Spaces:
Runtime error
Runtime error
import streamlit as st | |
from interface.utils import get_pipelines, extract_text_from_url, extract_text_from_file | |
from interface.draw_pipelines import get_pipeline_graph | |
def component_select_pipeline(container): | |
pipeline_names, pipeline_funcs, pipeline_func_parameters = get_pipelines() | |
with container: | |
selected_pipeline = st.selectbox( | |
"Select pipeline", | |
pipeline_names, | |
index=pipeline_names.index("Keyword Search") | |
if "Keyword Search" in pipeline_names | |
else 0, | |
) | |
index_pipe = pipeline_names.index(selected_pipeline) | |
st.write("---") | |
st.header("Pipeline Parameters") | |
for parameter, value in pipeline_func_parameters[index_pipe].items(): | |
if isinstance(value, str): | |
value = st.text_input(parameter, value) | |
elif isinstance(value, bool): | |
value = st.checkbox(parameter, value) | |
elif isinstance(value, int): | |
value = int(st.number_input(parameter, value)) | |
elif isinstance(value, float): | |
value = float(st.number_input(parameter, value)) | |
pipeline_func_parameters[index_pipe][parameter] = value | |
if ( | |
st.session_state["pipeline"] is None | |
or st.session_state["pipeline"]["name"] != selected_pipeline | |
or list(st.session_state["pipeline_func_parameters"][index_pipe].values()) | |
!= list(pipeline_func_parameters[index_pipe].values()) | |
): | |
st.session_state["pipeline_func_parameters"] = pipeline_func_parameters | |
(search_pipeline, index_pipeline,) = pipeline_funcs[ | |
index_pipe | |
](**pipeline_func_parameters[index_pipe]) | |
st.session_state["pipeline"] = { | |
"name": selected_pipeline, | |
"search_pipeline": search_pipeline, | |
"index_pipeline": index_pipeline, | |
"doc": pipeline_funcs[index_pipe].__doc__, | |
} | |
st.session_state["doc_id"] = 0 | |
def component_show_pipeline(pipeline, pipeline_name): | |
"""Draw the pipeline""" | |
expander_text = "Show pipeline" | |
if pipeline["doc"] is not None and "BUG" in pipeline["doc"]: | |
expander_text += " ⚠️" | |
with st.expander(expander_text): | |
if pipeline["doc"] is not None: | |
st.markdown(pipeline["doc"]) | |
fig = get_pipeline_graph(pipeline[pipeline_name]) | |
st.plotly_chart(fig, use_container_width=True) | |
def component_show_search_result(container, results): | |
with container: | |
for idx, document in enumerate(results): | |
st.markdown(f"### Match {idx+1}") | |
st.markdown(f"**Text**: {document['text']}") | |
st.markdown(f"**Document**: {document['id']}") | |
if "_split_id" in document["meta"]: | |
st.markdown(f"**Document Chunk**: {document['meta']['_split_id']}") | |
if document["score"] is not None: | |
st.markdown(f"**Score**: {document['score']:.3f}") | |
st.markdown("---") | |
def component_text_input(container, doc_id): | |
"""Draw the Text Input widget""" | |
with container: | |
texts = [] | |
with st.expander("Enter documents"): | |
while True: | |
text = st.text_input(f"Document {doc_id}", key=doc_id) | |
if text != "": | |
texts.append({"text": text, "doc_id": doc_id}) | |
doc_id += 1 | |
st.markdown("---") | |
else: | |
break | |
corpus = [{"text": doc["text"], "id": doc["doc_id"]} for doc in texts] | |
return corpus, doc_id | |
def component_article_url(container, doc_id): | |
"""Draw the Article URL widget""" | |
with container: | |
urls = [] | |
with st.expander("Enter URLs"): | |
while True: | |
url = st.text_input(f"URL {doc_id}", key=doc_id) | |
if url != "": | |
urls.append({"text": extract_text_from_url(url), "doc_id": doc_id}) | |
doc_id += 1 | |
st.markdown("---") | |
else: | |
break | |
for idx, doc in enumerate(urls): | |
with st.expander(f"Preview URL {idx}"): | |
st.write(doc["text"]) | |
corpus = [{"text": doc["text"], "id": doc["doc_id"]} for doc in urls] | |
return corpus, doc_id | |
def component_file_input(container, doc_id): | |
"""Draw the extract text from file widget""" | |
with container: | |
files = [] | |
with st.expander("Enter Files"): | |
while True: | |
file = st.file_uploader( | |
"Upload a .txt, .pdf, .csv, image file", key=doc_id | |
) | |
if file != None: | |
extracted_text = extract_text_from_file(file) | |
if extracted_text != None: | |
files.append({"text": extracted_text, "doc_id": doc_id}) | |
doc_id += 1 | |
st.markdown("---") | |
else: | |
break | |
else: | |
break | |
for idx, doc in enumerate(files): | |
with st.expander(f"Preview File {idx}"): | |
st.write(doc["text"]) | |
corpus = [{"text": doc["text"], "id": doc["doc_id"]} for doc in files] | |
return corpus, doc_id | |