Update app.py

app.py

@@ -15,7 +15,7 @@ from langchain.memory import ConversationBufferMemory
 from langchain_community.llms import HuggingFaceEndpoint
 import torch
 
-list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"]  
+list_llm = ["kodetr/stunting-qa-v3", "meta-llama/Meta-Llama-3-8B-Instruct"]  
 list_llm_simple = [os.path.basename(llm) for llm in list_llm]
 
 # Load and split PDF document
@@ -43,7 +43,7 @@ def create_db(splits):
 
 # Initialize langchain LLM chain
 def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
-    if llm_model == "meta-llama/Meta-Llama-3-8B-Instruct":
+    if llm_model == "kodetr/stunting-qa-v3":
         llm = HuggingFaceEndpoint(
             repo_id=llm_model,
             huggingfacehub_api_token = api_token,
@@ -93,7 +93,7 @@ def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, pr
     llm_name = list_llm[llm_option]
     print("llm_name: ",llm_name)
     qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress)
-    return qa_chain, "QA chain initialized. Chatbot is ready!"
+    return qa_chain, "QA chain initialized. Chatbot sudah siap!"
 
 
 def format_chat_history(message, chat_history):
@@ -137,39 +137,39 @@ def demo():
     with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue = "sky")) as demo:
         vector_db = gr.State()
         qa_chain = gr.State()
-        gr.HTML("<center><h1>RAG PDF chatbot</h1><center>")
-        gr.Markdown("""<b>Query your PDF documents!</b> This AI agent is designed to perform retrieval augmented generation (RAG) on PDF documents. The app is hosted on Hugging Face Hub for the sole purpose of demonstration. \
-        <b>Please do not upload confidential documents.</b>
+        gr.HTML("<center><h1>RAG PDF Stunting</h1><center>")
+        gr.Markdown("""<b>Kueri dokumen PDF Anda!</b> Agen AI ini dirancang untuk melakukan pengambilan augmented generation (RAG) pada dokumen PDF. Aplikasi ini dihosting di Hugging Face Hub hanya untuk tujuan demonstrasi. \
+        <b>Harap jangan mengunggah dokumen rahasia.</b>
         """)
         with gr.Row():
             with gr.Column(scale = 86):
-                gr.Markdown("<b>Step 1 - Upload PDF documents and Initialize RAG pipeline</b>")
+                gr.Markdown("<b>Langkah 1 - Unggah dokumen PDF dan Inisialisasi pipeline RAG</b>")
                 with gr.Row():
-                    document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF documents")
+                    document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Unggah dokumen PDF")
                 with gr.Row():
-                    db_btn = gr.Button("Create vector database")
+                    db_btn = gr.Button("Buat database vektor")
                 with gr.Row():
                         db_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Vector database status", 
-                gr.Markdown("<style>body { font-size: 16px; }</style><b>Select Large Language Model (LLM) and input parameters</b>")
+                gr.Markdown("<style>body { font-size: 16px; }</style><b>Pilih Model Bahasa Besar (LLM) dan parameter masukan</b>")
                 with gr.Row():
                     llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value = list_llm_simple[0], type="index") # info="Select LLM", show_label=False
                 with gr.Row():
-                    with gr.Accordion("LLM input parameters", open=False):
+                    with gr.Accordion("Parameter masukan LLM", open=False):
                         with gr.Row():
-                            slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.5, step=0.1, label="Temperature", info="Controls randomness in token generation", interactive=True)
+                            slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.5, step=0.1, label="Temperature", info="Mengontrol keacakan dalam pembuatan token", interactive=True)
                         with gr.Row():
-                            slider_maxtokens = gr.Slider(minimum = 128, maximum = 9192, value=4096, step=128, label="Max New Tokens", info="Maximum number of tokens to be generated",interactive=True)
+                            slider_maxtokens = gr.Slider(minimum = 128, maximum = 9192, value=4096, step=128, label="Maksimum Token Baru", info="Jumlah maksimum token yang akan dihasilkan",interactive=True)
                         with gr.Row():
-                                slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k", info="Number of tokens to select the next token from", interactive=True)
+                                slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k", info="Jumlah token untuk memilih token berikutnya", interactive=True)
                 with gr.Row():
-                    qachain_btn = gr.Button("Initialize Question Answering Chatbot")
+                    qachain_btn = gr.Button("Inisialisasi Chatbot Penjawab Pertanyaan")
                 with gr.Row():
                         llm_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Chatbot status", 
 
             with gr.Column(scale = 200):
-                gr.Markdown("<b>Step 2 - Chat with your Document</b>")
+                gr.Markdown("<b>Langkah 2 - Ngobrol dengan Dokumen Anda</b>")
                 chatbot = gr.Chatbot(height=505)
-                with gr.Accordion("Relevent context from the source document", open=False):
+                with gr.Accordion("Konteks yang relevan dari dokumen sumber", open=False):
                     with gr.Row():
                         doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20)
                         source1_page = gr.Number(label="Page", scale=1)