| from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper |
| from langchain import OpenAI |
| import gradio as gr |
| import sys |
| import os |
|
|
| os.environ["OPENAI_API_KEY"] = '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' |
|
|
| def construct_index(directory_path): |
| max_input_size = 4096 |
| num_outputs = 512 |
| max_chunk_overlap = 20 |
| chunk_size_limit = 600 |
|
|
| prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit) |
|
|
| llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.7, model_name='GPT4', max_tokens=num_outputs)) |
|
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| |
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| |
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|
| return index |
|
|
| def chatbot(input_text): |
| index = GPTSimpleVectorIndex.load_from_disk('index.json') |
| response = index.query(input_text, response_mode="compact") |
| return response.response |
|
|
| iface = gr.Interface(fn=chatbot, |
| inputs=gr.components.Textbox(lines=7, label="Enter your text"), |
| outputs="text", |
| title="Custom-trained AI Chatbot") |
|
|
|
|
| iface.launch() |
