import gradio as gr
from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper, ServiceContext
from langchain.agents import Tool
from langchain.chains.conversation.memory import ConversationBufferWindowMemory
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent
from langchain import OpenAI
from langchain.prompts import PromptTemplate
PREFIX = ''' You are an Automobile expert AI scientist having all the knowledge about all the existing cars and bikes with their respective models and all the information around it.
             If the question is not related to cars, bikes, automobiles or their related models then please let the user know that you don't have the relevant information.

            Return the entire output in an HTML format.

            Make sure to follow each and every instructions before giving the response.
'''
SUFFIX = '''
Begin!
Previous conversation history:
{chat_history}
Instructions: {input}
{agent_scratchpad}
'''

index = GPTSimpleVectorIndex.load_from_disk('./cars_bikes(2).json')
tools = [
        Tool(
            name = "LlamaIndex",
            func=lambda q: str(index.query(q)),
            description="""You are an Automobile expert equipped with all the information related to all the existing cars, bikes and all its respective brands & models, features, parameters and specifications
                          who is capable of perfectly answering everything related to every automobile brands in a tabular format or list.
                          Answer using formatted tables or lists as when required.
                          If the question is not related to cars, bikes, automobiles or their related models then please let the user know that you don't have the relevant information.
                          Please answer keeping in mind the Indian context.
                          Whenever possible do Comparitive Analysis. 
                          Return the entire output in an HTML format.
                          Avoid word 'html' in output.    
                          Make sure to follow each and every instructions before giving the response.
                          """,
            return_direct=True
            ),]

num_outputs = 2000
conversational_memory = ConversationBufferWindowMemory( memory_key='chat_history', k=5, return_messages=True )
llm = OpenAI(temperature=0.5, model_name="gpt-4",max_tokens=num_outputs)

agent_executor = initialize_agent(tools, llm, agent="conversational-react-description", memory=conversational_memory,agent_kwargs={'prefix':PREFIX,'suffix': SUFFIX}, handle_parsing_errors=True)

add = "Return the output in a table format or an ordered list legible to the user.\n"
def greet(Question):
  return agent_executor.run(input=add+Question)

# demo = gr.Interface(greet, gr.Textbox(lines=2, label="Question",placeholder="What do you want to know...?",outputs="text",title="Here Auto",description="Know everything about Cars and Bikes"),["html"])
demo = gr.Interface(
    fn=greet,
    inputs=gr.Textbox(lines=2, label="Question", placeholder="What do you want to know...?"),
    outputs=gr.HTML(""),
    # examples=['what are the specifications of tata indica?'],
    title="Here Auto",
    description="Know everything about Cars and Bikes",
)

demo.launch()