'replaced np with pandas'

app.py

@@ -9,7 +9,7 @@ from diffusers.models import AutoencoderKL
 from diffusers.models.attention_processor import AttnProcessor2_0
 from PIL import Image
 import cv2
-import numpy as np
+import pandas as pd
 from RealESRGAN import RealESRGAN
 import gradio as gr
 from gradio_imageslider import ImageSlider
@@ -146,21 +146,21 @@ def resize_and_upscale(input_image, resolution):
 def create_hdr_effect(original_image, hdr):
     if hdr == 0:
         return original_image
-    cv_original = cv2.cvtColor(np.array(original_image), cv2.COLOR_RGB2BGR)
+    cv_original = cv2.cvtColor(pd.DataFrame(original_image).to_numpy(), cv2.COLOR_RGB2BGR)
     factors = [1.0 - 0.9 * hdr, 1.0 - 0.7 * hdr, 1.0 - 0.45 * hdr,
                1.0 - 0.25 * hdr, 1.0, 1.0 + 0.2 * hdr,
                1.0 + 0.4 * hdr, 1.0 + 0.6 * hdr, 1.0 + 0.8 * hdr]
     images = [cv2.convertScaleAbs(cv_original, alpha=factor) for factor in factors]
     merge_mertens = cv2.createMergeMertens()
     hdr_image = merge_mertens.process(images)
-    hdr_image_8bit = np.clip(hdr_image * 255, 0, 255).astype('uint8')
+    hdr_image_8bit = pd.DataFrame(np.clip(hdr_image * 255, 0, 255).astype('uint8')).to_numpy()
     return Image.fromarray(cv2.cvtColor(hdr_image_8bit, cv2.COLOR_BGR2RGB))
 
 def apply_denoising(image, strength):
     return cv2.fastNlMeansDenoisingColored(image, None, strength * 10, strength * 10, 7, 21)
 
 def apply_sharpening(image, intensity):
-    kernel = np.array([[0, -1, 0], [-1, 5 + intensity * 4, -1], [0, -1, 0]])
+    kernel = pd.DataFrame([[0, -1, 0], [-1, 5 + intensity * 4, -1], [0, -1, 0]]).to_numpy()
     return cv2.filter2D(image, -1, kernel)
 
 def prepare_image(input_image, resolution, hdr):
@@ -187,7 +187,7 @@ def gradio_process_images(input_images, model_choice, custom_prompt, custom_nega
             "control_image": condition_image,
         }
         result = lazy_pipe(**options).images[0]
-        result = np.array(result)
+        result = pd.DataFrame(result).to_numpy()
 
         if denoising_strength > 0:
             result = apply_denoising(result, denoising_strength)
@@ -212,7 +212,7 @@ def update_live_preview(input_image, model_choice, custom_prompt, custom_negativ
         "control_image": condition_image,
     }
     result = lazy_pipe(**options).images[0]
-    return np.array(result)
+    return pd.DataFrame(result).to_numpy()
 
 # Gradio Interface
 input_images = gr.File(label="Input Images", type="file", multiple=True)
@@ -251,4 +251,4 @@ demo = gr.Interface(
     outputs=output_slider
 )
 
-demo.launch()
+demo.launch()