without chat mode

app.py

@@ -104,24 +104,24 @@ class SessionState:
 
 		self.cache_clear()
 
-	# --------------------------------------
-	# Load Chat History as a list
-	# --------------------------------------
-	def load_chat_history(self) -> list:
-		chat_history = []
-		try:
-			chat_memory = self.memory.load_memory_variables({})['chat_history']
-		except KeyError:
-			return chat_history
-
-		# チャット履歴をペアごとに読み取る
-		for i in range(0, len(chat_memory), 2):
-			user_message = chat_memory[i].content
-			ai_message = ""
-			if i + 1 < len(chat_memory):
-					ai_message = chat_memory[i + 1].content
-			chat_history.append([user_message, ai_message])
-		return chat_history
+	# # --------------------------------------
+	# # Load Chat History as a list
+	# # --------------------------------------
+	# def load_chat_history(self) -> list:
+	# 	chat_history = []
+	# 	try:
+	# 		chat_memory = self.memory.load_memory_variables({})['chat_history']
+	# 	except KeyError:
+	# 		return chat_history
+
+	# 	# チャット履歴をペアごとに読み取る
+	# 	for i in range(0, len(chat_memory), 2):
+	# 		user_message = chat_memory[i].content
+	# 		ai_message = ""
+	# 		if i + 1 < len(chat_memory):
+	# 				ai_message = chat_memory[i + 1].content
+	# 		chat_history.append([user_message, ai_message])
+	# 	return chat_history
 
 # --------------------------------------
 # 自作TextSplitter（テキストをLLMのトークン数内に分割）
@@ -627,9 +627,21 @@ def user(ss: SessionState, query) -> (SessionState, list):
   return ss, chat_history
 
 def bot(ss: SessionState, query, qa_flag) -> (SessionState, str):
-  if qa_flag is True:
+
+  if ss.llm is None:
+    response = "LLMが設定されていません。設定画面で任意のモデルを選択してください。"
+    ss.dialogue[-1] = (ss.dialogue[-1][0], response)
+    return ss, ""
+
+  elif qa_flag is True and ss.embeddings is None:
+    response = "Embeddingモデルが設定されていません。設定画面で任意のモデルを選択してください。"
+    ss.dialogue[-1] = (ss.dialogue[-1][0], response)
+
+  # QA Model
+  elif qa_flag is True and ss.embeddings is not None:
     ss = qa_predict(ss, query)      # LLMで回答を生成
 
+  # Chat Model
   else:
     ss = conversation_prep(ss)
     ss = chat_predict(ss, query)
@@ -637,7 +649,7 @@ def bot(ss: SessionState, query, qa_flag) -> (SessionState, str):
   return ss, ""                     # ssとquery欄（空欄）
 
 def chat_predict(ss: SessionState, query) -> SessionState:
-  response = ss.conversation_chain.predict(input=query)
+  response = ss.conversation_chain.predict(query=query)
   ss.dialogue[-1] = (ss.dialogue[-1][0], response)
   return ss
 
@@ -694,14 +706,13 @@ def qa_predict(ss: SessionState, query) -> SessionState:
 
 # 回答を1文字ずつチャット画面に表示する
 def show_response(ss: SessionState) -> str:
-  # chat_history = ss.load_chat_history()   # メモリから会話履歴をリスト型で取得
-  # response = chat_history[-1][1]          # メモリから最新の会話[-1]を取得し、チャットボットの回答[1]を退避
-  # chat_history[-1][1] = ""                # 逐次表示のため、チャットボットの回答[1]を空にする
-
   chat_history = [list(item) for item in ss.dialogue]   # タプルをリストに変換して、メモリから会話履歴を取得
   response = chat_history[-1][1]                        # メモリから最新の会話[-1]を取得し、チャットボットの回答[1]を退避
   chat_history[-1][1] = ""                              # 逐次表示のため、チャットボットの回答[1]を空にする
 
+  if response is None:
+    response = "回答を生成できませんでした。"
+
   for character in response:
     chat_history[-1][1] += character
     time.sleep(0.05)
@@ -716,7 +727,11 @@ with gr.Blocks() as demo:
   # API KEY をセット/クリアする関数
   # --------------------------------------
   def openai_api_setfn(openai_api_key) -> str:
-    if not openai_api_key or not openai_api_key.startswith("sk-") or len(openai_api_key) < 50:
+    if openai_api_key == "kikagaku":
+      os.environ["OPENAI_API_KEY"] = os.getenv("kikagaku_demo")
+      status_message = "✅ キカガク専用DEMOへようこそ！APIキーを設定しました"
+      return status_message
+    elif not openai_api_key or not openai_api_key.startswith("sk-") or len(openai_api_key) < 50:
       os.environ["OPENAI_API_KEY"] = ""
       status_message = "❌ 有効なAPIキーを入力してください"
       return status_message
@@ -761,7 +776,7 @@ with gr.Blocks() as demo:
           'sonoisa/sentence-bert-base-ja-mean-tokens-v2',
           'oshizo/sbert-jsnli-luke-japanese-base-lite',
           'text-embedding-ada-002',
-          "None"
+          # "None"
           ],
           value="sonoisa/sentence-bert-base-ja-mean-tokens-v2",
           label = 'Embedding model',
@@ -779,7 +794,7 @@ with gr.Blocks() as demo:
         with gr.Row():
           with gr.Column():
             load_in_8bit          = gr.Checkbox(label="8bit Quantize (HF)", value=True, interactive=True)
-            verbose               = gr.Checkbox(label="Verbose (OpenAI, HF)", value=True, interactive=False)
+            verbose               = gr.Checkbox(label="Verbose (OpenAI, HF)", value=True, interactive=True)
           with gr.Column():
             temperature           = gr.Slider(label='Temperature (OpenAI, HF)', minimum=0.0, maximum=1.0, step=0.1, value=0.2, interactive=True)
           with gr.Column():
@@ -856,7 +871,7 @@ with gr.Blocks() as demo:
               interactive=True,
           )
         with gr.Column(scale=5):
-          qa_flag = gr.Checkbox(label="QA mode", value=True, min_width=60, interactive=True)
+          qa_flag = gr.Checkbox(label="QA mode", value=True, min_width=60, interactive=False)
           query_send_btn = gr.Button(value="▶")
 
       # gr.Examples(["機械学習について説明してください"], inputs=[query])
@@ -865,5 +880,4 @@ with gr.Blocks() as demo:
 
 if __name__ == "__main__":
     demo.queue(concurrency_count=5)
-    demo.launch(debug=True, inbrowser=True)
-
+    demo.launch(debug=True, inbrowser=True)