Delete testRunningModel.py

testRunningModel.py

@@ -1,97 +0,0 @@
-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()