Create app.py

app.py

@@ -0,0 +1,97 @@
+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 AzureChatOpenAI
+from langchain.chains.conversation.memory import ConversationBufferWindowMemory
+import os
+from langchain.tools import BaseTool
+import torch
+from transformers import BlipProcessor, BlipForConditionalGeneration
+from transformers.models.oneformer.modeling_oneformer import OneFormerModelOutput
+import requests
+from PIL import Image
+import gradio as gr
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+
+OPENAI_API_KEY=os.getenv("OPENAI_API_KEY")
+OPENAI_API_BASE=os.getenv("OPENAI_API_BASE")
+DEP_NAME=os.getenv("deployment_name")
+llm=AzureChatOpenAI(deployment_name=DEP_NAME,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")
+
+image_to_text_model = "Salesforce/blip-image-captioning-large"
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+model = BlipForConditionalGeneration.from_pretrained(image_to_text_model).to(device)
+processor = BlipProcessor.from_pretrained(image_to_text_model)
+
+def describeImage(image_url):
+  #image_object = Image.open(requests.get(image_url, stream=True).raw).convert('RGB')
+  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)
+
+
+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()]
+
+def enToChinese(english):
+    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):
+    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)
+
+
+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
+    )
+)
+
+def image_to_txt(image_url,user_input):
+    user_input = chToEnglish(user_input)
+    return enToChinese(agent(f"{input}:\n{image_url}")['output'])
+
+with gr.Blocks() as demo:
+    image_url = gr.Image(type="filepath",label="请选择一张图片")
+    print(image_url)
+    user_input = gr.Textbox(show_label=False, placeholder="请输入...", lines=1)
+    output = gr.Textbox(show_label=False, placeholder="", lines=1)
+    submit_btn = gr.Button('提交',variant="primary")
+    submit_btn.click(image_to_txt,inputs=[image_url,user_input],outputs=output)
+    
+demo.launch()