Added Flask app and model

Procfile

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+web: gunicorn app:app

app.py

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+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import tensorflow as tf
+import pickle
+import numpy as np
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+import os
+# import requests
+import gdown
+
+app = Flask(__name__)
+CORS(app)
+
+# Load tokenizer and model
+with open("tokenizer.pkl", "rb") as f:
+    tokenizer = pickle.load(f)
+
+
+MODEL_PATH = "model.keras"
+MODEL_URL = (
+    "https://drive.google.com/uc?export=download&id=1uqqZiZsmI2wnxG6r9Z4IQq94rT9fGwgH"
+)
+
+def download_model():
+    if not os.path.exists(MODEL_PATH):
+        print("Downloading model using gdown...")
+
+        # Use gdown to download the file
+        gdown.download(MODEL_URL, MODEL_PATH, quiet=False)
+
+        if os.path.exists(MODEL_PATH):
+            print("Model downloaded successfully.")
+        else:
+            print("Error: Model download failed.")
+
+download_model()
+
+
+print("Loading model...")
+model = tf.keras.models.load_model(MODEL_PATH)
+
+MAX_LEN = 30
+
+
+def predict_next_words(text, top_k=3):
+    # Tokenize input text
+    sequence = tokenizer.texts_to_sequences([text])[0]
+
+    if len(sequence) == 0:  # If tokenization results in empty list
+        return []
+
+    # Keep only the last 30 tokens if input is too long
+    sequence = sequence[-MAX_LEN:]
+
+    # Pad sequence to required length (post-padding)
+    sequence = pad_sequences([sequence], maxlen=MAX_LEN, padding="post")
+
+    # Predict next word probabilities
+    predictions = model.predict(sequence)[0]
+
+    # Get top-k word indices
+    top_indices = np.argsort(predictions)[-top_k:][::-1]
+
+    # Convert indices back to words
+    word_index = tokenizer.index_word
+    top_words = [word_index.get(i, "") for i in top_indices]
+
+    return [word for word in top_words if word]
+
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    data = request.get_json()
+    text = data.get("text", "").strip()
+
+    if not text:
+        return jsonify({"error": "No text provided"}), 400
+
+    predictions = predict_next_words(text)
+    return jsonify({"predictions": predictions})
+
+
+if __name__ == "__main__":
+    app.run(debug=True)

requirements.txt

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+tensorflow 
+flask
+numpy
+flask_cors
+gunicorn
+gdown

tokenizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6c3f8818b6bb3faf829b875f7b6de44c9fba10b574f2918f3a61e8e2b11634a3
+size 8931605