Create app.py

app.py

@@ -0,0 +1,102 @@
+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()