fix: handle both numpy arrays and file paths in inference function

app.py

@@ -3,12 +3,18 @@ import cv2
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
-import torchvision
 import kornia as K
 
 def inference(file1,num_iters):
-    img: np.ndarray = cv2.imread(file1.name)
-    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0
+    # Check if file1 is already a numpy array
+    if isinstance(file1, np.ndarray):
+        img = file1
+    else:
+        # If it's not a numpy array, assume it's a file path
+        img = cv2.imread(file1)
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    
+    img = img.astype(np.float32) / 255.0
     img = img + np.random.normal(loc=0.0, scale=0.1, size=img.shape)
     img = np.clip(img, 0.0, 1.0)
 
@@ -55,13 +61,13 @@ examples = [ ["doraemon.png",2000]
 
 
 inputs = [
-    gr.Image(type='file', label='Input Image'),
-    gr.Slider(minimum=50, maximum=10000, step=50, default=500, label="num_iters")
+    gr.Image(type='numpy', label='Input Image'),
+    gr.Slider(minimum=50, maximum=10000, step=50, value=500, label="num_iters")
 ]
 
 outputs = [
-    gr.Image(type='file', label='Noised Image'),
-    gr.Image(type='file', label='Denoised Image'),
+    gr.Image(type='numpy', label='Noised Image'),
+    gr.Image(type='numpy', label='Denoised Image'),
 ]
 
 title = "Denoise image using total variation"