Update app.py

app.py

@@ -78,7 +78,7 @@ def initialize_index():
 
     parser = SentenceSplitter(chunk_size=150, chunk_overlap=10)
     nodes = parser.get_nodes_from_documents(documents)
-    embedding = HuggingFaceEmbedding("BAAI/bge-base-en-v1.5")
+    embedding = HuggingFaceEmbedding("sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
     Settings.embed_model = embedding
     index = VectorStoreIndex(nodes)
     return index
@@ -87,7 +87,7 @@ def initialize_index():
 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)
+    retriever = index.as_retriever(similarity_top_k=1)
     chat_engine = CondensePlusContextChatEngine.from_defaults(
         retriever=retriever,
         verbose=False,
@@ -111,17 +111,14 @@ 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))
+def chatbot_response(message, history):
+    return generate_response(message, history, chat_engine)
 
-        # Membuat antarmuka chat
-        chat_interface = gr.ChatInterface(
-            lambda message, history: generate_response(message, history, chat_engine)
-        )
-    demo.launch()
+gr.Interface(
+    fn=chatbot_response,
+    inputs=["text"],
+    outputs=["text"],
+).launch()
 
 # Fungsi Utama untuk Menjalankan Aplikasi
 def main():