initial commit

app.py

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+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")

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