New app.py

testRunningModel.py

@@ -0,0 +1,97 @@
+import PIL.Image
+import gradio as gr
+import numpy as np
+import random
+import PIL
+import subprocess
+import sys
+import os
+
+# os.system("ls -a")
+# print("1")
+# os.system("pip install ./dlib-19.24.99-cp36-cp36m-linux_x86_64.whl")
+# print("2")
+# os.system("pip install dlib-19.24.99-cp36-cp36m-linux_x86_64.whl")
+# os.system("pip install face-recognition")
+# import face_detection
+
+# import spaces #[uncomment to use ZeroGPU]
+from diffusers import DiffusionPipeline
+import torch
+
+import face_detection
+
+# # Function to display the uploaded image
+# def process_image(image : PIL.Image.Image):
+#     outputs = face_detection.getCroppedImages(image)
+#     # do AI stuff here
+#     return gr.Image(outputs[0])
+
+model_repo_id = "CSSE416-final-project/faceRecogModel"
+weight_file_id = "matTemp.bin"
+
+
+# 1. Load the model from Hugging Face Hub
+def load_model(repo_id):
+    # Download the model weights from the repo
+    weights_path = hf_hub_download(repo_id=model_repo_id, filename=weight_file_id)
+
+    # Initialize the ResNet-18 architecture
+    model = models.resnet18(pretrained=True) # TODO: does it matter if this is set to true or false?
+    num_ftrs = model.fc.in_features
+    model.fc = nn.Linear(num_ftrs, 100)  # Adjust for your task (e.g., 128 classes)
+    # TODO: check if this number^^ corresponds to the number of classes
+
+    # Load the model weights
+    state_dict = torch.load(weights_path, map_location=torch.device("cpu"))
+    model.load_state_dict(state_dict)
+    model.eval()  # Set the model to evaluation mode
+    return model
+
+
+# 2. Load model
+model = load_model(model_repo_id)
+
+
+# 3. Define how to transform image
+transforms = transforms.Compose(
+[
+    transforms.ToTensor()
+])
+
+
+# 4. Preprocess and display the image
+def process_image_str(groupImageFilePath: str):
+    groupImage = PIL.Image.open(groupImageFilePath)
+    locations, images = face_detection.getCroppedImages(groupImage)
+
+    outputLabels = []
+    for image in images:
+        # Process the image
+        intputTensor = transforms(image).unsqueeze(0) # unsqueeze? add batch dimension??
+
+        # do AI stuff here
+        with torch.no_grad():
+            outputs_t = model(intputTensor)
+            _, pred_t = torch.max(outputs_t, dim=1)
+            outputLabels.append(pred_t.item())
+
+
+    #return gr.Image(image)
+    return outputLabels.pop(0)
+
+
+# 5. Create the Gradio interface
+interface = gr.Interface(
+    fn=process_image_str,  # Function to process the image
+    inputs=gr.Image(type='filepath'),  # Upload input
+    # outputs=gr.Image(),  # Display output
+    outputs='text',
+    allow_flagging='never',
+    title="Celebrity Face Detector",
+    description="Upload a picture of a celebrity or group of celebrities to identify them"
+)
+
+# 6. Launch the app
+if __name__ == "__main__":
+    interface.launch()