Create langChain_test.py

langChain_test.py

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+# -*- 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