Fix'er Upper

- Updated Gradio preflight installation to allow patch updates within 3.50.x
- Switched model setup to use CPU instead of GPU for compatibility with Hugging Face free tier
- Removed references to half-precision (torch.float16) in favor of full precision (torch.float32) to accommodate CPU environments

app.py

@@ -1,16 +1,12 @@
-##!/usr/bin/python3
+##!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 import os
 
 print("Installing correct gradio version...")
-os.system("pip uninstall -y gradio")
-os.system("pip install gradio==3.50.0")
+os.system("pip install 'gradio>=3.50.0,<3.51.0' --force-reinstall -q")
 print("Installing Finished!")
 
-##!/usr/bin/python3
-# -*- coding: utf-8 -*-
 import gradio as gr
-import os
 import cv2
 from PIL import Image
 import numpy as np
@@ -19,7 +15,7 @@ import torch
 from diffusers import StableDiffusionBrushNetPipeline, BrushNetModel, UniPCMultistepScheduler
 import random
 
-mobile_sam = sam_model_registry['vit_h'](checkpoint='data/ckpt/sam_vit_h_4b8939.pth').to("cuda")
+mobile_sam = sam_model_registry['vit_h'](checkpoint='data/ckpt/sam_vit_h_4b8939.pth').to("cpu")
 mobile_sam.eval()
 mobile_predictor = SamPredictor(mobile_sam)
 colors = [(255, 0, 0), (0, 255, 0)]
@@ -42,9 +38,9 @@ base_model_path = "data/ckpt/realisticVisionV60B1_v51VAE"
 # input brushnet ckpt path
 brushnet_path = "data/ckpt/segmentation_mask_brushnet_ckpt"
 
-brushnet = BrushNetModel.from_pretrained(brushnet_path, torch_dtype=torch.float16)
+brushnet = BrushNetModel.from_pretrained(brushnet_path, torch_dtype=torch.float32)
 pipe = StableDiffusionBrushNetPipeline.from_pretrained(
-    base_model_path, brushnet=brushnet, torch_dtype=torch.float16, low_cpu_mem_usage=False
+    base_model_path, brushnet=brushnet, torch_dtype=torch.float32, low_cpu_mem_usage=False
 )
 
 # speed up diffusion process with faster scheduler and memory optimization
@@ -107,7 +103,7 @@ def process(input_image,
     init_image = Image.fromarray(masked_image.astype(np.uint8)).convert("RGB")
     mask_image = Image.fromarray(original_mask.astype(np.uint8)).convert("RGB")
 
-    generator = torch.Generator("cuda").manual_seed(random.randint(0,2147483647) if randomize_seed else seed)
+    generator = torch.Generator("cpu").manual_seed(random.randint(0,2147483647) if randomize_seed else seed)
 
     image = pipe(
         [prompt]*2,