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Create app.py
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app.py
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import streamlit as st
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from transformers import pipeline
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import nltk
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from nltk.tokenize import word_tokenize
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from nltk.corpus import stopwords
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nltk.download('punkt')
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nltk.download("stopwords")
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# Buat objek terjemahan
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translator = pipeline("translation_id_to_en", model="Helsinki-NLP/Opus-MT-ID-EN")
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# Menggunakan pipa untuk klasifikasi diagnosis penyakit
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disease_classifier = pipeline("text-classification", model="BenK10/disease-diagnosis")
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def preprocess_text(teks):
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tokens = word_tokenize(teks)
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tokens = [token for token in tokens if token.isalnum()]
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tokens = [token for token in tokens if token.lower() not in stopwords.words("indonesian")]
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preprocessed_text = " ".join(tokens)
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return preprocessed_text
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def predict_disease(gejala, usia, jenis_kelamin):
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# Pra-pemrosesan teks gejala
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gejala_diproses = preprocess_text(gejala)
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# Melakukan terjemahan
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terjemahan = translator(gejala_diproses, max_length=50)
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terjemahan_inggris = terjemahan[0]["translation_text"]
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# Membuat fitur tambahan untuk klasifikasi
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fitur = {"usia": usia, "jenis_kelamin": jenis_kelamin}
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# Klasifikasi diagnosis penyakit dengan fitur tambahan
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klasifikasi = disease_classifier(terjemahan_inggris, features=fitur)
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# Mengembalikan diagnosis dan skor kepercayaan
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diagnosis = klasifikasi[0]["label"]
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kepercayaan = klasifikasi[0]["score"]
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return diagnosis, kepercayaan
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# Halaman Streamlit
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st.title("Diagnosis Penyakit")
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# Masukan gejala
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gejala = st.text_area("Masukkan gejala Anda:", "")
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# Masukan usia
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usia = st.number_input("Masukkan usia Anda:", min_value=0, max_value=120)
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# Masukan jenis kelamin
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jenis_kelamin_pilihan = ["Laki-laki", "Perempuan"]
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jenis_kelamin = st.selectbox("Masukkan jenis kelamin Anda:", jenis_kelamin_pilihan)
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# Memanggil fungsi prediksi
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if st.button("Diagnosis"):
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diagnosis, kepercayaan = predict_disease(gejala, usia, jenis_kelamin)
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st.success(f"Diagnosis: {diagnosis}")
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st.write(f"Kepercayaan: {kepercayaan:.2f}")
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