Create app.py

app.py

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+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+import matplotlib.pyplot as plt
+from keras.models import load_model
+
+def load_image(img_path, show=False):
+
+    img = image.load_img(img_path, target_size=(224, 224))
+    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_path)
+    #result = model.predict(new_image)
+    result = model.predict(img)
+    results = dict(zip(classes, result[0]))
+    return max(results, key = results.get)
+    
+title = "Indian Sign Language Classifier"
+description = "<p style='text-align: center'>Classifies images from 0-9, A-Z made using Indian Sign Language"
+examples = ['ex1.jpg','ex2.jpg','ex3.jpg','ex4.jpg','ex5.jpg','ex6.jpg','ex7.jpg','ex8.jpg','ex9.jpg','ex10.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()