Update app.py

app.py

@@ -4,31 +4,23 @@ import matplotlib.pyplot as plt
 from keras.models import load_model
 import numpy as np
 
-def load_image(img, show=False):
-    #img = img.reshape((-1, 224, 224, 3))
-    #img = image.load_img(img_path, target_size=(224, 224))
+def load_image(img):
     img_tensor = image.img_to_array(img)
     img_tensor = np.expand_dims(img_tensor, axis=0)
     img_tensor /= 255
 
-    if show:
-        plt.imshow(img_tensor[0])                           
-        plt.axis('off')
-        plt.show()
-
     return img_tensor
 
 def run_model(img):
     model = load_model("res.h5")
 
-    #img_path = '/content/Indian/9/1020.jpg'
     new_image = load_image(img)
-    #result = model.predict(new_image)
     classes = ['1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']
     img = img.reshape((-1, 224, 224, 3))
     result = model.predict(img)
     results = dict(zip(classes, result[0]))
-    return max(results, key = results.get)
+    #return max(results, key = results.get)
+    return results
     
 title = "Indian Sign Language Classifier"
 description = "<p style='text-align: center'>Classifies images from 0-9, A-Z made using Indian Sign Language"
@@ -36,4 +28,4 @@ examples = ['5.jpg','9.jpg','A.jpg','L.jpg','P.jpg']
 
 
 import gradio as gr
-gr.Interface(fn=run_model, inputs=gr.inputs.Image(shape=(224,224)), outputs='text', title=title, description=description, examples=examples).launch()
+gr.Interface(fn=run_model, inputs=gr.inputs.Image(shape=(224,224)), outputs=gr.outputs.Label(num_top_classes=35), title=title, description=description, examples=examples).launch()