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