updated dataset and filenames

.github/workflows/hugging-face.yaml

@@ -12,7 +12,33 @@ jobs:
         with:
           fetch-depth: 0
           lfs: true
+      - name: Set Git identity
+        run: |
+          git config --global user.email "[email protected]"
+          git config --global user.name "GitHub Actions"
+
+      - name: Update README.md
+        run: |
+          tmp_file=$(mktemp)
+          echo "---" >> $tmp_file
+          echo "title: Headshot" >> $tmp_file
+          echo "emoji: 🏹" >> $tmp_file
+          echo "colorFrom: purple" >> $tmp_file
+          echo "colorTo: blue" >> $tmp_file
+          echo "sdk: streamlit" >> $tmp_file
+          echo "sdk_version: 1.29.0" >> $tmp_file
+          echo "app_file: space.py" >> $tmp_file
+          echo "pinned: false" >> $tmp_file
+          echo "license: openrail" >> $tmp_file
+          echo "---" >> $tmp_file
+          echo "" >> $tmp_file
+          cat README.md >> $tmp_file
+          mv $tmp_file README.md
+          git add README.md
+          git commit -m "Updated README.md"
+
       - name: Push to hub
         env:
           HF_TOKEN: ${{ secrets.HF_TOKEN }}
-        run: git push https://zaibutcooler:[email protected]/spaces/zaibutcooler/headshot --force main
+        run: |
+          git push https://zaibutcooler:[email protected]/spaces/zaibutcooler/headshot --force main

README.md

@@ -1,15 +1,2 @@
----
-
-title: Headshot
-emoji: ✨
-colorFrom: purple
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.29.0
-app_file: space.py
-pinned: false
-license: openrail
----
-
 
 # Headshot Project

headshot/__init__.py

@@ -1,2 +1 @@
-from headshot.headshot import Headshot
-from .config import Config
+from headshot.model import Model

headshot/__main__.py

@@ -0,0 +1,2 @@
+if __name__ == "__main__":
+    print("Main Function")

headshot/config.py

@@ -1,6 +1,9 @@
-class Config:
-    def __init__(self) -> None:
-        pass
-
+import torch
 
-config = Config()
+class Config:
+    def __init__(self,num_epoch=20,lr=0.02) -> None:
+        self.num_epoch = num_epoch
+        self.lr = lr
+        self.device ='cuda' if torch.cuda.is_available() else 'cpu'
+    
+base_config = Config()

headshot/{data_prep.py → dataset.py}

@@ -1,29 +1,19 @@
 import torch
-from torch.utils.data import DataLoader, Dataset
+from torch.utils.data import DataLoader
+from torch.utils.data import Dataset as HfDataset
 from torchvision import transforms, datasets
 from datasets import load_dataset
 
-class FaceDataset(Dataset):
+class Dataset(HfDataset):
     def __init__(self):
-        self.tranforms = transforms.Compose([
-
-        ])
+        self.loaded_data = load_dataset('zaibutcooler/chimp-detection')
         
-        self.data = None
-
+        self.images = self.loaded_data['image']
+        self.labels = self.loaded_data['faces']
 
     def __len__(self):
         return len(self.data)
 
-    def download_data(self):
-        loaded_data = load_dataset('zaibutcooler/chimpanzees')
-        self.data = loaded_data
-
     def __getitem__(self, idx):
-        # Load and preprocess the image at the given index
-        image = self.data[idx]
-        label = self.labels[idx]
-        
-
-        image = self.transform(image)
+        image,label = self.images[idx],self.labels[idx]
         return image,label

headshot/headshot.py

@@ -1,48 +0,0 @@
-import torch
-from torch import nn
-from .data_prep import FaceDataset
-
-class Headshot(nn.Module):
-    def __init__(self,num_epoch=20,lr=0.02):
-        super().__init__()
-        # self.dataset = FaceDataset()
-        self.num_epoch = num_epoch
-        self.lr = lr
-        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-        self.layer = nn.Sequential(
-
-        )
-
-        
-    def forward(self,x):
-        out = self.layer 
-        return out
-
-    def train(self):
-        loss_fn = nn.CrossEntropyLoss()
-        optimizer = torch.optim.Adam(self.parameters(),lr=self.lr)
-        for epoch in range(self.num_epoch):
-            for i,(image,label) in enumerate(self.dataset):
-                image,label = image.to(self.device),label.to(self.device)
-
-                optimizer.zero_grad()
-                
-                output = self(image)
-                loss = loss_fn(output,label)
-                loss.backward()
-                optimizer.step()
-            print(f"epoch {epoch} loss:{loss.item()}")
-                
-
-    def predict_image(self,image):
-        points = self.forward(image)
-        
-    def predict_video(self,video):
-        pass
-
-    def load_pretrain(self,name=""):
-        pretrained = torch.load(pretrained)
-        self.load_state_dict(pretrained)
-
-    

headshot/model.py

@@ -0,0 +1,40 @@
+import torch
+from torch import nn
+from .dataset import Dataset
+from .config import Config
+from huggingface_hub import PyTorchModelHubMixin
+import huggingface_hub
+
+class Model(nn.Module,PyTorchModelHubMixin):
+    def __init__(self,config:Config):
+        super().__init__()
+        self.config = config
+        self.num_epoch = config.num_epoch
+        self.lr = config.lr
+        self.device = config.device
+
+        # TODO start training
+        self.layer = nn.Sequential(
+
+        )
+
+        
+    def forward(self,x):
+        out = self.layer 
+        return out
+
+    def save_pretrained(self, name="headshot",username="zaibutcooler"):
+        # self.model.save_pretrained(name)
+        self.model.push_to_hub(f"{username}/{name}")
+        print("Successfully saved the pretrainied")
+
+    def load_pretrained(self, url="zaibutcooler/headshot"):
+        self.model = self.gpt.from_pretrained(url)
+        print("Successfully loaded the pretrained")
+
+    def huggingface_login(self, token):
+        assert token is not None
+        huggingface_hub.login(token=token)
+        print("Logged in successfully")
+
+    

headshot/robot.py

@@ -1,16 +0,0 @@
-# to connect with some physical machine
-from pyrobot import Robot
-
-# Initialize the robot
-robot = Robot('locobot')
-
-def move_forward(distance):
-    # Move the robot forward
-    robot.base.go_to_relative([distance, 0, 0])
-
-if __name__ == "__main__":
-    # Define the displacement (in meters) for the robot to move forward
-    forward_distance = 0.5  # Move 0.5 meters forward
-    
-    # Move the robot forward
-    move_forward(forward_distance)

robot/robot.py

@@ -0,0 +1 @@
+from headshot import Model