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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() |