77 lines
2.0 KiB
Python
77 lines
2.0 KiB
Python
import base64
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from io import BytesIO
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import firebase_admin
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import gradio as gr
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import numpy as np
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from fastai.vision.all import *
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import json
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from firebase_admin import credentials, firestore
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import pathlib
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temp = pathlib.PosixPath
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pathlib.PosixPath = pathlib.WindowsPath
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cred = credentials.Certificate("firebase_key.json")
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app = firebase_admin.initialize_app(cred)
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db = firestore.client()
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learn = load_learner("model.pkl")
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names = json.load(open("./translations.json"))
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def classify(image):
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buffered = BytesIO()
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image.save(buffered, format="JPEG")
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img_str = base64.b64encode(buffered.getvalue())
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pred, idx, probs = learn.predict(np.asarray(image))
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db.collection("preds").add(
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{
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"image": img_str,
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"prediction": pred.title(),
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"time_added": firestore.SERVER_TIMESTAMP,
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}
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)
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return [
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names[pred]["id"],
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names[pred]["en"],
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f"./audios/en/{pred}.mp3",
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f"./audios/id/{pred}.mp3",
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]
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with gr.Blocks(
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css=".gradio-container {background-image: url('file=Background\fix.png');background-size: cover; background-size: 100% 100%;}"
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) as demo:
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with gr.Row():
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with gr.Column():
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image_input = gr.Webcam(label="Gambar", shape=(200, 200), type="pil")
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predict_id_btn = gr.Button("Prediksi Bahasa Indonesia", variant="primary")
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predict_en_btn = gr.Button("Predict English", variant="primary")
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with gr.Column():
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id_fruit_name = gr.Label(label="Bahasa Indonesia")
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id_audio = gr.Audio(label="Audio Indonesia")
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with gr.Column():
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en_fruit_name = gr.Label(label="English")
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en_audio = gr.Audio(label="English Audio")
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predict_id_btn.click(
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fn=classify,
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inputs=image_input,
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outputs=[id_fruit_name, id_audio],
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api_name="classify_image",
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)
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predict_en_btn.click(
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fn=classify,
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inputs=image_input,
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outputs=[en_fruit_name, en_audio],
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api_name="classify_image",
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)
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demo.launch()
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