Delete app.py

app.py

@@ -1,102 +0,0 @@
-import os
-import gradio as gr
-from langchain.agents import  initialize_agent,AgentType
-from langchain.chat_models import AzureChatOpenAI
-from langchain.chains.conversation.memory import ConversationBufferWindowMemory
-import torch
-from transformers import BlipProcessor,BlipForConditionalGeneration
-import requests
-from PIL import Image
-from langchain.tools import BaseTool
-from langchain.chains import LLMChain
-from langchain import PromptTemplate, FewShotPromptTemplate
-
-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'
-
-processor=BlipProcessor.from_pretrained(image_to_text_model)
-
-model=BlipForConditionalGeneration.from_pretrained(image_to_text_model).to(device)
-
-def descImage(image_url):
-    image_obj=Image.open(image_url).convert('RGB')
-    inputs=processor(image_obj,return_tensors='pt').to(device)
-    outputs=model.generate(**inputs)
-    return processor.decode(outputs[0],skip_special_tokens=True)
-
-def toChinese(en:str):
-    pp="翻译下面语句到中文\n{en}"
-    prompt = PromptTemplate(
-            input_variables=["en"],
-            template=pp
-        )
-    llchain=LLMChain(llm=llm,prompt=prompt)
-    return llchain.run(en)
-
-class DescTool(BaseTool):
-    name="Describe Image Tool"
-    description="use this tool to describe an image"
-
-    def _run(self,url:str):
-        description=descImage(url)
-        return description
-    def _arun(
-        self,query:str):
-        raise NotImplementedError('未实现')
-
-tools=[DescTool()]
-memory=ConversationBufferWindowMemory(
-                          memory_key='chat_history',
-                          k=5,
-                          return_messages=True
-                      )
-
-agent=initialize_agent(
-                      agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
-                      tools=tools,
-                      llm=llm,
-                      verbose=False,
-                      max_iterations=3,
-                      early_stopping_method='generate',
-                      memory=memory
-                      )
-def reset_user_input():
-    return gr.update(value='')
-def reset_state():
-    return [], []
-
-def predict(file,input, chatbot,history):
-    input1=f""+input+"\n"+file
-    out=agent(input1)
-    anws=toChinese(out['output'])
-    chatbot.append(input)
-    chatbot[-1] = (input, anws)
-    yield chatbot, history
-    return
-
-with gr.Blocks(css=".chat-blocks{height:calc(100vh - 332px);} .mychat{flex:1} .mychat .block{min-height:100%} .mychat .block .wrap{max-height: calc(100vh - 330px);} .myinput{flex:initial !important;min-height:180px}") as demo:
-    title = '图像识别'
-    demo.title=title
-    with gr.Column(elem_classes="chat-blocks"):
-        with gr.Row(elem_classes="mychat"):
-            file = gr.Image(type="filepath")
-            chatbot = gr.Chatbot(label="图像识别", show_label=False)
-        with gr.Column(elem_classes="myinput"):
-            user_input = gr.Textbox(show_label=False, placeholder="请输入...", lines=1).style(
-                container=False)
-            submitBtn = gr.Button("提交", variant="primary", elem_classes="btn1")
-            emptyBtn = gr.Button("清除历史").style(container=False)
-
-    history = gr.State([])
-    submitBtn.click(predict, [file,user_input, chatbot,history], [chatbot, history],
-                        show_progress=True)
-    submitBtn.click(reset_user_input, [], [user_input])
-    emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True)
-
-
-demo.queue(api_open=False,concurrency_count=20).launch()