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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__,
}
def component_show_pipeline(pipeline, pipeline_name):
"""Draw the pipeline"""
with st.expander("Show pipeline"):
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 'score' in document:
st.markdown(f"**Score**: {document['score']:.3f}")
if 'content_audio' in document:
st.audio(str(document['content_audio']))
st.markdown("---")
def component_text_input(container):
"""Draw the Text Input widget"""
with container:
texts = []
doc_id = 1
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 += 1
st.markdown("---")
else:
break
corpus = [
{"text": doc["text"], "id": doc_id} for doc_id, doc in enumerate(texts)
]
return corpus
def component_article_url(container):
"""Draw the Article URL widget"""
with container:
urls = []
doc_id = 1
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 += 1
st.markdown("---")
else:
break
for idx, doc in enumerate(urls):
with st.expander(f"Preview URL {idx}"):
st.write(doc)
corpus = [
{"text": doc["text"], "id": doc_id} for doc_id, doc in enumerate(urls)
]
return corpus
def component_file_input(container):
"""Draw the extract text from file widget"""
with container:
files = []
doc_id = 1
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 += 1
st.markdown("---")
else:
break
else:
break
for idx, doc in enumerate(files):
with st.expander(f"Preview File {idx}"):
st.write(doc)
corpus = [
{"text": doc["text"], "id": doc_id} for doc_id, doc in enumerate(files)
]
return corpus
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