secondcommit

app.py

@@ -4,39 +4,38 @@ import numpy as np
 import gradio as gr
 
 gr.Interface(fn=predict, 
+    inputs=gr.inputs.Image(type="pil"
              ),
-             outputs=gr.outputs.Label(num_top_classes=3),
-             
-             
+             outputs=gr.outputs.Label(num_top_classes=2),
 def greet(name):
     return "Hello " + name + "!!"
-def predict: 
+
+def predict(img): 
 
 # Load the model
-model = load_model('keras_model.h5')
+    model = load_model('keras_model.h5')
 
 # Create the array of the right shape to feed into the keras model
 # The 'length' or number of images you can put into the array is
 # determined by the first position in the shape tuple, in this case 1.
-data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
 # Replace this with the path to your image
-
-inputs=gr.inputs.Image(type="pil"
+    image = img
 # image = Image.open('<IMAGE_PATH>')
 #resize the image to a 224x224 with the same strategy as in TM2:
 #resizing the image to be at least 224x224 and then cropping from the center
-size = (224, 224)
-image = ImageOps.fit(image, size, Image.ANTIALIAS)
+    size = (224, 224)
+    image = ImageOps.fit(image, size, Image.ANTIALIAS)
 
 #turn the image into a numpy array
-image_array = np.asarray(image)
+    image_array = np.asarray(image)
 # Normalize the image
-normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+    normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
 # Load the image into the array
-data[0] = normalized_image_array
+    data[0] = normalized_image_array
 
 # run the inference
-prediction = model.predict(data)
-print(prediction)
+    prediction = model.predict(data)
+    return prediction
 iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+iface.launch()