Update app.py

wiki and duckduckgo agent. check provider available

app.py

@@ -15,12 +15,21 @@ from g4f.Provider import (
     H2o,
     ChatgptLogin,
     DeepAi,
-    GetGpt
+    GetGpt,
+    AItianhu,
+    EasyChat,
+    Acytoo,
+    DfeHub,
+    AiService,
+    BingHuan,
+    Wewordle,
+    ChatgptAi,
 )
 import os
 import json
 import pandas as pd
-
+from langchain.tools.python.tool import PythonREPLTool
+from langchain.agents.agent_toolkits import create_python_agent
 from models_for_langchain.model import CustomLLM
 from langchain.memory import ConversationBufferWindowMemory, ConversationTokenBufferMemory
 from langchain import LLMChain, PromptTemplate
@@ -31,6 +40,10 @@ from langchain.prompts import (
     AIMessagePromptTemplate,
     HumanMessagePromptTemplate,
 )
+from langchain.agents.agent_types import AgentType
+from langchain.tools import WikipediaQueryRun
+from langchain.utilities import WikipediaAPIWrapper
+from langchain.tools import DuckDuckGoSearchRun
 
 provider_dict = {
     'Ails': Ails,
@@ -47,19 +60,39 @@ provider_dict = {
     'H2o': H2o,
     'ChatgptLogin': ChatgptLogin,
     'DeepAi': DeepAi,
-    'GetGpt': GetGpt
+    'GetGpt': GetGpt,
+    'AItianhu': AItianhu,
+    'EasyChat': EasyChat,
+    'Acytoo': Acytoo,
+    'DfeHub': DfeHub,
+    'AiService': AiService,
+    'BingHuan': BingHuan,
+    'Wewordle': Wewordle,
+    'ChatgptAi': ChatgptAi,
+}
+
+available_dict = {
+    'gpt-3.5-turbo':['Acytoo', 'AiService', 'Aichat', 'GetGpt', 'Wewordle'],
+    'gpt-4':['ChatgptAi'],
+    'falcon-7b':['H2o'],
+    'falcon-13b':['H2o'],
+    'llama-13b':['H2o']
 }
 
 def change_prompt_set(prompt_set_name):
     return gr.Dropdown.update(choices=list(prompt_set_list[prompt_set_name].keys()))
 
+def change_model(model_name):
+    new_choices = list(available_dict[model_name])
+    return gr.Dropdown.update(choices=new_choices, value=new_choices[0])
+
 def change_prompt(prompt_set_name, prompt_name):
     return gr.update(value=prompt_set_list[prompt_set_name][prompt_name])
 
 def user(user_message, history):
     return gr.update(value="", interactive=False), history + [[user_message, None]]
 
-def bot(message, history, model_name, provider_name, system_msg):
+def bot(message, history, model_name, provider_name, system_msg, agent):
     response = ''
 
     if len(system_msg)>3000:
@@ -68,23 +101,45 @@ def bot(message, history, model_name, provider_name, system_msg):
     global template, memory
     llm.model_name = model_name
     llm.provider_name = provider_name
+    if agent == '系统提示':
+        new_template = template.format(system_instruction=system_msg)
+    elif agent == '维基百科':
+        wikipedia = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())
+        target = llm(f'用户的问题：```{message}```。为了回答用户的问题，你需要在维基百科上进行搜索，只有一次搜索的机会，请返回需要搜索的词汇，只需要返回一个英文词汇，不要加任何解释：')
+        new_template = template.format(system_instruction=wikipedia.run(str(target)))
+    elif agent == 'duckduckgo':
+        search = DuckDuckGoSearchRun()
+        target = llm(f'用户的问题：```{message}```。为了回答用户的问题，你需要在duckduckgo搜索引擎上进行搜索，只有一次搜索的机会，请返回需要搜索的内容，只需要返回纯英文的搜索语句，不要加任何解释：')     
+        new_template = template.format(system_instruction=search.run(str(target))) 
+    elif agent == 'python':
+        py_agent = create_python_agent(
+                llm,
+                tool=PythonREPLTool(), # REPL，一种代码交互方式，类似jupyter，可以执行代码
+                verbose=True,
+                # agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION
+                handle_parsing_errors=True, # 输出无法解析，返回给llm要求改正。
+            )
+        response = py_agent.run(message)
+        return str(response)
+    else:
+        new_template = template.format(system_instruction=system_msg)
     prompt = PromptTemplate(
-                        input_variables=["chat_history", "human_input"], template=template.format(system_instruction=system_msg)
-                    )
+                        input_variables=["chat_history", "human_input"], template=new_template
+                        )
     llm_chain = LLMChain(
                         llm=llm,
                         prompt=prompt,
-                        verbose=False,
+                        verbose=True,
                         memory=memory,
                     )
     bot_msg = llm_chain.run(message)
     for c in bot_msg:
         response += c
-        yield response
+    return response
 
 def empty_chat():
     global memory
-    memory = ConversationBufferWindowMemory(k=10, memory_key="chat_history")
+    memory = ConversationBufferWindowMemory(k=6, memory_key="chat_history")
     return None
 
 prompt_set_list = {}
@@ -112,23 +167,26 @@ with gr.Blocks() as demo:
     Human: {{human_input}}
     Chatbot:"""
 
-    memory = ConversationBufferWindowMemory(k=10, memory_key="chat_history")
+    memory = ConversationBufferWindowMemory(k=6, memory_key="chat_history")
     with gr.Row():
-        model_name = gr.Dropdown(['gpt-3.5-turbo', 'gpt-4'], value='gpt-3.5-turbo', label='模型')
-        provider = gr.Dropdown(provider_dict.keys(), value='GetGpt', label='提供者', min_width=20)
+        model_name = gr.Dropdown(list(available_dict.keys()), value='gpt-3.5-turbo', label='模型')
+        provider = gr.Dropdown(available_dict['gpt-3.5-turbo'], value='AiService', label='提供者', min_width=20)
+        agent = gr.Dropdown(['系统提示', '维基百科', 'duckduckgo'], value='系统提示', label='Agent')
     system_msg = gr.Textbox(value="你是一名助手，可以解答问题。", label='系统提示')
     gr.ChatInterface(bot,
                      additional_inputs=[
                                 model_name,
                                 provider,
-                                system_msg]
+                                system_msg,
+                                agent]
                     )
     with gr.Row():
         default_prompt_set = "1 中文提示词.json"
         prompt_set_name = gr.Dropdown(prompt_set_list.keys(), value=default_prompt_set, label='提示词集合')
-        prompt_name = gr.Dropdown(prompt_set_list[default_prompt_set].keys(), label='提示词', min_width=20)
+        prompt_name = gr.Dropdown(prompt_set_list[default_prompt_set].keys(), label='提示词', min_width=5, container=True)
 
     prompt_set_name.select(change_prompt_set, prompt_set_name, prompt_name)
+    model_name.select(change_model, model_name, provider)
     prompt_name.select(change_prompt, [prompt_set_name, prompt_name], system_msg)
 
 demo.title = "AI Chat"