Delete app.py

app.py

@@ -1,97 +0,0 @@
-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()