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app.py

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+import cv2
+import numpy as np
+import tempfile
+import time
+import streamlit as st
+from PIL import Image 
+from io import BytesIO
+import plotly.graph_objs as go
+from transformers import CLIPProcessor, CLIPModel
+from torch.cuda import is_available
+
+MODEL_ID = "openai/clip-vit-base-patch32"
+
+DEMO_IMAGE = 'demo.jpg'
+
+EMOTION_DICT = {
+  0: ['Angry', '😡'],
+  1: ['Disgusted', '🤢'],
+  2: ['Fearful', '😨'],
+  3: ['Happy', '😃'],
+  4: ['Neutral', '😐'],
+  5: ['Sad', '☹️'],
+  6: ['Surprised', '😮']
+}
+
+device = 'cuda' if is_available() else 'cpu'
+
+@st.cache_data
+def load_model():
+  processor = CLIPProcessor.from_pretrained(MODEL_ID)
+  model = CLIPModel.from_pretrained(MODEL_ID)
+  return processor, model
+
+@st.cache_data
+def load_token_embds():
+  emotions = list(EMOTION_DICT.values())
+  desc = [f'a photo of a {emotion[0]} person' for emotion in emotions]
+  tok = processor(text = desc, return_tensors = 'pt', images = None, padding = True).to(device)
+  tok_emb = model.get_text_features(**tok)
+  tok_emb = tok_emb.detach().cpu().numpy() / np.linalg.norm(tok_emb.detach().cpu().numpy(), axis=0)
+  return tok_emb
+
+st.set_page_config(page_title="Mood Scope", page_icon="🎭")
+st.title('Mood-Scope')
+st.sidebar.title('Options')
+
+app_mode = st.sidebar.selectbox('Choose Page', ['About the App', 'Run Mood Scope'])
+
+st.markdown(
+      """
+      <style>
+        [data-testid = 'stSidebar'][aria-expanded = 'true'] > div:first-child{
+          width: 350px
+        }
+        [data-testid = 'stSidebar'][aria-expanded = 'false'] > div:first-child{
+          width: 350px
+          margin-left: -350px
+        }
+      </style>
+      """, unsafe_allow_html = True
+)
+
+if app_mode == 'About the App':
+  st.markdown('Will edit this later!!')
+
+elif app_mode == 'Run Mood Scope':
+  
+  processor, model = load_model()
+
+  st.sidebar.markdown('---')
+  
+  with st.columns(3)[1]:
+    kpi = st.markdown('**Dominant Detected Emotion**')
+    emotion_emoji = st.markdown('-')
+    #emotion_text = st.markdown('-')
+
+  img_file_buffer = st.sidebar.file_uploader('Upload an Image', type = ['jpg', 'png', 'jpeg'])
+  if img_file_buffer:
+    buffer = BytesIO(img_file_buffer.read())
+    data = np.frombuffer(buffer.getvalue(), dtype=np.uint8)
+    image = cv2.imdecode(data, cv2.IMREAD_COLOR)
+  else:
+    demo_image = DEMO_IMAGE
+    image = cv2.imread(demo_image, cv2.IMREAD_COLOR)
+  
+  st.sidebar.text('Original Image')
+  st.sidebar.image(image, channels = 'BGR')
+
+  im_proc = processor(images=image, return_tensors='pt')['pixel_values']
+  im_emb = model.to(device).get_image_features(im_proc.to(device))
+  im_emb = im_emb.detach().cpu().numpy()
+
+  tok_emb = load_token_embds()
+  score = np.dot(im_emb, tok_emb.T)
+  
+  output_emoji = EMOTION_DICT[score.argmax(axis = 1).item()][1]
+  output_text = EMOTION_DICT[score.argmax(axis = 1).item()][0]
+
+  emotion_emoji.write(f'<h1> {output_emoji} </h1>', unsafe_allow_html = True)
+  
+  categories = [emotion[0] for emotion in EMOTION_DICT.values()]
+  data = list(map(int, (100 * (score / score.sum())).squeeze()))
+
+  trace = go.Scatterpolar(r = data, theta = categories, fill = 'toself', name = 'Emotions')
+
+  layout = go.Layout(
+      polar = dict(
+          radialaxis = dict(
+              visible = False,
+              range = [0, 50]
+          )
+      ),
+  )
+  fig = go.Figure(data=[trace], layout=layout)
+  st.plotly_chart(fig, use_container_width=True)
+
+  #emotion_text.write(f'**{output_text}**')

requirements.txt

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+datasets
+transformers
+torchvision
+torch
+streamlit
+plotly