Upload 4 files

app.py

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+import gradio as gr
+import numpy as np
+from tensorflow import keras
+import tensorflow as tf
+import pickle
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+pickle_open = open("tokenizer.pkl","rb")
+tokenizer = pickle.load(pickle_open)
+model = load_model("model.h5")
+pickle_open.close()
+
+def predict(t):
+    example = tokenizer.texts_to_sequences([t])
+    example = pad_sequences(example, maxlen=2)
+    prediction = model.predict(np.array(example))
+    predictions = []
+    sorted_ = np.sort(prediction[0])[::-1]
+  
+    for i in sorted_[:5]:
+        predictions.append(np.where(prediction[0] == i)[0])
+    predicted_words = []
+    reverse_word_map = dict(map(reversed, tokenizer.word_index.items())) 
+    for i in predictions:
+        predicted_words.append(reverse_word_map[i[0]])
+    return predicted_words
+
+input_text = gr.inputs.Textbox(lines=1, placeholder="Enter sentence or word here...")
+output_text = gr.outputs.Textbox()
+
+title = "Next Word Prediction"
+description = "Enter some text or sentence or word and get the next 5 possible autocompletion words"
+
+gr.Interface(fn=predict, inputs=input_text, outputs=output_text, title=title, description=description).launch()

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3a4d272b3473e658b28c418a7147e9b155f8e3658c24c77aef18b6747f407d0c
+size 14168072

tokenizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:da96e37ac2b81afc0774a55ccd5e772e79e8dfe57f0104d0757c36084ac71d98
+size 2929187