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from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.chat_models import AzureChatOpenAI
from langchain.llms import OpenAI
from langchain.chains.conversation.memory import ConversationBufferWindowMemory
import os
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 describeImage(image_url):
image_object = Image.open(image_url).convert('RGB')
# image
inputs = processor(image_object, return_tensors="pt").to(device)
outputs = model.generate(**inputs)
return processor.decode(outputs[0], skip_special_tokens=True)
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 = describeImage(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)
import gradio as gr
def segment(image,text):
#pass # Implement your image segmentation model here...
print(image)
image_url = image
text = chToEnglish(text)
print(text)
return enToChinese(agent(f"{text}:\n{image_url}").get('output'))
demo = gr.Interface(
fn=segment,
inputs=[
gr.Image(type="filepath",shape=(200, 200),label="请选择一张图片"),
gr.components.Textbox(label="请输入问题"),
],
outputs=[gr.components.Textbox(label="答案",lines=4)])
demo.launch()
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