app.py

from fastapi import FastAPI
import pickle
import numpy as np
from pydantic import BaseModel
from tensorflow.keras.models import model_from_json
import uvicorn

# Load model
with open("model.json", "r") as json_file:
    loaded_model_json = json_file.read()
model = model_from_json(loaded_model_json)
model.load_weights("model.weights.h5")

# Load vectorizer
with open("vectorizer.pkl", "rb") as file:
    vectorizer = pickle.load(file)

# Load label encoder
with open("label_encoder.pkl", "rb") as file:
    label_encoder = pickle.load(file)

# FastAPI app
app = FastAPI()

# Request schema
class CommentInput(BaseModel):
    text: str

@app.post("/predict")
def predict(data: CommentInput):
    text_vectorized = vectorizer.transform([data.text]).toarray()
    prediction = model.predict(text_vectorized)
    predicted_label = int(label_encoder.inverse_transform([np.argmax(prediction)])[0])  

    return {
        "text": data.text,
        "predicted_label": predicted_label
    }

# Run API locally (Opsional)
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)