main app

app.py

@@ -0,0 +1,87 @@
+from flask import Flask, render_template, request, jsonify
+import numpy as np
+import pickle
+
+from tensorflow.keras.models import load_model
+from tensorflow.keras.utils import pad_sequences
+
+app = Flask(__name__)
+
+encoder_model = load_model("encoder_model.keras")
+decoder_model = load_model("decoder_model.keras")
+
+with open("encoder_tokenizer.pkl","rb") as f:
+    encoder_tokenizer = pickle.load(f)
+
+with open("decoder_tokenizer.pkl","rb") as f:
+    decoder_tokenizer = pickle.load(f)
+
+with open("config.pkl","rb") as f:
+    config = pickle.load(f)
+
+input_maxln = config["input_mxlen"]
+output_maxln = config["output_mxlen"]
+
+
+def translate(sentence):
+
+    sequence = encoder_tokenizer.texts_to_sequences([sentence])
+
+    sequence = pad_sequences(sequence,
+                             maxlen=input_maxln,
+                             padding="post")
+
+    states = encoder_model.predict(sequence, verbose=0)
+
+    start = decoder_tokenizer.word_index["<start>"]
+    end = decoder_tokenizer.word_index["<end>"]
+
+    target_seq = np.array([[start]])
+
+    translated = []
+
+    while True:
+
+        output, h, c = decoder_model.predict(
+            [target_seq] + states,
+            verbose=0
+        )
+
+        token = np.argmax(output[0, -1, :])
+
+        if token == end:
+            break
+
+        word = decoder_tokenizer.index_word.get(token, "")
+
+        if word:
+            translated.append(word)
+
+        target_seq = np.array([[token]])
+        states = [h, c]
+
+        if len(translated) > output_maxln:
+            break
+
+    return " ".join(translated)
+
+
+@app.route("/")
+def home():
+    return render_template("index.html")
+
+
+@app.route("/translate", methods=["POST"])
+def translate_api():
+
+    text = request.json["text"]
+
+    prediction = translate(text)
+
+    return jsonify({
+        "translation": prediction
+    })
+
+
+if __name__ == "__main__":
+    app.run(debug=True)