Spaces:
Runtime error
Runtime error
File size: 2,989 Bytes
19a75cb c543e79 19a75cb c543e79 1c87512 c543e79 e9fae98 c543e79 f48ca20 c543e79 e9fae98 c543e79 19a75cb |
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 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
import gradio as gr
from huggingface_hub import InferenceClient
import json
import pandas as pd
import numpy as np
import torch
from sentence_transformers import SentenceTransformer
import nltk
from nltk.tokenize import sent_tokenize
import faiss
from langchain_text_splitters import RecursiveCharacterTextSplitter
optimus = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
textsplitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
dbpath = r''##push datasets to the hub to use here##
gridpath = r''
with open(dbpath) as f:
papers = [json.loads(line) for line in f]
df = pd.DataFrame(papers)
reqdf = df[['id', 'title', 'categories', 'abstract']]
d = 384
index = faiss.IndexFlatL2(d)
thegird = []#load the grid and index from json file here
def gen_embeddings(text):
sentences = sent_tokenize(text)
embeddings = optimus.encode(sentences)
return embeddings
query_list = gen_embeddings(query)
if len(query_list) > 1:
query_list = torch.mean(query_list, dim=0)
query_matrix = np.array(query_list).astype('float32').reshape(-1,1)
k = 25
distances, indices = index.search(query_matrix, k)
result_texts = [thegrid[idx]['text'] for idx in indices[0]]
for i, text in enumerate(result_texts):
printres = f"Match {i+1}: {text}"
searched_topics = []
idcache = []
for text in result_texts:
rowid = text.split("|||")[0]
if rowid in idcache:
break;
else:
topic = reqdf.loc[reqdf['id'] == rowid, 'title'].values[0]
searched_topics.append(rowid)
idcache.append(rowid)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch()
|