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| import gradio as gr | |
| import openai | |
| import pandas as pd | |
| import numpy as np | |
| import csv | |
| openai.api_key="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" | |
| from openai.embeddings_utils import get_embedding | |
| from openai.embeddings_utils import cosine_similarity | |
| df = pd.read_csv("TA_embeddings.csv") | |
| df["embedding"]=df["embedding"].apply(eval).apply(np.array) | |
| def reply(input): | |
| input = input | |
| input_vector = get_embedding(input, engine="text-embedding-ada-002") | |
| df["similiarities"]=df["embedding"].apply(lambda x: cosine_similarity(x,input_vector)) | |
| data = df.sort_values("similiarities", ascending=False).head(20) | |
| data.to_csv("sorted.csv") | |
| context = [] | |
| for i, row in data.iterrows(): | |
| context.append(row['text']) | |
| context | |
| text = "\n".join(context) | |
| context = text | |
| prompt = f""" | |
| Answer the following question If you don't know the answer for certain, say I don't know. | |
| Context: {context} | |
| Q: {input} | |
| """ | |
| return openai.Completion.create( | |
| prompt=prompt, | |
| temperature=1, | |
| max_tokens=500, | |
| top_p=1, | |
| frequency_penalty=0, | |
| presence_penalty=0, | |
| model="text-davinci-003" | |
| )["choices"][0]["text"].strip(" \n") | |
| input_text = gr.inputs.Textbox(label="Enter your text here") | |
| text_output = gr.outputs.Textbox(label="Most similar text") | |
| ui = gr.Interface(fn=reply, | |
| inputs=input_text, | |
| outputs=[text_output], | |
| theme="compact", | |
| layout="vertical", | |
| inputs_layout="stacked", | |
| outputs_layout="stacked", | |
| allow_flagging=False) | |
| ui.launch() |