104 lines
3.3 KiB
Python
104 lines
3.3 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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with gr.Blocks(
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css=".gradio-container {background-image: url('file=Background/4.jpeg');background-size: cover; background-size: 100% 100%;}" # .block.svelte-kz0ejz{background-color: rgba(0,0,0,0);} kalo butuh transparan
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) as demo:
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gr.Markdown(" ")
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gr.Markdown(" ")
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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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with gr.Row():
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predict_id_btn = gr.Button("Bahasa Indonesia", variant="primary")
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predict_en_btn = gr.Button("Bahasa Inggris", variant="secondary")
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with gr.Column():
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with gr.Column(visible=False) as id_fruit_name_col:
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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(visible=False) as en_fruit_name_col:
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en_fruit_name = gr.Label(label="Bahasa inggris")
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en_audio = gr.Audio(label="Audio inggris")
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def classify_id(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( # inilo db
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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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id_fruit_name_col = gr.update(visible=True)
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en_fruit_name_col = gr.update(visible=False)
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return [
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names[pred]["id"],
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f"./audios/id/" + names[pred]["id"] + ".mp3",
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id_fruit_name_col,
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en_fruit_name_col,
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]
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def classify_en(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( # inilo db
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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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en_fruit_name_col = gr.update(visible=True)
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id_fruit_name_col = gr.update(visible=False)
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return [
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names[pred]["en"],
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f"./audios/en/" + names[pred]["id"] + ".mp3",
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id_fruit_name_col,
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en_fruit_name_col,
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]
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predict_id_btn.click(
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fn=classify_id,
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inputs=image_input,
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outputs=[id_fruit_name, id_audio, id_fruit_name_col, en_fruit_name_col],
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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_en,
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inputs=image_input,
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outputs=[en_fruit_name, en_audio, id_fruit_name_col, en_fruit_name_col],
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api_name="classify_image",
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)
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# demo.launch(share=True)
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demo.launch()
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