Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import joblib
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+model = tf.keras.models.load_model("best_model.h5")
+english_tokenizer = joblib.load('english_tokenizer')
+hindi_tokenizer = joblib.load('hindi_tokenizer')
+
+def generate_text(txts):
+    txts = txts.split(' ')
+    res = []
+    for txt in txts:
+        tmp_tok = english_tokenizer.texts_to_sequences([txt])
+        tmp_pad = pad_sequences(tmp_tok, padding='post', maxlen=100, truncating='post')
+        tmp_preds = model.predict(tmp_pad,verbose=0)
+        text2 = hindi_tokenizer.sequences_to_texts([[np.argmax(p)] for p in tmp_preds[0]])
+        res.append("".join(text2))
+    return " ".join(res)
+
+demo = gr.Interface(fn=generate_text, inputs="text", outputs="text")
+    
+demo.launch(share=True)