Upload a chest X-ray image to check for pneumonia.",
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch()
+