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File size: 2,703 Bytes
89915a3 f3d3104 89915a3 f3d3104 89915a3 fa736bb 89915a3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | import pandas as pd
df=pd.read_csv('https://raw.githubusercontent.com/aiquotient-chatbot/oss-enterprise/main/NewQuip%20-%20Case%20Studies.csv')
df.head()
import pandas as pd
from sentence_transformers import SentenceTransformer
def preprocess_posts(csv_file):
df = pd.read_csv(csv_file)
model = SentenceTransformer('bert-base-nli-mean-tokens')
post_embeddings = model.encode(df['Summary'])
return post_embeddings, model, df[['Summary']]
post_embeddings, model, post_data = preprocess_posts('https://raw.githubusercontent.com/aiquotient-chatbot/oss-enterprise/main/NewQuip%20-%20Case%20Studies.csv')
import numpy as np
from scipy.spatial.distance import cosine
def get_most_similar_post(query, post_embeddings, model, post_data):
query_embedding = model.encode([query])[0]
similarity_scores = [1 - cosine(query_embedding, post_embedding) for post_embedding in post_embeddings]
most_similar_index = np.argmax(similarity_scores)
return post_data.iloc[most_similar_index]
import openai
openai.api_key = "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"
def generate_answer(query, post_text):
response = openai.Completion.create(
engine="text-davinci-002",
prompt=f"{query} {post_text}",
max_tokens=1024,
n=1,
stop=None,
temperature=0.5,
)
answer = response["choices"][0]["text"].strip()
return answer
import gradio as gr
def answer_generating_interface(query):
result = get_most_similar_post(query, post_embeddings, model, post_data)
answer = generate_answer(query, result['Summary'])
text = answer.replace("Share\nSave\nHide\n2\nUnfollow\nFollowing\nShare\nSort by Best\n\n", "")
text = text.replace("Share", "")
return text
#iface = gr.Interface(answer_generating_interface,
# gr.inputs.Textbox(default=query), gr.outputs.Textbox(),
# title="AI Tutor: Improve your AI Quotient",
# description="Enter a question around an AI concept and get the answer")
iface = gr.Interface(answer_generating_interface,
gr.inputs.Textbox(default="How does AirBnB use ML to forecast demand?"), gr.outputs.Textbox(),
examples=[['How does Twitter use Machine Learning?', 'What is logistic regression?'],
['How does Uber use ML to forecast demand?', 'What is random forest?']],
title="AI Enterprise Explorer: Improve your AI Quotient",
description="Enter a question around how a company is using AI and get the answer",allow_flagging="manual",flagging_options=["wrong response", "correct response"])
iface.launch(debug=True)
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