Update app.py

app.py

@@ -30,20 +30,19 @@ from langchain.document_loaders import PDFMinerLoader
 from langchain.document_loaders import UnstructuredFileLoader
 
 disclaimer = ("""
-#### Disclaimer and Precautions
-######  Use of this tool is at your own risk. The creator cannot be held responsible for any harm caused by this tool.
-######  The creator is not responsible for any damages caused to users or third parties as a result of such content.
+#### 注意事項及び免責事項 Disclaimer and Precautions
+###### ご利用はご自身の責任において行ってください。本ツールにより生じたいかなる損害についても、作成者は責任を負いかねます。 Use of this tool is at your own risk. The creator cannot be held responsible for any harm caused by this tool.
+###### 当該コンテンツに起因してご利用者様および第三者に損害が発生したとしても作成者は責任を負わないものとします。 The creator is not responsible for any damages caused to users or third parties as a result of such content.
 """)
 
 # Configure Chroma
 persist_directory="/data/"
 
 #Convert local file to Embedding (txt file is better)
-loader = UnstructuredFileLoader('data/ChatGPT_S_ND.txt')
+loader = UnstructuredFileLoader('data/pal-end-of-life-trajectory0611 en.txt')
 paldoc = loader.load()
 
-openai.api_key = os.environ.get("OPENAI_API_KEY")
-
+os.environ['OPENAI_API_KEY'] = os.getenv('openai_api_key')
 #OPENAI_API_KEY = os.getenv('openai_api_key')
 #OPENAI_API_KEY = "openai_api_key"
 
@@ -58,9 +57,9 @@ vStore = Chroma.from_documents(paldocs, embeddings)
 from langchain.chains import RetrievalQA
 from langchain.chat_models import ChatOpenAI
 
-# RetrievalQA
+# RetrievalQAチェーンの生成
 model = RetrievalQA.from_chain_type(
-    llm=ChatOpenAI(temperature=0.5, model_name="gpt-3.5-turbo-16k", max_tokens = 256), 
+    llm=ChatOpenAI(temperature=0.5, model_name="gpt-3.5-turbo", max_tokens = 256), 
     chain_type="stuff",
     retriever=vStore.as_retriever()
 )
@@ -68,14 +67,14 @@ model = RetrievalQA.from_chain_type(
 #open as a demo Web app using Gradio
 import gradio as gr
 # 関数を定義
-def askandanswer(question, Language): return model.run("Please create a simple answer to the question from  in " + Language + ". [Question] " + question) 
+def askandanswer(question, Language): return model.run("Please create a simple answer to the question from family members of advanced cancer patients as an expert of palliative care and hospice care in " + Language + ". [Question] " + question) 
 # Webアプリを作成
 app = gr.Interface(fn=askandanswer,
-                   inputs= [gr.Textbox(placeholder="Please input query "),
-                            gr.Dropdown(["中文 Chinese", "英語 English"], label="言語 Language")],
+                   inputs= [gr.Textbox(placeholder="Please input query 質問を入力して下さい"),
+                            gr.Dropdown(["日本語 Japanese", "英語 English"], label="言語 Language")],
                    outputs="text",
-                  title="Chat with Document(s) using OpenAI ChatGPT API and Text Embedding",
-    description= "How to chat with any documents, PDFs, and books using OpenAI ChatGPT API and Text Embedding",
+                  title="終末期がん患者の家族サポートツール Empowering-Families-in-Cancer-Journey",
+    description= "これは「これからの過ごし方について（看取りのパンフレット）」についてのGPTを用いたQ&Aツールです. This is a Q&A tool for End-of-Life Care Brochure  https://www.kanwacare.net/formedical/s_pamphlet/",
     article= (disclaimer),
 )
 # Webアプリを起動