Upload app.py

app.py

@@ -1,7 +1,31 @@
 import gradio as gr
+import numpy
+import tensorflow as tf
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+
+interpreter = tf.lite.Interpreter(model_path=r"C:\Users\lenwe\converted_dog_model.tflite")
+interpreter.allocate_tensors()
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+
+def predict(img):
+
+    img = img/255.
+
+    interpreter.set_tensor(input_details[0]['index'], np.expand_dims(img, axis = 0).astype(np.float32))
+    interpreter.invoke()
+    pred = interpreter.get_tensor(output_details[0]['index'])  
+
+    if len(pred[0]) > 1:
+        pred_class = class_names[tf.argmax(pred[0])]
+    else:
+        pred_class = class_names[int(tf.round(pred[0]))]
+    
+    return f"Your dog breed is {pred_class}."
+            
+
+demo = gr.Interface(fn=predict, inputs=gr.Image(shape=(224, 224)), outputs=gr.Label(num_top_classes=3))
+
+demo.launch()