Update app.py

app.py

@@ -1,136 +1,105 @@
 import gradio as gr
 import gspread
+import time
 from oauth2client.service_account import ServiceAccountCredentials
 from llama_cpp import Llama
 from llama_index.core import VectorStoreIndex, Settings
-from llama_index.core.node_parser import SentenceSplitter
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-from llama_index.llms.llama_cpp import LlamaCPP
-from huggingface_hub import hf_hub_download
-from llama_index.core.llms import ChatMessage
-from llama_index.core.chat_engine.condense_plus_context import CondensePlusContextChatEngine
 from llama_index.core.schema import Document
 
 # ===================================
-# 1️⃣ Fungsi Membaca Data Google Spreadsheet
+# 1️⃣ Cache Data Google Sheets
 # ===================================
+cached_text_data = None
+
 def read_google_sheets():
+    global cached_text_data
+    if cached_text_data is not None:
+        return cached_text_data
+
     try:
         scope = ["https://www.googleapis.com/auth/spreadsheets", "https://www.googleapis.com/auth/drive"]
         creds = ServiceAccountCredentials.from_json_keyfile_name("credentials.json", scope)
-        client = gspread.authorize(creds)
-        
-        SPREADSHEET_ID = "1e_cNMhwF-QYpyYUpqQh-XCw-OdhWS6EuYsoBUsVtdNg"
-        sheet_names = ["datatarget", "datacuti", "dataabsen", "datalembur", "pkb"]
-
-        all_data = []
-        spreadsheet = client.open_by_key(SPREADSHEET_ID)
-        
-        for sheet_name in sheet_names:
-            try:
-                sheet = spreadsheet.worksheet(sheet_name)
-                data = sheet.get_all_values()
-                all_data.append(f"=== Data dari {sheet_name.upper()} ===")
-                all_data.extend([" | ".join(row) for row in data])
-                all_data.append("\n")
             except gspread.exceptions.WorksheetNotFound:
                 all_data.append(f"❌ ERROR: Worksheet {sheet_name} tidak ditemukan.")
 
-        return "\n".join(all_data).strip()
+        cached_text_data = "\n".join(all_data).strip()
+        return cached_text_data
     
     except gspread.exceptions.SpreadsheetNotFound:
         return "❌ ERROR: Spreadsheet tidak ditemukan!"
-    
-    except Exception as e:
-        return f"❌ ERROR: {str(e)}"
-
-# ===================================
-# 2️⃣ Inisialisasi Model Llama
 # ===================================
 def initialize_llama_model():
     model_path = hf_hub_download(
-        repo_id="TheBLoke/zephyr-7b-beta-GGUF",
+        repo_id="TheBloke/zephyr-7b-beta-GGUF",
         filename="zephyr-7b-beta.Q4_K_M.gguf",
         cache_dir="./models"
     )
-    return model_path
-
 # ===================================
 # 3️⃣ Inisialisasi Pengaturan Model
 # ===================================
+
 def initialize_settings(model_path):
-    Settings.llm = LlamaCPP(model_path=model_path, temperature=0.7)
+    Settings.llm = LlamaCPP(
+        model_path=model_path,
+        temperature=0.7,
+        context_window=4096,
+        max_new_tokens=512,
+        # n_gpu_layers=20,  # ❌ Hapus jika error
+        model_kwargs={"n_ctx": 4096}
+    )
+
 
 # ===================================
 # 4️⃣ Inisialisasi Index & Chat Engine
-# ===================================
-def initialize_index():
-    text_data = read_google_sheets()
-    document = Document(text=text_data)
     parser = SentenceSplitter(chunk_size=100, chunk_overlap=30)
     nodes = parser.get_nodes_from_documents([document])
     
-    embedding = HuggingFaceEmbedding("sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
+    embedding = HuggingFaceEmbedding("sentence-transformers/all-MiniLM-L6-v2")  # ✅ Lebih ringan
     Settings.embed_model = embedding
     
     index = VectorStoreIndex(nodes)
     return index
 
 def initialize_chat_engine(index):
-    retriever = index.as_retriever(similarity_top_k=3)
+    retriever = index.as_retriever(similarity_top_k=1)  # ✅ Kurangi ke 1 untuk kecepatan
     chat_engine = CondensePlusContextChatEngine.from_defaults(
         retriever=retriever,
-        verbose=False  # ❌ Hapus verbose agar tidak ada referensi dokumen
+        verbose=False
     )
     return chat_engine
 
-# ===================================
 # 5️⃣ Fungsi untuk Merapikan Jawaban Chatbot
 # ===================================
 def clean_response(response):
-    text = "".join(response.response_gen)  # Gabungkan teks yang dihasilkan
-    text = text.replace("\n\n", "\n").strip()  # Hilangkan newline berlebihan
+    text = "".join(response.response_gen)
+    text = text.replace("\n\n", "\n").strip()
     text = text.replace("user:", "").replace("jawaban:", "").replace("assistant:", "").strip()
     return text
 
-# ===================================
-# 6️⃣ Fungsi untuk Menghasilkan Respons Chatbot
-# ===================================
-def generate_response(message, history, chat_engine):
-    if history is None:
-        history = []
-
-    chat_messages = [
-        ChatMessage(
-            role="system",
-            content=(
-                "Anda adalah chatbot HRD yang membantu karyawan memahami administrasi perusahaan. "
                 "Jangan menjawab menggunakan Bahasa Inggris. "
                 "Gunakan Bahasa Indonesia dengan gaya profesional dan ramah. "
                 "Jika informasi tidak tersedia dalam dokumen, katakan dengan sopan bahwa Anda tidak tahu. "
-                "Jawaban harus singkat, jelas, dan sesuai konteks."
+                "Jawaban harus singkat, jelas, dan sesuai konteks. "
                 "Jangan memberikan jawaban untuk pertanyaan yang tidak diajukan oleh pengguna. "
                 "Jangan menyertakan rekomendasi pertanyaan lain."
             ),
         ),
     ]
 
-    response = chat_engine.stream_chat(message)
-    cleaned_text = clean_response(response)  # 🔹 Gunakan fungsi clean_response()
+    response = chat_engine.chat(message)  # GANTI: pakai .chat() bukan .stream_chat()
+    cleaned_text = response.response.strip()  # GANTI: langsung ambil response
 
-    history.append((message, cleaned_text))  # 🔹 Pastikan hanya teks yang masuk ke history
+    history.append((message, cleaned_text))
     return cleaned_text
 
-# ===================================
-# 7️⃣ Fungsi Utama untuk Menjalankan Aplikasi
 # ===================================
 def main():
     model_path = initialize_llama_model()
     initialize_settings(model_path)
-
+    
     index = initialize_index()
     chat_engine = initialize_chat_engine(index)
-
+    
     def chatbot_response(message, history):
         return generate_response(message, history, chat_engine)