Upload 2 files

app (2).py

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+# -*- coding: utf-8 -*-
+"""app.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Pl40oGz5tSGYUl-qkOCO9EQQuNw4ElvP
+"""
+
+# =================================================================
+# KODE APP.PY: UNTUK DIUNGGAH KE HUGGING FACE
+# =================================================================
+import streamlit as st
+import pandas as pd
+import pickle
+import re
+import nltk
+
+# --- KONFIGURASI FILE ---
+MODEL_PATH = 'chatbot_model.pkl'
+FAQ_PATH = 'perpustakaan_faq.csv'
+
+# Unduh punkt NLTK
+try:
+    nltk.data.find('tokenizers/punkt')
+except nltk.downloader.DownloadError:
+    nltk.download('punkt')
+
+# --- 1. Muat Model dan Data (Caching) ---
+@st.cache_resource
+def load_resources():
+    """Memuat model dan data FAQ sekali saja menggunakan caching Streamlit."""
+    try:
+        # Muat Model Klasifikasi (Pipeline TFIDF + Naive Bayes)
+        with open(MODEL_PATH, 'rb') as file:
+            # Karena versi scikit-learn dan numpy telah dikunci, loading seharusnya berhasil
+            model_pipeline = pickle.load(file)
+
+        # Muat Data FAQ untuk mendapatkan Jawaban
+        df_faq = pd.read_csv(FAQ_PATH)
+
+        return model_pipeline, df_faq
+    except FileNotFoundError as e:
+        st.error(f"Gagal memuat file penting: {e}. Pastikan '{MODEL_PATH}' dan '{FAQ_PATH}' telah diunggah.")
+        st.stop()
+    except Exception as e:
+        # Menangkap error loading (termasuk binary incompatibility)
+        st.error(f"Terjadi kesalahan fatal saat memuat model. Pastikan versi scikit-learn dan numpy di requirements.txt sama persis dengan yang digunakan saat pelatihan model. Error: {e}")
+        st.stop()
+
+model, df_faq = load_resources()
+
+# --- 2. Fungsi Pembersihan Teks dan Logika Chatbot ---
+def clean_text(text):
+    """Membersihkan teks (sesuai dengan yang digunakan saat pelatihan)."""
+    text = re.sub(r'[^\w\s]', '', text)
+    return text.lower().strip()
+
+def predict_and_respond(query, model, df_faq):
+    """Memprediksi Intent dan mengambil Jawaban yang sesuai."""
+
+    cleaned_q = clean_text(query)
+    predicted_intent = model.predict([cleaned_q])[0]
+    responses = df_faq[df_faq['kategori'] == predicted_intent]['chatbot_response'].tolist()
+
+    if responses:
+        return responses[0]
+    else:
+        return f"Maaf, saya tidak dapat menemukan jawaban. (Intent: {predicted_intent})"
+
+# --- 3. Antarmuka Streamlit ---
+
+st.set_page_config(page_title="Perpustakaan Chatbot")
+st.title("📚 Asisten Virtual Perpustakaan")
+st.markdown("Halo! Tanyakan tentang **keanggotaan, peminjaman, atau fasilitas** kami.")
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+if prompt := st.chat_input("Tulis pertanyaan Anda di sini..."):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    response = predict_and_respond(prompt, model, df_faq)
+
+    with st.chat_message("assistant"):
+        st.markdown(response)
+
+    st.session_state.messages.append({"role": "assistant", "content": response})
+

chatbot_model (1).pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f2ec834278832bc8a1f44a934b75819e29cc8017342e37adf5917e6a9e09b44d
+size 5459