Update app.py

app.py

@@ -1,261 +1,101 @@
-# -*- coding: utf-8 -*-
-"""Chatbot
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1mbREVflL7r6y0hCUPE-MD3VzJJUs5OfR
-"""
-
-!huggingface-cli download TheBloke/zephyr-7B-beta-GGUF zephyr-7b-beta.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
-
-!CMAKE_ARGS="-DGGML_CUDA=on" pip install llama-cpp-python
-
+# Import Library yang Diperlukan
+import gradio as gr
+import shutil
+import os
+import subprocess
 from llama_cpp import Llama
-
-llm = Llama(
-      model_path="/content/zephyr-7b-beta.Q4_K_M.gguf",
-      n_gpu_layers=-1, # Uncomment to use GPU acceleration
-      # seed=1337, # Uncomment to set a specific seed
-      # n_ctx=2048, # Uncomment to increase the context window
-)
-
-llm("What is Large Language Model?", max_tokens=128)
-
-!pip install llama-index gradio
-
-pip install llama-index-llms-llama-cpp
-
-from typing import Sequence, Optional
-
-from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
-from llama_index.llms.llama_cpp import LlamaCPP
-from llama_index.llms.llama_cpp.llama_utils import (
-    messages_to_prompt,
-    completion_to_prompt,
-)
+from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, Settings
 from llama_index.core.llms import ChatMessage
+from llama_index.llms.llama_cpp import LlamaCPP
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from huggingface_hub import hf_hub_download
+from llama_index.core.node_parser import SentenceSplitter
 
-def zephyr_messages_to_prompt(
-    messages: Sequence[ChatMessage],
-    system_prompt: Optional[str]=None
-) -> str:
-    prompt = ""
-    for message in messages:
-        prompt += f"<|{message.role}|>\n"
-        prompt += f"{message.content}</s>\n"
-
-    return prompt + "<|assistant|>\n"
-
-llm = LlamaCPP(
-    # You can pass in the URL to a GGML model to download it automatically
-    model_url=None,
-    # optionally, you can set the path to a pre-downloaded model instead of model_url
-    model_path="/content/zephyr-7b-beta.Q4_K_M.gguf",
-    temperature=0.1,
-    max_new_tokens=1000,
-    # llama2 has a context window of 4096 tokens, but we set it lower to allow for some wiggle room
-    context_window=3900,
-    # kwargs to pass to __call__()
-    generate_kwargs={},
-    # kwargs to pass to __init__()
-    # set to at least 1 to use GPU
-    model_kwargs={"n_gpu_layers": 1},
-    # transform inputs into Llama2 format
-    messages_to_prompt=messages_to_prompt,
-    completion_to_prompt=completion_to_prompt,
-    verbose=True,
-)
-
-messages = [
-    ChatMessage(
-        role="system",
-        content="Anda adalah chatbot yang selalu menjawab pertanyaan secara singkat, ramah, dan jelas dalam bahasa Indonesia."
-    ),
-    ChatMessage(
-        role="user",
-        content="There's a llama on my lawn, how can I get rid of him?"
+# Fungsi untuk mengunduh model Llama
+def initialize_llama_model():
+    # Unduh model jika belum ada di direktori kerja
+    model_path = hf_hub_download(
+        repo_id="TheBLoke/zephyr-7b-beta-GGUF",  # Nama repo model
+        filename="zephyr-7b-beta.Q4_K_M.gguf",  # Nama file model
+        cache_dir="./models"  # Lokasi direktori untuk menyimpan model
     )
-]
-
-# zephyr_messages_to_prompt(messages)
-
-response = llm.stream_chat(messages)
-for r in response:
-  print(r.delta, end="", flush=True)
+    return model_path
 
-!pip install flask-ngrok
-!pip install pymongo
-
-!ngrok http 27017
-
-import gradio as gr
+# Fungsi untuk mengatur konfigurasi Settings
+def initialize_settings(model_path):  
+    Settings.llm = LlamaCPP(
+        model_path=model_path,
+        temperature=0.7,
+    )
 
+# Fungsi untuk Menginisialisasi Index
+def initialize_index():
+    # Tentukan dokumen input untuk pembacaan data
+    documents = SimpleDirectoryReader(input_files=["bahandokumen/K3.txt",
+                                                   "bahandokumen/bonus.txt",
+                                                   "bahandokumen/cuti.txt",
+                                                   "bahandokumen/disiplinkerja.txt",
+                                                   "bahandokumen/fasilitas&bantuan.txt",
+                                                   "bahandokumen/upahlembur.txt",
+                                                   "bahandokumen/waktukerja.txt"]).load_data()
+
+    parser = SentenceSplitter(chunk_size=150, chunk_overlap=10)
+    nodes = parser.get_nodes_from_documents(documents)
+    embedding = HuggingFaceEmbedding("BAAI/bge-base-en-v1.5")
+    Settings.embed_model = embedding
+    index = VectorStoreIndex(nodes)
+    return index
+
+# Inisialisasi Mesin Chat
+def initialize_chat_engine(index):
+    from llama_index.core.prompts import PromptTemplate
+    from llama_index.core.chat_engine.condense_plus_context import CondensePlusContextChatEngine
+    retriever = index.as_retriever(similarity_top_k=3)
+    chat_engine = CondensePlusContextChatEngine.from_defaults(
+        retriever=retriever,
+        verbose=True,
+    )
+    return chat_engine
 
-def generate_response(message, history):
+# Fungsi untuk menghasilkan respons chatbot
+def generate_response(message, history, chat_engine):
     chat_messages = [
         ChatMessage(
             role="system",
             content="Anda adalah chatbot yang selalu menjawab pertanyaan secara singkat, ramah, dan jelas dalam bahasa Indonesia."
         ),
     ]
-    for human, ai in history:
-        chat_messages.append(ChatMessage(
-            role="user",
-            content=human
-        ))
-        chat_messages.append(ChatMessage(
-            role="assistant",
-            content=ai
-        ))
-
-    chat_messages.append(ChatMessage(
-        role="user",
-        content=message
-    ))
-
-    response = llm.stream_chat(chat_messages)
-    text = ""
-    for r in response:
-      text += r.delta
-      yield text
-
-gr.ChatInterface(generate_response).launch()
-
-from google.colab import files
-uploaded = files.upload()
-for filename in uploaded.keys():
-  print(uploaded[filename])
-
-!pip install pypdf -q
-
-from llama_index.core import SimpleDirectoryReader
-documents = SimpleDirectoryReader(input_files=["/content/bahandokumen/K3.txt",
-                                                   "/content/bahandokumen/bonus.txt",
-                                                   "/content/bahandokumen/jadwallembur.txt",
-                                                   "/content/bahandokumen/datalembur.txt",
-                                                   "/content/bahandokumen/absensi.txt",
-                                                   "/content/bahandokumen/sisacuti.txt",
-                                                   "/content/bahandokumen/target.txt",
-                                                   "/content/bahandokumen/cuti.txt",
-                                                   "/content/bahandokumen/disiplinkerja.txt",
-                                                   "/content/bahandokumen/fasilitas&bantuan.txt",
-                                                   "/content/bahandokumen/fasilitaskerja.txt",
-                                                   "/content/bahandokumen/hak.txt",
-                                                   "/content/bahandokumen/hubunganpengusaha&serikat.txt",
-                                                   "/content/bahandokumen/istilah.txt",
-                                                   "/content/bahandokumen/jaminanserikat.txt",
-                                                   "/content/bahandokumen/jamkes.txt",
-                                                   "/content/bahandokumen/jamsos.txt",
-                                                   "/content/bahandokumen/keluhkesah.txt",
-                                                   "/content/bahandokumen/kenaikanupah.txt",
-                                                   "/content/bahandokumen/kewajiban.txt",
-                                                   "/content/bahandokumen/kompensasi.txt",
-                                                   "/content/bahandokumen/larangan.txt",
-                                                   "/content/bahandokumen/lembur.txt",
-                                                   "/content/bahandokumen/luaskesepakatan.txt",
-                                                   "/content/bahandokumen/mogok.txt",
-                                                   "/content/bahandokumen/pelanggaran&sanksi.txt",
-                                                   "/content/bahandokumen/pendidikan.txt",
-                                                   "/content/bahandokumen/pengangkatan.txt",
-                                                   "/content/bahandokumen/penilaian&promosi.txt",
-                                                   "/content/bahandokumen/pensiun.txt",
-                                                   "/content/bahandokumen/perjadin.txt",
-                                                   "/content/bahandokumen/pesangon.txt",
-                                                   "/content/bahandokumen/phk.txt",
-                                                   "/content/bahandokumen/pihak.txt",
-                                                   "/content/bahandokumen/pkb.txt",
-                                                   "/content/bahandokumen/resign.txt",
-                                                   "/content/bahandokumen/sanksi.txt",
-                                                   "/content/bahandokumen/shift.txt",
-                                                   "/content/bahandokumen/syaratkerja.txt",
-                                                   "/content/bahandokumen/tatacara.txt",
-                                                   "/content/bahandokumen/tka.txt",
-                                                   "/content/bahandokumen/tunjangan.txt",
-                                                   "/content/bahandokumen/uangpisah.txt",
-                                                   "/content/bahandokumen/upah.txt",
-                                                   "/content/bahandokumen/upahlembur.txt",
-                                                   "/content/bahandokumen/waktukerja.txt"]).load_data()
-
-from llama_index.core.node_parser import SentenceSplitter
-parser = SentenceSplitter(chunk_size=300, chunk_overlap=20)
-nodes = parser.get_nodes_from_documents(documents)
-
-nodes[1].get_content()
-
-!pip install llama-index-embeddings-huggingface
-
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-from llama_index.core import Settings
-
-embedding = HuggingFaceEmbedding("BAAI/bge-base-en-v1.5")
-Settings.llm=llm
-Settings.embed_model=embedding
-
-from llama_index.core import VectorStoreIndex
-
-index = VectorStoreIndex(nodes)
-
-retriever = index.as_retriever(similarity_top_k=3)
-
-results = retriever.retrieve("Sebutkan Jadwal Lembur Tahun 2024?")
-
-len(results)
-
-results[0].get_content()
-
-query_engine = index.as_query_engine(similarity_top_k=3)
-response = query_engine.query("Sebutkan Jadwal Lembur Tahun 2024?")
-response.response
-
-from llama_index.core.prompts import PromptTemplate
-from llama_index.core.llms import ChatMessage, MessageRole
-from llama_index.core.chat_engine.condense_plus_context import (
-    CondensePlusContextChatEngine,
-)
-
-chat_engine = CondensePlusContextChatEngine.from_defaults(
-    retriever=retriever,
-    verbose=True,
-)
-
-response = chat_engine.chat("Sebutkan Jadwal Lembur Tahun 2024?")
-
-response.response
-
-response = chat_engine.chat("Apakah anda bisa menjelaskan target Bulan Januari?")
-
-response.response
-
-import gradio as gr
-
-chat_engine.reset()
-
-def generate_response(message, history):
-    chat_messages = [
-        ChatMessage(
-            role="system",
-            content="Anda adalah chatbot yang selalu pertanyaan dalam bahasa Indonesia dengan singkat dan ramah."
-        ),
-    ]
     response = chat_engine.stream_chat(message)
-    text = ""
-    for token in response.response_gen:
-        text += token
-        yield text
-
-def clear_history():
-    chat_engine.reset()
-
-# Membuat tombol 'Clear'
-clear_btn = gr.Button("Clear")
-
-# Mengatur layout dengan gr.Blocks
-with gr.Blocks() as demo:
-    # Mengatur tombol untuk menghapus riwayat chat
-    clear_btn.click(clear_history)
-
-    # Membuat antarmuka chat tanpa argumen clear_btn
-    chat_interface = gr.ChatInterface(generate_response)
-
-demo.launch()
+    text = "".join(response.response_gen)  # Gabungkan semua token menjadi string
+    history.append((message, text))  # Tambahkan ke riwayat
+    return history
+
+def clear_history(chat_engine):
+    chat_engine.clear()
+    
+# Inisialisasi Komponen Gradio untuk UI
+def launch_gradio(chat_engine):
+    with gr.Blocks() as demo:
+        # Mengatur tombol untuk menghapus riwayat chat
+        clear_btn = gr.Button("Clear")
+        clear_btn.click(lambda: clear_history(chat_engine))
+
+        # Membuat antarmuka chat
+        chat_interface = gr.ChatInterface(
+            lambda message, history: generate_response(message, history, chat_engine)
+        )
+    demo.launch()
+
+# Fungsi Utama untuk Menjalankan Aplikasi
+def main():
+    # Unduh model dan inisialisasi pengaturan
+    model_path = initialize_llama_model()
+    initialize_settings(model_path)  # Mengirimkan model_path ke fungsi initialize_settings
+    # Inisialisasi index dan engine
+    index = initialize_index()
+    chat_engine = initialize_chat_engine(index)
+    # Luncurkan antarmuka
+    launch_gradio(chat_engine)
+    
+if __name__ == "__main__":
+    main()