import streamlit as st import numpy as np from st_btn_select import st_btn_select from streamlit_option_menu import option_menu from cgi import test import streamlit as st import pandas as pd from PIL import Image import os import glob from transformers import CLIPVisionModel, AutoTokenizer, AutoModel from transformers import ViTFeatureExtractor, ViTForImageClassification import torch from tqdm import tqdm from PIL import Image import numpy as np from torch.utils.data import DataLoader from transformers import default_data_collator from torch.utils.data import Dataset, DataLoader import torchvision.transforms as transforms from bokeh.models.widgets import Button from bokeh.models import CustomJS from streamlit_bokeh_events import streamlit_bokeh_events from webcam import webcam ## Global Variables MP3_ROOT_PATH = "samples_mp3/" SPECTROGRAMS_PATH = "sample_spectrograms/" IMAGE_SIZE = 224 MEAN = torch.tensor([0.48145466, 0.4578275, 0.40821073]) STD = torch.tensor([0.26862954, 0.26130258, 0.27577711]) TEXT_MODEL = 'bert-base-uncased' CLIP_TEXT_MODEL_PATH = "text_model/" CLIP_VISION_MODEL_PATH = "vision_model/" ## NavBar def streamlit_menu(example=1): if example == 1: # 1. as sidebar menu with st.sidebar: selected = option_menu( menu_title="Main Menu", # required options=["Text", "Audio", "Camera"], # required icons=["chat-text", "mic", "camera"], # optional menu_icon="cast", # optional default_index=0, # optional ) return selected if example == 2: # 2. horizontal menu w/o custom style selected = option_menu( menu_title=None, # required options=["Text", "Audio", "Camera"], # required icons=["chat-text", "mic", "camera"], # optional menu_icon="cast", # optional default_index=0, # optional orientation="horizontal", ) return selected if example == 3: # 2. horizontal menu with custom style selected = option_menu( menu_title=None, # required options=["Text", "Audio", "Camera"], # required icons=["chat-text", "mic", "camera"], # optional menu_icon="cast", # optional default_index=0, # optional orientation="horizontal", styles={ "container": {"padding": "0!important", "background-color": "#fafafa"}, "icon": {"color": "#ffde59", "font-size": "25px"}, "nav-link": { "font-size": "25px", "text-align": "left", "margin": "0px", "--hover-color": "#eee", }, "nav-link-selected": {"background-color": "#5271ff"}, }, ) return selected ## Draw Sidebar def draw_sidebar( key, plot=False, ): st.write( """ # Sidebar ```python Think. Search. Feel. ``` """ ) st.slider("From 1 to 10, how cool is this app?", min_value=1, max_value=10, key=key) option = st_btn_select(('option1', 'option2', 'option3'), index=2) st.write(f'Selected option: {option}') ## Change Color #def change_color(styles="") ## VisionDataset class VisionDataset(Dataset): preprocess = transforms.Compose([ transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)), transforms.ToTensor(), transforms.Normalize(mean=MEAN, std=STD) ]) def __init__(self, image_paths: list): self.image_paths = image_paths def __getitem__(self, idx): return self.preprocess(Image.open(self.image_paths[idx]).convert('RGB')) def __len__(self): return len(self.image_paths) ## TextDataset class TextDataset(Dataset): def __init__(self, text: list, tokenizer, max_len): self.len = len(text) self.tokens = tokenizer(text, padding='max_length', max_length=max_len, truncation=True) def __getitem__(self, idx): token = self.tokens[idx] return {'input_ids': token.ids, 'attention_mask': token.attention_mask} def __len__(self): return self.len ## CLIP Demo class CLIPDemo: def __init__(self, vision_encoder, text_encoder, tokenizer, batch_size: int = 64, max_len: int = 64, device='cuda'): """ Initializes CLIPDemo it has the following functionalities: image_search: Search images based on text query zero_shot: Zero shot image classification analogy: Analogies with embedding space arithmetic. Args: vision_encoder: Fine-tuned vision encoder text_encoder: Fine-tuned text encoder tokenizer: Transformers tokenizer device (torch.device): Running device batch_size (int): Size of mini-batches used to embeddings max_length (int): Tokenizer max length Example: >>> demo = CLIPDemo(vision_encoder, text_encoder, tokenizer) >>> demo.compute_image_embeddings(test_df.image.to_list()) >>> demo.image_search('یک مرد و یک زن') >>> demo.zero_shot('./workers.jpg') >>> demo.anology('./sunset.jpg', additional_text='دریا') """ self.vision_encoder = vision_encoder.eval().to(device) self.text_encoder = text_encoder.eval().to(device) self.batch_size = batch_size self.device = device self.tokenizer = tokenizer self.max_len = max_len self.text_embeddings_ = None self.image_embeddings_ = None def compute_image_embeddings(self, image_paths: list): self.image_paths = image_paths dataloader = DataLoader(VisionDataset( image_paths=image_paths), batch_size=self.batch_size, num_workers=8) embeddings = [] with torch.no_grad(): bar = st.progress(0) for i, images in tqdm(enumerate(dataloader), desc='computing image embeddings'): bar.progress(int(i/len(dataloader)*100)) image_embedding = self.vision_encoder( pixel_values=images.to(self.device)).pooler_output embeddings.append(image_embedding) bar.empty() self.image_embeddings_ = torch.cat(embeddings) def compute_text_embeddings(self, text: list): self.text = text dataloader = DataLoader(TextDataset(text=text, tokenizer=self.tokenizer, max_len=self.max_len), batch_size=self.batch_size, collate_fn=default_data_collator) embeddings = [] with torch.no_grad(): for tokens in tqdm(dataloader, desc='computing text embeddings'): image_embedding = self.text_encoder(input_ids=tokens["input_ids"].to(self.device), attention_mask=tokens["attention_mask"].to(self.device)).pooler_output embeddings.append(image_embedding) self.text_embeddings_ = torch.cat(embeddings) def text_query_embedding(self, query: str = 'A happy song'): tokens = self.tokenizer(query, return_tensors='pt') with torch.no_grad(): text_embedding = self.text_encoder(input_ids=tokens["input_ids"].to(self.device), attention_mask=tokens["attention_mask"].to(self.device)).pooler_output return text_embedding def most_similars(self, embeddings_1, embeddings_2): values, indices = torch.cosine_similarity( embeddings_1, embeddings_2).sort(descending=True) return values.cpu(), indices.cpu() def image_search(self, query: str, top_k=10): """ Search images based on text query Args: query (str): text query image_paths (list[str]): a bunch of image paths top_k (int): number of relevant images """ query_embedding = self.text_query_embedding(query=query) _, indices = self.most_similars(self.image_embeddings_, query_embedding) matches = np.array(self.image_paths)[indices][:top_k] songs_path = [] for match in matches: filename = os.path.split(match)[1] filename = int(filename.replace(".jpeg", "")) audio_path = MP3_ROOT_PATH + "/" + f"{filename:06d}"[0:3] + "/" + f"{filename:06d}" songs_path.append(audio_path) return songs_path ## Draw text page def draw_text( key, plot=False, ): image = Image.open("data/logo.png") st.image(image, use_column_width="always") if 'model' not in st.session_state: #with st.spinner('We are orginizing your traks...'): text_encoder = AutoModel.from_pretrained(CLIP_TEXT_MODEL_PATH, local_files_only=True) vision_encoder = CLIPVisionModel.from_pretrained(CLIP_VISION_MODEL_PATH, local_files_only=True) tokenizer = AutoTokenizer.from_pretrained(TEXT_MODEL) model = CLIPDemo(vision_encoder=vision_encoder, text_encoder=text_encoder, tokenizer=tokenizer) model.compute_image_embeddings(glob.glob(SPECTROGRAMS_PATH + "/*.jpeg")[:1000]) st.session_state["model"] = model "" "" moods = ['-', 'angry', 'calm', 'happy', 'sad'] genres = ['-', 'house', 'pop', 'rock', 'techno'] artists = ['-', 'bad dad', 'lazy magnet', 'the astronauts', 'yan yalego'] years = ['-', '80s', '90s', '2000s', '2010s'] col1, col2 = st.columns(2) mood = col1.selectbox('Which mood do you feel right now?', moods, help="Select a mood here") genre = col2.selectbox('Which genre do you want to listen?', genres, help="Select a genre here") artist = col1.selectbox('Which artist do you like best?', artists, help="Select an artist here") year = col2.selectbox('Which period do you want to relive?', years, help="Select a period here") button_form = st.button('Search', key="button_form") st.text_input("Otherwise, describe the song you are looking for!", value="", key="sentence") button_sentence = st.button('Search', key="button_sentence") if (button_sentence and st.session_state.sentence != "") or (button_form and not (mood == "-" and artist == "-" and genre == "-" and year == "-")): if button_sentence: sentence = st.session_state.sentence elif button_form: sentence = mood if mood != "-" else "" sentence = sentence + " " + genre if genre != "-" else sentence sentence = sentence + " " + artist if artist != "-" else sentence sentence = sentence + " " + year if year != "-" else sentence song_paths = st.session_state.model.image_search(sentence) for song in song_paths: song_name = df.loc[df['track_id'] == int(song[-6:])]['track_title'].to_list()[0] artist_name = df.loc[df['track_id'] == int(song[-6:])]['artist_name'].to_list()[0] st.write('**"'+song_name+'"**' + ' by ' + artist_name) st.audio(song + ".mp3", format="audio/mp3", start_time=0) ## Draw audio page def draw_audio( key, plot=False, ): image = Image.open("data/logo.png") st.image(image, use_column_width="always") if 'model' not in st.session_state: #with st.spinner('We are orginizing your traks...'): text_encoder = AutoModel.from_pretrained(CLIP_TEXT_MODEL_PATH, local_files_only=True) vision_encoder = CLIPVisionModel.from_pretrained(CLIP_VISION_MODEL_PATH, local_files_only=True) tokenizer = AutoTokenizer.from_pretrained(TEXT_MODEL) model = CLIPDemo(vision_encoder=vision_encoder, text_encoder=text_encoder, tokenizer=tokenizer) model.compute_image_embeddings(glob.glob(SPECTROGRAMS_PATH+"/*.jpeg")[:5000]) st.session_state["model"] = model #st.session_state['model'] = CLIPDemo(vision_encoder=vision_encoder, text_encoder=text_encoder, tokenizer=tokenizer) #st.session_state.model.compute_image_embeddings(glob.glob("/data1/mlaquatra/TSOAI_hack/data/spectrograms/*.jpeg")[:100]) #st.success('Done!') "" "" st.write("Please, describe the kind of song you are looking for!") stt_button = Button(label="Start Recording", margin=[5,5,5,200], width=200, default_size=10, width_policy='auto', button_type='primary') stt_button.js_on_event("button_click", CustomJS(code=""" var recognition = new webkitSpeechRecognition(); recognition.continuous = false; recognition.interimResults = true; recognition.onresult = function (e) { var value = ""; for (var i = e.resultIndex; i < e.results.length; ++i) { if (e.results[i].isFinal) { value += e.results[i][0].transcript; } } if ( value != "") { document.dispatchEvent(new CustomEvent("GET_TEXT", {detail: value})); } } recognition.start(); """)) result = streamlit_bokeh_events( stt_button, events="GET_TEXT", key="listen", refresh_on_update=False, override_height=75, debounce_time=0) if result: if "GET_TEXT" in result: sentence = result.get("GET_TEXT") st.write('You asked for: "' + sentence + '"') song_paths = st.session_state.model.image_search(sentence) for song in song_paths: song_name = df.loc[df['track_id'] == int(song[-6:])]['track_title'].to_list()[0] artist_name = df.loc[df['track_id'] == int(song[-6:])]['artist_name'].to_list()[0] st.write('**"'+song_name+'"**' + ' by ' + artist_name) st.audio(song + ".mp3", format="audio/mp3", start_time=0) ## Draw camera page def draw_camera( key, plot=False, ): image = Image.open("data/logo.png") st.image(image, use_column_width="always") if 'model' not in st.session_state: #with st.spinner('We are orginizing your traks...'): text_encoder = AutoModel.from_pretrained(CLIP_TEXT_MODEL_PATH, local_files_only=True) vision_encoder = CLIPVisionModel.from_pretrained(CLIP_VISION_MODEL_PATH, local_files_only=True) tokenizer = AutoTokenizer.from_pretrained(TEXT_MODEL) model = CLIPDemo(vision_encoder=vision_encoder, text_encoder=text_encoder, tokenizer=tokenizer) model.compute_image_embeddings(glob.glob(SPECTROGRAMS_PATH + "/*.jpeg")[:5000]) st.session_state["model"] = model #st.session_state['model'] = CLIPDemo(vision_encoder=vision_encoder, text_encoder=text_encoder, tokenizer=tokenizer) #st.session_state.model.compute_image_embeddings(glob.glob("/data1/mlaquatra/TSOAI_hack/data/spectrograms/*.jpeg")[:100]) #st.success('Done!') "" "" st.write("Please, show us how you are feeling today!") captured_image = webcam() if captured_image is None: st.write("Waiting for capture...") else: # st.write("Got an image from the webcam:") # st.image(captured_image) # st.write(type(captured_image)) # st.write(captured_image) # st.write(captured_image.size) captured_image = captured_image.convert("RGB") vit_feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k") vit_model = ViTForImageClassification.from_pretrained("ViT_ER/best_checkpoint", local_files_only=True) inputs = vit_feature_extractor(images=[captured_image], return_tensors="pt") outputs = vit_model(**inputs, output_hidden_states=True) #st.write(outputs) emotions = ['Anger', 'Disgust', 'Fear', 'Happiness', 'Sadness', 'Surprise', 'Neutral'] mood = emotions[np.argmax(outputs.logits.detach().cpu().numpy())] #st.write(mood) st.write(f"Your mood seems to be **{mood.lower()}** today! Here's a song for you that matches with how you feel!") song_paths = st.session_state.model.image_search(mood) for song in song_paths: song_name = df.loc[df['track_id'] == int(song[-6:])]['track_title'].to_list()[0] artist_name = df.loc[df['track_id'] == int(song[-6:])]['artist_name'].to_list()[0] st.write('**"'+song_name+'"**' + ' by ' + artist_name) st.audio(song + ".mp3", format="audio/mp3", start_time=0) ## Main selected = streamlit_menu(example=3) df = pd.read_csv('full_metadata.csv', index_col=False) if selected == "Text": # st.title(f"You have selected {selected}") draw_text("text", plot=True) if selected == "Audio": # st.title(f"You have selected {selected}") draw_audio("audio", plot=True) if selected == "Camera": # st.title(f"You have selected {selected}") #draw_camera("camera", plot=True) continue # with st.sidebar: # draw_sidebar("sidebar")