Spaces:
Runtime error
Runtime error
File size: 4,617 Bytes
7606e16 c006ab1 7606e16 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 |
import os
from typing import Dict, List
import datasets
import pandas as pd
import sentence_transformers
import streamlit as st
from findkit import feature_extractors, indexes, retrieval_pipeline
from toolz import partial
def truncate_description(description, length=50):
return " ".join(description.split()[:length])
def get_repos_with_descriptions(repos_df, repos):
return repos_df.loc[repos]
def search_f(
retrieval_pipe: retrieval_pipeline.RetrievalPipeline,
query: str,
k: int,
description_length: int,
doc_col: List[str],
):
results = retrieval_pipe.find_similar(query, k)
# results['repo'] = results.index
results["link"] = "https://github.com/" + results["repo"]
for col in doc_col:
results[col] = results[col].apply(
lambda desc: truncate_description(desc, description_length)
)
shown_cols = ["repo", "tasks", "link", "distance"]
shown_cols = shown_cols + doc_col
return results.reset_index(drop=True)[shown_cols]
def show_retrieval_results(
retrieval_pipe: retrieval_pipeline.RetrievalPipeline,
query: str,
k: int,
all_queries: List[str],
description_length: int,
repos_by_query: Dict[str, pd.DataFrame],
doc_col: str,
):
print("started retrieval")
if query in all_queries:
with st.expander(
"query is in gold standard set queries. Toggle viewing gold standard results?"
):
st.write("gold standard results")
task_repos = repos_by_query.get_group(query)
st.table(get_repos_with_descriptions(retrieval_pipe.X_df, task_repos))
with st.spinner(text="fetching results"):
st.write(
search_f(retrieval_pipe, query, k, description_length, doc_col).to_html(
escape=False, index=False
),
unsafe_allow_html=True,
)
print("finished retrieval")
def setup_pipeline(
extractor: feature_extractors.SentenceEncoderFeatureExtractor,
documents_df: pd.DataFrame,
text_col: str,
):
retrieval_pipeline.RetrievalPipelineFactory.build(
documents_df[text_col], metadata=documents_df
)
@st.cache
def setup_retrieval_pipeline(
query_encoder_path, document_encoder_path, documents, metadata
):
document_encoder = feature_extractors.SentenceEncoderFeatureExtractor(
sentence_transformers.SentenceTransformer(document_encoder_path, device="cpu")
)
query_encoder = feature_extractors.SentenceEncoderFeatureExtractor(
sentence_transformers.SentenceTransformer(query_encoder_path, device="cpu")
)
retrieval_pipe = retrieval_pipeline.RetrievalPipelineFactory(
feature_extractor=document_encoder,
query_feature_extractor=query_encoder,
index_factory=partial(indexes.NMSLIBIndex.build, distance="cosinesimil"),
)
return retrieval_pipe.build(documents, metadata=metadata)
def app(retrieval_pipeline, retrieval_df, doc_col):
retrieved_results = st.sidebar.number_input("number of results", value=10)
description_length = st.sidebar.number_input(
"number of used description words", value=10
)
tasks_deduped = (
retrieval_df["tasks"].explode().value_counts().reset_index()
) # drop_duplicates().sort_values().reset_index(drop=True)
tasks_deduped.columns = ["task", "documents per task"]
with st.sidebar.expander("View test set queries"):
st.table(tasks_deduped.explode("task"))
additional_shown_cols = st.sidebar.multiselect(
label="additional cols", options=[doc_col], default=doc_col
)
repos_by_query = retrieval_df.explode("tasks").groupby("tasks")
query = st.text_input("input query", value="metric learning")
show_retrieval_results(
retrieval_pipeline,
query,
retrieved_results,
tasks_deduped["task"].to_list(),
description_length,
repos_by_query,
additional_shown_cols,
)
def app_main(
query_encoder_path,
document_encoder_path,
data_path,
):
print("loading data")
retrieval_df = datasets.load_dataset(data_path)["train"].to_pandas()
print("setting up retrieval_pipe")
doc_col = "dependencies"
retrieval_pipeline = setup_retrieval_pipeline(
query_encoder_path, document_encoder_path, retrieval_df[doc_col], retrieval_df
)
app(retrieval_pipeline, retrieval_df, doc_col)
app_main(
query_encoder_path="lambdaofgod/query_nbow_1_2000-5",
document_encoder_path="lambdaofgod/document_nbow_1_2000-5",
data_path="lambdaofgod/pwc_repositories_with_dependencies",
)
|