Upload 64 files

ModelClass.py

@@ -1,24 +1,6 @@
-
 import torch
-from torch import nn, optim
+from torch import nn
 from torchvision import transforms, models
-#from torch_snippets import *
-#from torch.utils.data import DataLoader, Dataset
-#from torchsummary import summary 
-
-#import seaborn as sns
-#import matplotlib.pyplot as plt
-#from sklearn.model_selection import train_test_split
-from PIL import Image
-#import numpy as np
-#import cv2
-#from glob import glob
-#import pandas as pd
-import numpy as np
-
-#device = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-
 
 class ActionClassifier(nn.Module):
     def __init__(self, ntargets):
@@ -86,17 +68,10 @@ def get_class(index):
 
 
 
-    
-    
-    
-
 
 # img = Image.open('./inputs/Image_102.jpg').convert('RGB')
-
 # #print(transform(img))
-
 # img = transform(img)
-
 # img = img.unsqueeze(dim=0)
 # print(img.shape)
 

app.py

@@ -6,6 +6,7 @@ import ModelClass
 from glob import glob
 import torch
 import torch.nn as nn
+import numpy as np
 
 @st.cache_resource
 def load_model():
@@ -99,8 +100,8 @@ def predict(image):
 def app():
     
     st.title('ActionNet')
-    st.markdown("[![View in W&B](https://img.shields.io/badge/View%20in-W%26B-blue)](https://wandb.ai/<username>/<project_name>?workspace=user-<username>)")
-    st.markdown('This project aims to identify whales and dolphins by their unique characteristics. It can help researchers understand their behavior, population dynamics, and migration patterns. This project can aid researchers in identifying these marine mammals, providing valuable data for conservation efforts. [[Source Code]](https://kaggle.com/)')
+    # st.markdown("[![View in W&B](https://img.shields.io/badge/View%20in-W%26B-blue)](https://wandb.ai/<username>/<project_name>?workspace=user-<username>)")
+    st.markdown('Human Action Recognition using CNN: A Conputer Vision project that trains a ResNet model to classify human activities. The dataset contains 15 activity classes, and the model predicts the activity from input images.')
     
     
     uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
@@ -109,7 +110,7 @@ def app():
     test_image = st.selectbox('Or choose a test image', list(test_images.keys()))
     
     
-    st.subheader('Selected Image')
+    st.markdown('#### Selected Image')
     
     left_column, right_column = st.columns([1.5, 2.5], gap="medium")
     with left_column:
@@ -126,22 +127,23 @@ def app():
         if st.button('✨ Get prediction from AI', type='primary'):
             spacer = st.empty()
             
-            
             res = infer(image)
-            res = torch.argmax(res)
-            cname = ModelClass.get_class(res)
-            st.write(f'{cname}')
+            prob = res.numpy()
+            idx = np.argpartition(prob, -4)[-4:]
+            right_column.markdown('#### Results')
             
-            
-            prediction = predict(image)
-            right_column.subheader('Results')
-            for class_name, class_probability in prediction.items():
+            idx = list(idx)
+            for i in idx:
+                
+                class_name = ModelClass.get_class(i).replace('_', ' ').capitalize()
+                class_probability = prob[i].astype(float)
                 right_column.write(f'{class_name}: {class_probability:.2%}')
                 right_column.progress(class_probability)
+            
     
     
     st.markdown("---")
-    st.markdown("Built by [Shamim Ahamed](https://your-portfolio-website.com/). Data provided by [Kaggle](https://www.kaggle.com/c/)")
+    st.markdown("Built by [Shamim Ahamed](https://www.shamimahamed.com/). Data provided by [aiplanet](https://aiplanet.com/challenges/data-sprint-76-human-activity-recognition/233/overview/about)")
 
     
 app()

requirements.txt

@@ -1,6 +1,6 @@
-
+streamlit==1.21.0
 Pillow
 protobuf
 torchvision==0.15.2
 torch==2.0.1
-
+numpy