Create app.py

app.py

@@ -0,0 +1,33 @@
+import gradio as gr
+import onnxruntime as ort
+import numpy as np
+from PIL import Image
+
+# Load ONNX model
+onnx_model_path = "your_model.onnx"  # Replace with your ONNX model file name
+ort_session = ort.InferenceSession(onnx_model_path)
+
+def preprocess_image(image):
+    image = image.resize((224, 224))  # Adjust size according to your model
+    image = np.array(image).astype(np.float32) / 255.0  # Normalize
+    image = np.expand_dims(image, axis=0).transpose(0, 3, 1, 2)  # Adjust shape
+    return image
+
+def predict(image):
+    input_tensor = preprocess_image(image)
+    ort_inputs = {ort_session.get_inputs()[0].name: input_tensor}
+    ort_outs = ort_session.run(None, ort_inputs)
+    prediction = np.argmax(ort_outs[0])
+    return "Infected" if prediction == 1 else "Uninfected"
+
+# Create Gradio Interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Malaria Detection",
+    description="Upload a blood cell image to check if it is infected or not."
+)
+
+# Launch app
+iface.launch()