app/model.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from keras.models import load_model
import pickle
import numpy as np
from keras.preprocessing.sequence import pad_sequences

app = FastAPI()

max_sequence_length = 180

# Load the trained model
try:
    model = load_model('word_prediction_model.h5')
except Exception as e:
    print(f"Error loading the model: {str(e)}")
    model = None

# Load the tokenizer
try:
    with open('tokenizer.pickle', 'rb') as handle:
        tokenizer = pickle.load(handle)
except Exception as e:
    print(f"Error loading the tokenizer: {str(e)}")
    tokenizer = None

class PredictionRequest(BaseModel):
    input_phrase: str
    top_n: int = 5

class PredictionResponse(BaseModel):
    top_words: list
    top_probabilities: list

@app.post("/predict", response_model=PredictionResponse)
def predict(request: PredictionRequest):
    if tokenizer is None or model is None:
        raise HTTPException(status_code=500, detail="Model or tokenizer not loaded")

    input_phrase = request.input_phrase
    top_n = request.top_n

    input_sequence = tokenizer.texts_to_sequences([input_phrase])[0]
    padded_sequence = pad_sequences([input_sequence], maxlen=max_sequence_length-1, padding='pre')
    predicted_probs = model.predict(padded_sequence)[0]
    top_indices = predicted_probs.argsort()[-top_n:][::-1]
    top_words = [tokenizer.index_word[index] for index in top_indices]
    top_probabilities = predicted_probs[top_indices]

    return {"top_words": top_words, "top_probabilities": top_probabilities.tolist()}

@app.get("/")
def read_root():
    return {"message": "Hello from MDS Darija Prediction Team!"}