Update app.py

app.py

@@ -3,9 +3,21 @@ from diffusers import StableDiffusionPipeline
 import torch
 import huggingface_hub as hf
 import os
+
+my_file = open("./style_name.txt", "r")
+  
+# reading the file
+data = my_file.read()
+  
+# replacing end splitting the text 
+# when newline ('\n') is seen.
+data_into_list = data.split("\n")[:-1]
+
+my_file.close()
+
 hf.login(token=os.environ['model_token'])
 #remember to login with token before loading model
-def text_to_hair(prompt, guidance_scale=8, num_inference_steps=30, model_path ="CVH-vn1210/hair-model"):
+def text_to_hair(prompt, guidance_scale=8, num_inference_steps=30, styles, model_path ="CVH-vn1210/hair-model"):
     
     pipe = StableDiffusionPipeline.from_pretrained(os.environ['bmd'], torch_dtype=torch.float16, use_auth_token=True)
     pipe.unet.load_attn_procs(model_path)
@@ -14,5 +26,8 @@ def text_to_hair(prompt, guidance_scale=8, num_inference_steps=30, model_path ="
     #image.save(save_name) #comment if don't want to save image
     return image #PIL format
 
-demo = gr.Interface(fn=text_to_hair, inputs=["text", gr.Slider(5, 20, value=8, label="Guidance_scale", info="Choose between 5 and 20 to improve image's content"), gr.Slider(20, 500, value=20, label="Num_infer_steps", info="Choose between 20 and 500 to improve image's resolution")], outputs="image")
+demo = gr.Interface(fn=text_to_hair, inputs=["text", gr.Slider(5, 20, value=8, label="Guidance_scale", info="Choose between 5 and 20 to improve image's content"), 
+                                             gr.Slider(20, 500, value=20, label="Num_infer_steps", info="Choose between 20 and 500 to improve image's resolution"),
+                                            gr.Dropdown(data_into_list, label="Some suggestion hairstyles", info="For your suggestion and reference!")],
+                    outputs="image")
 demo.launch()