Delete langChain_test.py

langChain_test.py

@@ -1,131 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from langchain.agents import load_tools
-from langchain.agents import initialize_agent
-from langchain.agents import AgentType
-from langchain.llms import OpenAI
-from langchain.chat_models import ChatOpenAI
-from langchain.chains.conversation.memory import ConversationBufferWindowMemory
-
-import os
-from langchain.chat_models import AzureChatOpenAI
-
-
-#llm_fy = OpenAI(model_name="text-davinci-003", max_tokens=1024) #用来翻译的，已经用LLMChain替换了
-
-
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-OPENAI_API_BASE = os.getenv("OPENAI_API_BASE")
-#llm = ChatOpenAI(openai_api_key=OPENAI_API_KEY, temperature=0, model_name='gpt-3.5-turbo',openai_api_base=OPENAI_API_BASE)
-llm = AzureChatOpenAI(deployment_name="bitservice_chat_35",openai_api_base=OPENAI_API_BASE,openai_api_key=OPENAI_API_KEY,openai_api_version="2023-03-15-preview",model_name="gpt-3.5-turbo")
-
-import torch
-from transformers import BlipProcessor, BlipForConditionalGeneration
-
-image_to_text_model = "Salesforce/blip-image-captioning-large"
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-
-processor = BlipProcessor.from_pretrained(image_to_text_model)
-model = BlipForConditionalGeneration.from_pretrained(image_to_text_model).to(device)
-
-from transformers.models.oneformer.modeling_oneformer import OneFormerModelOutput
-import requests
-from PIL import Image
-
-def describeImageByUrl(image_url):
-  image_object = Image.open(requests.get(image_url, stream=True).raw).convert('RGB')
-  # image
-  inputs = processor(image_object, return_tensors="pt").to(device)
-  outputs = model.generate(**inputs)
-  describe = processor.decode(outputs[0], skip_special_tokens=True)
-  return describe
-
-def describeImageByPath(image_path):
-  image_object = Image.open(image_path).convert('RGB')
-  # image
-  inputs = processor(image_object, return_tensors="pt").to(device)
-  outputs = model.generate(**inputs)
-  describe = processor.decode(outputs[0], skip_special_tokens=True)
-  return describe
-
-
-#description = describeImageByUrl('https://img0.baidu.com/it/u=4190066402,1916608022&fm=253&fmt=auto&app=120&f=JPEG?w=1280&h=800')
-#description
-
-from langchain.tools import BaseTool
-
-class DescribeImageTool(BaseTool):
-    name = "Describe Image Tool"
-    description = 'use this tool to describe an image.'
-
-    def _run(self, url: str):
-        #description = describeImageByUrl(url) 
-        description = describeImageByPath(url)  
-        return description
-    
-    def _arun(self, query: str):
-        raise NotImplementedError("Async operation not supported yet")
-
-
-tools = [DescribeImageTool()]
-
-
-agent = initialize_agent(
-    agent='chat-conversational-react-description',
-    tools=tools,
-    llm=llm,
-    verbose=True,
-    max_iterations=3,
-    early_stopping_method='generate',
-    memory=ConversationBufferWindowMemory(
-        memory_key='chat_history',
-        k=5,
-        return_messages=True
-    )
-)
-
-from langchain.chains import LLMChain
-from langchain.prompts import PromptTemplate
-def enToChinese(english):
-    #ch = llm_fy("Please translate the following sentence from English to Chinese："+english)
-    #return ch
-    pp = "Please translate the following sentence from English to Chinese：{english}"
-    prompt = PromptTemplate(
-           input_variables=["english"],
-           template=pp
-    )
-    llchain=LLMChain(llm=llm,prompt=prompt)
-    return llchain.run(english)
-    
-
-def chToEnglish(chinese):
-    #en = llm_fy("Please translate the following sentence from Chinese to English："+chinese)
-    #return en
-    pp = "Please translate the following sentence from Chinese to English：{chinese}"
-    prompt = PromptTemplate(
-           input_variables=["chinese"],
-           template=pp
-    )
-    llchain=LLMChain(llm=llm,prompt=prompt)
-    return llchain.run(chinese)
-
-#image_url = 'https://img0.baidu.com/it/u=4190066402,1916608022&fm=253&fmt=auto&app=120&f=JPEG?w=1280&h=800'
-#agent(f"Describe the following image:\n{image_url}")
-#en_result = agent(f"描述下面这张图片:\n{image_url}")['output']
-#print(enToChinese(en_result))
-
-#agent(f"What is the brand of car in the following image:\n{image_url}")
-#en_result = agent(f"下面这张图片的汽车品牌是什么:\n{image_url}")['output']
-#print(enToChinese(en_result))
-
-def imageAnalyse(image_path,question):
-    question = question.strip();
-    if len(question) ==0:
-        question = "请描述这张图片"
-    print("question:"+question)
-    en_result = agent(f"{question}:\n{image_path}")['output']
-    print("en_result:"+en_result)
-    ch_result = enToChinese(en_result)
-    print("ch_result:"+ch_result)
-    return ch_result