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| import time | |
| import sys | |
| import streamlit as st | |
| import string | |
| from io import StringIO | |
| import pdb | |
| import json | |
| from twc_embeddings import HFModel,SimCSEModel,SGPTModel,CausalLMModel,SGPTQnAModel | |
| from twc_clustering import TWCClustering | |
| import torch | |
| import requests | |
| import socket | |
| MAX_INPUT = 100 | |
| SEM_SIMILARITY="1" | |
| DOC_RETRIEVAL="2" | |
| CLUSTERING="3" | |
| use_case = {"1":"Finding similar phrases/sentences","2":"Retrieving semantically matching information to a query. It may not be a factual match","3":"Clustering"} | |
| use_case_url = {"1":"https://huggingface.co/spaces/taskswithcode/semantic_similarity","2":"https://huggingface.co/spaces/taskswithcode/semantic_search","3":""} | |
| from transformers import BertTokenizer, BertForMaskedLM | |
| APP_NAME = "hf/semantic_clustering" | |
| INFO_URL = "http://www.taskswithcode.com/stats/" | |
| def get_views(action): | |
| ret_val = 0 | |
| hostname = socket.gethostname() | |
| ip_address = socket.gethostbyname(hostname) | |
| if ("view_count" not in st.session_state): | |
| try: | |
| app_info = {'name': APP_NAME,"action":action,"host":hostname,"ip":ip_address} | |
| res = requests.post(INFO_URL, json = app_info).json() | |
| print(res) | |
| data = res["count"] | |
| except: | |
| data = 0 | |
| ret_val = data | |
| st.session_state["view_count"] = data | |
| else: | |
| ret_val = st.session_state["view_count"] | |
| if (action != "init"): | |
| app_info = {'name': APP_NAME,"action":action,"host":hostname,"ip":ip_address} | |
| res = requests.post(INFO_URL, json = app_info).json() | |
| return "{:,}".format(ret_val) | |
| def construct_model_info_for_display(model_names): | |
| options_arr = [] | |
| markdown_str = f"<div style=\"font-size:16px; color: #2f2f2f; text-align: left\"><br/><b>Models evaluated ({len(model_names)})</b><br/><i>These are either state-of-the-art or the most downloaded models on Huggingface</i></div>" | |
| markdown_str += f"<div style=\"font-size:2px; color: #2f2f2f; text-align: left\"><br/></div>" | |
| for node in model_names: | |
| options_arr .append(node["name"]) | |
| if (node["mark"] == "True"): | |
| markdown_str += f"<div style=\"font-size:16px; color: #5f5f5f; text-align: left\"> • Model: <a href=\'{node['paper_url']}\' target='_blank'>{node['name']}</a><br/> Code released by: <a href=\'{node['orig_author_url']}\' target='_blank'>{node['orig_author']}</a><br/> Model info: <a href=\'{node['sota_info']['sota_link']}\' target='_blank'>{node['sota_info']['task']}</a></div>" | |
| if ("Note" in node): | |
| markdown_str += f"<div style=\"font-size:16px; color: #a91212; text-align: left\"> {node['Note']}<a href=\'{node['alt_url']}\' target='_blank'>link</a></div>" | |
| markdown_str += "<div style=\"font-size:16px; color: #5f5f5f; text-align: left\"><br/></div>" | |
| markdown_str += "<div style=\"font-size:12px; color: #9f9f9f; text-align: left\"><b>Note:</b><br/>• Uploaded files are loaded into non-persistent memory for the duration of the computation. They are not cached</div>" | |
| limit = "{:,}".format(MAX_INPUT) | |
| markdown_str += f"<div style=\"font-size:12px; color: #9f9f9f; text-align: left\">• User uploaded file has a maximum limit of {limit} sentences.</div>" | |
| return options_arr,markdown_str | |
| st.set_page_config(page_title='TWC - Compare popular/state-of-the-art models for tasks using sentence embeddings', page_icon="logo.jpg", layout='centered', initial_sidebar_state='auto', | |
| menu_items={ | |
| 'About': 'This app was created by taskswithcode. http://taskswithcode.com' | |
| }) | |
| col,pad = st.columns([85,15]) | |
| with col: | |
| st.image("long_form_logo_with_icon.png") | |
| def load_model(model_name,model_class,load_model_name): | |
| try: | |
| ret_model = None | |
| obj_class = globals()[model_class] | |
| ret_model = obj_class() | |
| ret_model.init_model(load_model_name) | |
| assert(ret_model is not None) | |
| except Exception as e: | |
| st.error("Unable to load model:" + model_name + " " + load_model_name + " " + str(e)) | |
| pass | |
| return ret_model | |
| def cached_compute_similarity(sentences,_model,model_name,threshold,_cluster,clustering_type): | |
| texts,embeddings = _model.compute_embeddings(sentences,is_file=False) | |
| results = _cluster.cluster(None,texts,embeddings,threshold,clustering_type) | |
| return results | |
| def uncached_compute_similarity(sentences,_model,model_name,threshold,cluster,clustering_type): | |
| with st.spinner('Computing vectors for sentences'): | |
| texts,embeddings = _model.compute_embeddings(sentences,is_file=False) | |
| results = cluster.cluster(None,texts,embeddings,threshold,clustering_type) | |
| #st.success("Similarity computation complete") | |
| return results | |
| DEFAULT_HF_MODEL = "sentence-transformers/paraphrase-MiniLM-L6-v2" | |
| def get_model_info(model_names,model_name): | |
| for node in model_names: | |
| if (model_name == node["name"]): | |
| return node,model_name | |
| return get_model_info(model_names,DEFAULT_HF_MODEL) | |
| def run_test(model_names,model_name,sentences,display_area,threshold,user_uploaded,custom_model,clustering_type): | |
| display_area.text("Loading model:" + model_name) | |
| #Note. model_name may get mapped to new name in the call below for custom models | |
| orig_model_name = model_name | |
| model_info,model_name = get_model_info(model_names,model_name) | |
| if (model_name != orig_model_name): | |
| load_model_name = orig_model_name | |
| else: | |
| load_model_name = model_info["model"] | |
| if ("Note" in model_info): | |
| fail_link = f"{model_info['Note']} [link]({model_info['alt_url']})" | |
| display_area.write(fail_link) | |
| model = load_model(model_name,model_info["class"],load_model_name) | |
| display_area.text("Model " + model_name + " load complete") | |
| try: | |
| if (user_uploaded): | |
| results = uncached_compute_similarity(sentences,model,model_name,threshold,st.session_state["cluster"],clustering_type) | |
| else: | |
| display_area.text("Computing vectors for sentences") | |
| results = cached_compute_similarity(sentences,model,model_name,threshold,st.session_state["cluster"],clustering_type) | |
| display_area.text("Similarity computation complete") | |
| return results | |
| except Exception as e: | |
| st.error("Some error occurred during prediction" + str(e)) | |
| st.stop() | |
| return {} | |
| def display_results(orig_sentences,results,response_info,app_mode,model_name): | |
| main_sent = f"<div style=\"font-size:14px; color: #2f2f2f; text-align: left\">{response_info}<br/><br/></div>" | |
| main_sent += f"<div style=\"font-size:14px; color: #2f2f2f; text-align: left\">Showing results for model: <b>{model_name}</b></div>" | |
| score_text = "cosine distance" | |
| main_sent += f"<div style=\"font-size:14px; color: #6f6f6f; text-align: left\">Clustering by {score_text}. <b>{len(results['clusters'])} clusters</b>. mean:{results['info']['mean']:.2f}; std:{results['info']['std']:.2f}; current threshold:{results['info']['current_threshold']}<br/>Threshold hints:{str(results['info']['zscores'])}<br/>Overlap stats(overlap,freq):{str(results['info']['overlap'])}</div>" | |
| body_sent = [] | |
| download_data = {} | |
| for i in range(len(results["clusters"])): | |
| pivot_index = results["clusters"][i]["pivot_index"] | |
| pivot_sent = orig_sentences[pivot_index] | |
| pivot_index += 1 | |
| d_cluster = {} | |
| download_data[i + 1] = d_cluster | |
| d_cluster["pivot"] = {"pivot_index":pivot_index,"sent":pivot_sent,"children":{}} | |
| body_sent.append(f"<div style=\"font-size:16px; color: #2f2f2f; text-align: left\">{pivot_index}] {pivot_sent} <b><i>(Cluster {i+1})</i></b> </div>") | |
| neighs_dict = results["clusters"][i]["neighs"] | |
| for key in neighs_dict: | |
| cosine_dist = neighs_dict[key] | |
| child_index = key | |
| sentence = orig_sentences[child_index] | |
| child_index += 1 | |
| body_sent.append(f"<div style=\"font-size:16px; color: #2f2f2f; text-align: left\">{child_index}] {sentence} <b>{cosine_dist:.2f}</b></div>") | |
| d_cluster["pivot"]["children"][sentence] = f"{cosine_dist:.2f}" | |
| body_sent.append(f"<div style=\"font-size:16px; color: #2f2f2f; text-align: left\"> </div>") | |
| main_sent = main_sent + "\n" + '\n'.join(body_sent) | |
| st.markdown(main_sent,unsafe_allow_html=True) | |
| st.session_state["download_ready"] = json.dumps(download_data,indent=4) | |
| get_views("submit") | |
| def init_session(): | |
| if ("model_name" not in st.session_state): | |
| st.session_state["model_name"] = "ss_test" | |
| st.session_state["download_ready"] = None | |
| st.session_state["model_name"] = "ss_test" | |
| st.session_state["threshold"] = 1.5 | |
| st.session_state["file_name"] = "default" | |
| st.session_state["overlapped"] = "overlapped" | |
| st.session_state["cluster"] = TWCClustering() | |
| else: | |
| print("Skipping init session") | |
| def app_main(app_mode,example_files,model_name_files,clus_types): | |
| init_session() | |
| with open(example_files) as fp: | |
| example_file_names = json.load(fp) | |
| with open(model_name_files) as fp: | |
| model_names = json.load(fp) | |
| with open(clus_types) as fp: | |
| cluster_types = json.load(fp) | |
| curr_use_case = use_case[app_mode].split(".")[0] | |
| st.markdown("<h5 style='text-align: center;'>Compare popular/state-of-the-art models for tasks using sentence embeddings</h5>", unsafe_allow_html=True) | |
| st.markdown(f"<p style='font-size:14px; color: #4f4f4f; text-align: center'><i>Or compare your own model with state-of-the-art/popular models</p>", unsafe_allow_html=True) | |
| st.markdown(f"<div style='color: #4f4f4f; text-align: left'>Use cases for sentence embeddings<br/> • <a href=\'{use_case_url['1']}\' target='_blank'>{use_case['1']}</a><br/> • <a href=\'{use_case_url['2']}\' target='_blank'>{use_case['2']}</a><br/> • {use_case['3']}<br/><i>This app illustrates <b>'{curr_use_case}'</b> use case</i></div>", unsafe_allow_html=True) | |
| st.markdown(f"<div style='color: #9f9f9f; text-align: right'>views: {get_views('init')}</div>", unsafe_allow_html=True) | |
| try: | |
| with st.form('twc_form'): | |
| step1_line = "Upload text file(one sentence in a line) or choose an example text file below" | |
| if (app_mode == DOC_RETRIEVAL): | |
| step1_line += ". The first line is treated as the query" | |
| uploaded_file = st.file_uploader(step1_line, type=".txt") | |
| selected_file_index = st.selectbox(label=f'Example files ({len(example_file_names)})', | |
| options = list(dict.keys(example_file_names)), index=0, key = "twc_file") | |
| st.write("") | |
| options_arr,markdown_str = construct_model_info_for_display(model_names) | |
| selection_label = 'Select Model' | |
| selected_model = st.selectbox(label=selection_label, | |
| options = options_arr, index=0, key = "twc_model") | |
| st.write("") | |
| custom_model_selection = st.text_input("Model not listed above? Type any Huggingface sentence embedding model name ", "",key="custom_model") | |
| hf_link_str = "<div style=\"font-size:12px; color: #9f9f9f; text-align: left\"><a href='https://huggingface.co/models?pipeline_tag=sentence-similarity' target = '_blank'>List of Huggingface sentence embedding models</a><br/><br/><br/></div>" | |
| st.markdown(hf_link_str, unsafe_allow_html=True) | |
| threshold = st.number_input('Choose a zscore threshold (number of std devs from mean)',value=st.session_state["threshold"],min_value = 0.0,step=.01) | |
| st.write("") | |
| clustering_type = st.selectbox(label=f'Select type of clustering', | |
| options = list(dict.keys(cluster_types)), index=0, key = "twc_cluster_types") | |
| st.write("") | |
| submit_button = st.form_submit_button('Run') | |
| input_status_area = st.empty() | |
| display_area = st.empty() | |
| if submit_button: | |
| start = time.time() | |
| if uploaded_file is not None: | |
| st.session_state["file_name"] = uploaded_file.name | |
| sentences = StringIO(uploaded_file.getvalue().decode("utf-8")).read() | |
| else: | |
| st.session_state["file_name"] = example_file_names[selected_file_index]["name"] | |
| sentences = open(example_file_names[selected_file_index]["name"]).read() | |
| sentences = sentences.split("\n")[:-1] | |
| if (len(sentences) > MAX_INPUT): | |
| st.info(f"Input sentence count exceeds maximum sentence limit. First {MAX_INPUT} out of {len(sentences)} sentences chosen") | |
| sentences = sentences[:MAX_INPUT] | |
| if (len(custom_model_selection) != 0): | |
| run_model = custom_model_selection | |
| else: | |
| run_model = selected_model | |
| st.session_state["model_name"] = selected_model | |
| st.session_state["threshold"] = threshold | |
| st.session_state["overlapped"] = cluster_types[clustering_type]["type"] | |
| results = run_test(model_names,run_model,sentences,display_area,threshold,(uploaded_file is not None),(len(custom_model_selection) != 0),cluster_types[clustering_type]["type"]) | |
| display_area.empty() | |
| with display_area.container(): | |
| device = 'GPU' if torch.cuda.is_available() else 'CPU' | |
| response_info = f"Computation time on {device}: {time.time() - start:.2f} secs for {len(sentences)} sentences" | |
| if (len(custom_model_selection) != 0): | |
| st.info("Custom model overrides model selection in step 2 above. So please clear the custom model text box to choose models from step 2") | |
| display_results(sentences,results,response_info,app_mode,run_model) | |
| #st.json(results) | |
| st.download_button( | |
| label="Download results as json", | |
| data= st.session_state["download_ready"] if st.session_state["download_ready"] != None else "", | |
| disabled = False if st.session_state["download_ready"] != None else True, | |
| file_name= (st.session_state["model_name"] + "_" + str(st.session_state["threshold"]) + "_" + st.session_state["overlapped"] + "_" + '_'.join(st.session_state["file_name"].split(".")[:-1]) + ".json").replace("/","_"), | |
| mime='text/json', | |
| key ="download" | |
| ) | |
| except Exception as e: | |
| st.error("Some error occurred during loading" + str(e)) | |
| st.stop() | |
| st.markdown(markdown_str, unsafe_allow_html=True) | |
| if __name__ == "__main__": | |
| #print("comand line input:",len(sys.argv),str(sys.argv)) | |
| #app_main(sys.argv[1],sys.argv[2],sys.argv[3]) | |
| #app_main("1","sim_app_examples.json","sim_app_models.json") | |
| app_main("3","clus_app_examples.json","clus_app_models.json","clus_app_clustypes.json") | |