Update app.py

This is initial app

app.py

@@ -0,0 +1,44 @@
+import gradio as gr
+from load_model import build
+
+
+
+def custom_standardization(s):
+    s = tf.strings.lower(s)
+    s = tf.strings.regex_replace(s, f'[{re.escape(string.punctuation)}]', '')
+    s = tf.strings.join(['[START]', s, '[END]'], separator=' ')
+    return s
+    
+model = build()
+
+def simple_gen(image, temperature=1):
+    initial = model.word_to_index([['[START]']]) # (batch, sequence)
+    img_features = model.feature_extractor(image[tf.newaxis, ...])
+
+    tokens = initial # (batch, sequence)
+    for n in range(50):
+        preds = model((img_features, tokens)).numpy()  # (batch, sequence, vocab)
+        preds = preds[:,-1, :]  #(batch, vocab)
+        if temperature==0:
+            next = tf.argmax(preds, axis=-1)[:, tf.newaxis]  # (batch, 1)
+        else:
+            next = tf.random.categorical(preds/temperature, num_samples=1)  # (batch, 1)
+        tokens = tf.concat([tokens, next], axis=1) # (batch, sequence) 
+
+        if next[0] == model.word_to_index('[END]'):
+            break
+            
+    words = model.index_to_word(tokens[0, 1:-1])
+    result = tf.strings.reduce_join(words, axis=-1, separator=' ')
+    return result.numpy().decode()
+
+def transcribes(image):
+    result = []
+    for t in [0,0.5,1]:
+        result.append(simple_gen(image, t))
+    return result
+    
+gr.interface(fn=transcribes, 
+             inputs=gr.Image(type="pil"),
+             outputs=["text","text","text"]
+            ).launch()