Update app.py

app.py

@@ -22,14 +22,14 @@ os.chdir("Stable_Diffusion_Finetuned_Minecraft_Skin_Generator")
 
 
 @spaces.GPU()
-def run_inference(prompt, stable_diffusion_model, num_inference_steps, guidance_scale, model_precision_type, seed, output_image_name, verbose):
+def run_inference(prompt, stable_diffusion_model, num_inference_steps, guidance_scale, model_precision_type, seed, filename, verbose):
     
     if stable_diffusion_model == '2':
         sd_model = "minecraft-skins"
     else:
         sd_model = "minecraft-skins-sdxl"
 
-    inference_command = f"python Scripts/{sd_model}.py '{prompt}' {num_inference_steps} {guidance_scale} {model_precision_type} {seed} {output_image_name} {'--verbose' if verbose else ''}"
+    inference_command = f"python Scripts/{sd_model}.py '{prompt}' {num_inference_steps} {guidance_scale} {model_precision_type} {seed} {filename} {'--verbose' if verbose else ''}"
     
     os.system(inference_command)
 
@@ -49,7 +49,7 @@ num_inference_steps = gr.Number(label="Number of Inference Steps", precision=0,
 guidance_scale = gr.Number(minimum=0.1, value=7.5, label="Guidance Scale", info="The number of denoising steps of the image. More denoising steps usually lead to a higher quality image at the cost of slower inference")
 model_precision_type = gr.Dropdown(["fp16", "fp32"], value="fp16", label="Model Precision Type", info="The precision type to load the model, like fp16 which is faster, or fp32 which gives better results")
 seed = gr.Number(value=42, label="Seed", info="A starting point to initiate generation, put 0 for a random one")
-output_image_name = gr.Textbox(label="Output Image Name", info="The name of the file of the output image skin, keep the .png", value="output-skin.png")
+filename = gr.Textbox(label="Output Image Name", info="The name of the file of the output image skin, keep the .png", value="output-skin.png")
 verbose = gr.Checkbox(label="Verbose Output", info="Produce more detailed output while running", value=False)
 
 
@@ -63,7 +63,7 @@ gr.Interface(
         guidance_scale,
         model_precision_type,
         seed,
-        output_image_name,
+        filename,
         verbose
     ],
     outputs=[