Update app.py

app.py

@@ -1,18 +1,22 @@
 import gradio as gr
-from transformers import pipeline
+import cv2
+import numpy as np
+from PIL import Image
+from keras.preprocessing import image
+import tensorflow as tf
+#predict function
+def predict(img):
+    print("hi")
 
-pipeline = pipeline(task="image-classification", model="sarth123/dbrdetectionmodel")
-
-def predict(input_img):
-    predictions = pipeline(input_img)
-    return input_img, {p["label"]: p["score"] for p in predictions} 
-
-gradio_app = gr.Interface(
-    predict,
-    inputs=gr.Image(label="Retinopathy score", sources=['upload', 'webcam'], type="pil"),
-    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
-    title="Retinopathy score",
-)
-
-if __name__ == "__main__":
-    gradio_app.launch()
+    model = tf.keras.models.load_model(r"model_adv.keras") 
+    print("hi")
+    Retina_classes = ['DR', 'No_DR']
+    img = np.resize(img,(299,299,3))
+    img = np.expand_dims(img, axis=0)
+    prediction=model.predict(img)[0]
+    print(prediction)
+    return {Retina_classes[i]: float(prediction[i]) for i in range(2)}
+#Just four line implementation
+image = gr.Image(label="Upload Here")
+label = gr.Label(num_top_classes=2)
+gr.Interface(fn=predict, inputs="image", outputs=label).launch()