Spaces:
Runtime error
Runtime error
File size: 2,407 Bytes
828c90a 0d78964 828c90a 0d78964 828c90a 0d78964 828c90a bbe9324 0d78964 828c90a 0d78964 828c90a 0d78964 828c90a |
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 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
import os
import cohere
import gradio as gr
import numpy as np
import pinecone
import torch
from transformers import AutoModel, AutoTokenizer
co = cohere.Client(os.environ.get('COHERE_API', ''))
pinecone.init(
api_key=os.environ.get('PINECONE_API', ''),
environment=os.environ.get('PINECONE_ENV', '')
)
model = AutoModel.from_pretrained('monsoon-nlp/gpt-nyc')
tokenizer = AutoTokenizer.from_pretrained('monsoon-nlp/gpt-nyc')
zos = np.zeros(4096-1024).tolist()
def list_me(matches):
result = ''
for match in matches:
result += '<li><a target="_blank" href="https://reddit.com/r/AskNYC/comments/' + match['id'] + '">'
result += match['metadata']['question']
result += '</a>'
if 'body' in match['metadata']:
result += '<br/>' + match['metadata']['body']
result += '</li>'
return result.replace('/mini', '/')
def query(question):
# Cohere search
response = co.embed(
model='large',
texts=[question],
)
index = pinecone.Index("gptnyc")
closest = index.query(
top_k=2,
include_metadata=True,
vector=response.embeddings[0],
)
# SGPT search
batch_tokens = tokenizer(
[question],
padding=True,
truncation=True,
return_tensors="pt"
)
with torch.no_grad():
last_hidden_state = model(**batch_tokens, output_hidden_states=True, return_dict=True).last_hidden_state
weights = (
torch.arange(start=1, end=last_hidden_state.shape[1] + 1)
.unsqueeze(0)
.unsqueeze(-1)
.expand(last_hidden_state.size())
.float().to(last_hidden_state.device)
)
input_mask_expanded = (
batch_tokens["attention_mask"]
.unsqueeze(-1)
.expand(last_hidden_state.size())
.float()
)
sum_embeddings = torch.sum(last_hidden_state * input_mask_expanded * weights, dim=1)
sum_mask = torch.sum(input_mask_expanded * weights, dim=1)
embeddings = sum_embeddings / sum_mask
closest_sgpt = index.query(
top_k=2,
include_metadata=True,
namespace="mini",
vector=embeddings[0].tolist() + zos,
)
return '<h3>Cohere</h3><ul>' + list_me(closest['matches']) + '</ul><h3>SGPT</h3><ul>' + list_me(closest_sgpt['matches']) + '</ul>'
iface = gr.Interface(
fn=query,
inputs="text",
outputs="html"
)
iface.launch()
|