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import gradio as gr
import json
import os
from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
from langchain import OpenAI
import sys
from IPython.display import Markdown, display
def construct_index(directory_path):
max_input_size = 4096
num_outputs = 2000
max_chunk_overlap = 20
chunk_size_limit = 600
llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="gpt-3.5-turbo", max_tokens=num_outputs))
prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
def ask_ai(question,api):
index = GPTSimpleVectorIndex.load_from_disk('index.json')
response = index.query(question, response_mode="compact")
return response.response
os.environ["OPENAI_API_KEY"] = "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"
construct_index("data")
iface = gr.Interface(fn=ask_ai, inputs="text", outputs="text" ,title="Chatbot")
iface.launch() |