Uploaded app.py and requirements

For classifying into three classes

app.py

@@ -1,7 +1,38 @@
-import gradio as gr
-
-def greet(name):
-    return "Hello " + name + "!!"
-
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+import gradio as gr
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+
+# Load your trained model
+model = load_model('my_modelled.h5')
+
+# Define the class labels
+class_labels = ['DR', 'DME', 'NONE']
+
+def classify_image(img):
+    # Preprocess the image for the model
+    img = img.resize((256, 256))  # Resize image to match model input
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = img_array / 255.0  # Normalize the image
+
+    # Predict the class
+    prediction = model.predict(img_array)
+    class_index = np.argmax(prediction)
+    class_label = class_labels[class_index]
+    
+    return class_label
+
+def create_interface():
+    # Define the Gradio interface
+    iface = gr.Interface(
+        fn=classify_image,
+        inputs=gr.inputs.Image(shape=(256, 256)),
+        outputs=gr.outputs.Label(num_top_classes=3),
+        live=True
+    )
+    return iface
+
+if __name__ == "__main__":
+    interface = create_interface()
+    interface.launch()

requirements.txt

@@ -0,0 +1,2 @@
+tensorflow
+gradio