Update app.py

Deal with text length.

app.py

@@ -45,6 +45,8 @@ from langchain.agents.agent_types import AgentType
 from langchain.tools import WikipediaQueryRun
 from langchain.utilities import WikipediaAPIWrapper
 from langchain.tools import DuckDuckGoSearchRun
+from models_for_langchain.memory_func import validate_memory_len
+
 
 provider_dict = {
     'Ails': Ails,
@@ -125,31 +127,31 @@ def bot(history, model_name, provider_name, system_msg, agent):
         return str(response)
     else:
         new_template = template.format(system_instruction=system_msg)
-    prompt = PromptTemplate(
-                        input_variables=["chat_history", "human_input"], template=new_template
-                        )
 
-    prev_memory = ''
     if len(history)>1 and history[-2][1]!=None:
-        memory.chat_memory.add_user_message(history[-2][0])
         memory.chat_memory.add_ai_message(history[-2][1]) 
+    memory.chat_memory.add_user_message(history[-1][0])
+    validate_memory_len(memory=memory, max_token_limit=1800)
+    if len(memory.chat_memory.messages)==0:
+        for c in '文本长度超过限制，请清空后再试':
+            history[-1][1] += c
+            yield history
+    else:
         prev_memory = memory.load_memory_variables({})['chat_history']
-    
-    prompt = new_template.format(
-        chat_history = prev_memory,
-        human_input = message
-    )
-
-    bot_msg = llm._call(prompt=prompt)
+        prompt = new_template.format(
+            chat_history = prev_memory,
+        )
+        # print(f'prompt = \n --------\n{prompt}\n --------')
+        bot_msg = llm._call(prompt=prompt)
 
-    for c in bot_msg:
-        history[-1][1] += c
-        yield history
+        for c in bot_msg:
+            history[-1][1] += c
+            yield history
 
 def empty_fn():
     global memory
-    memory = ConversationBufferWindowMemory(k=10, memory_key="chat_history")
-    return None
+    memory = ConversationBufferWindowMemory(k=6, memory_key="chat_history")
+    return [[None, None]]
 
 def undo_fn(history):
     return history[:-1]
@@ -180,7 +182,6 @@ with gr.Blocks() as demo:
     ```
     The following is a conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
     {{chat_history}}
-    Human: {{human_input}}
     AI:"""
 
     memory = ConversationBufferWindowMemory(k=6, memory_key="chat_history")