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thejas-gida
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Duplicate from thejas-gida/here-auto1
Browse files- .gitattributes +37 -0
- README.md +14 -0
- app.py +40 -0
- cars_bikes(1).json +3 -0
- cars_bikes(2).json +3 -0
- requirements.txt +3 -0
.gitattributes
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README.md
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---
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title: Here Auto1
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emoji: 💻
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colorFrom: blue
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colorTo: green
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sdk: gradio
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sdk_version: 3.36.1
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app_file: app.py
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pinned: false
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license: apache-2.0
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duplicated_from: thejas-gida/here-auto1
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import gradio as gr
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from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper, ServiceContext
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from langchain.agents import Tool
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from langchain.chains.conversation.memory import ConversationBufferWindowMemory
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from langchain.chat_models import ChatOpenAI
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from langchain.agents import initialize_agent
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from langchain import OpenAI
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from langchain.prompts import PromptTemplate
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PREFIX = ''' You are an Automobile expert AI scientist having all the knowledge about all the existing cars and bikes and all the information around it.
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'''
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SUFFIX = '''
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Begin!
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Previous conversation history:
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{chat_history}
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Instructions: {input}
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{agent_scratchpad}
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'''
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index = GPTSimpleVectorIndex.load_from_disk('./cars_bikes(2).json')
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tools = [Tool(
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name = "LlamaIndex",
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func=lambda q: str(index.query(q)),
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description="You are an Automobile expert equipped with all the information related to cars and bikes. If the question is not related to cars, bike models, then please let the user know that My expertise lies only in automobiles, I can answer questions around the same. The input to this tool should be relevant to cars and bikes in Indian currency",
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return_direct=True
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)]
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conversational_memory = ConversationBufferWindowMemory( memory_key='chat_history', k=5, return_messages=True )
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llm = OpenAI(temperature=0.3, model_name="text-davinci-003")
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agent_executor = initialize_agent(tools, llm, agent="conversational-react-description", memory=conversational_memory,agent_kwargs={'prefix':PREFIX,'suffix': SUFFIX})
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def greet(Question):
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return agent_executor.run(input=Question)
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demo = gr.Interface(fn=greet, inputs=gr.Textbox(lines=2, label ="Question", placeholder="Enter the query"), outputs="text",title="Here Auto",description="Know everything about Cars and Bikes")
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demo.launch()
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cars_bikes(1).json
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version https://git-lfs.github.com/spec/v1
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oid sha256:0b5177a2ec88ef54a7ff7a5c366c2a27ac6437be43fc6d01838c6eae46f04c3f
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size 124299833
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cars_bikes(2).json
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version https://git-lfs.github.com/spec/v1
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oid sha256:4e3af2e8abb171f7da28bd2f57bb424b7743bbcd0945ea8c856e91cb66f11410
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size 183662823
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requirements.txt
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llama-index==0.5.6
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langchain==0.0.148
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openai
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