Update app.py

app.py

@@ -8,7 +8,6 @@ from PIL import Image
 from deep_translator import GoogleTranslator
 import json
 
-
 API_TOKEN = os.getenv("HF_READ_TOKEN")
 headers = {"Authorization": f"Bearer {API_TOKEN}"}
 timeout = 100
@@ -27,28 +26,21 @@ article_text = """
 """
 
 def query(lora_id, prompt, steps=28, cfg_scale=3.5, randomize_seed=True, seed=-1, width=1024, height=1024):
-    if prompt == "" or prompt == None:
+    if not prompt:
         return None
 
-    if lora_id.strip() == "" or lora_id == None:
-        lora_id = "black-forest-labs/FLUX.1-dev" 
-
+    lora_id = lora_id.strip() or "black-forest-labs/FLUX.1-dev"
     key = random.randint(0, 999)
 
-    API_URL = "https://api-inference.huggingface.co/models/"+ lora_id.strip()
-    
-    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
-    headers = {"Authorization": f"Bearer {API_TOKEN}"}
+    API_URL = f"https://api-inference.huggingface.co/models/{lora_id}"
     
-     #prompt = GoogleTranslator(source='my', target='en').translate(prompt)
-    # print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')
+    # Uncomment the line below to enable Myanmar-to-English prompt translation
+    prompt = GoogleTranslator(source='my', target='en').translate(prompt)
 
     prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
-   #  print(f'\033[1mGeneration {key}:\033[0m {prompt}')
 
     # If seed is -1, generate a random seed and use it
-    if randomize_seed:
-        seed = random.randint(1, 4294967296)
+    seed = random.randint(1, 4294967296) if randomize_seed else seed
     
     payload = {
         "inputs": prompt,
@@ -56,15 +48,14 @@ def query(lora_id, prompt, steps=28, cfg_scale=3.5, randomize_seed=True, seed=-1
         "cfg_scale": cfg_scale,
         "seed": seed,
         "parameters": {
-            "width": width,  # Pass the width to the API
-            "height": height  # Pass the height to the API
+            "width": width,
+            "height": height
         }
     }
 
     response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
     if response.status_code != 200:
-        print(f"Error: Failed to get image. Response status: {response.status_code}")
-        print(f"Response content: {response.text}")
+        print(f"Error: Failed to get image. Status: {response.status_code} - {response.text}")
         if response.status_code == 503:
             raise gr.Error(f"{response.status_code} : The model is being loaded")
         raise gr.Error(f"{response.status_code}")
@@ -98,37 +89,23 @@ with gr.Blocks(theme='Nymbo/Nymbo_Theme', css=css) as app:
     with gr.Column(elem_id="app-container"):
         with gr.Row():
             with gr.Column(elem_id="prompt-container"):
-                with gr.Row():
-                    text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here ( English လိုသာရေးလို့ရပါမယ် ) ", lines=2, elem_id="prompt-text-input")
-                with gr.Row():
-                    custom_lora = gr.Textbox(label="Custom Model", info="Model path (Walone LoRA Library မှာ Model path များရနိုင်ပါတယ်)", placeholder="shweaung/mawc-cc")
-                with gr.Row():
-                    with gr.Accordion("Advanced Settings", open=False):
-                        with gr.Row():
-                            width = gr.Slider(label="Width", value=1024, minimum=64, maximum=1216, step=8)
-                            height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, step=8)
-                        seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=4294967296, step=1)
-                        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                        with gr.Row():
-                            steps = gr.Slider(label="Sampling steps", value=28, minimum=1, maximum=100, step=1)
-                            cfg = gr.Slider(label="CFG Scale", value=3.5, minimum=1, maximum=20, step=0.5)
-                        # method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
-
-        with gr.Row():
-            text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
-        with gr.Row():
-            image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
-        with gr.Row():
-            seed_output = gr.Textbox(label="Seed Used", show_copy_button = True, elem_id="seed-output")
-
+                text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here (English လိုသာရေးလို့ရပါမယ်)", lines=2, elem_id="prompt-text-input")
+                custom_lora = gr.Textbox(label="Custom Model", info="Model path (Walone LoRA Library မှာ Model path များရနိုင်ပါတယ်)", placeholder="shweaung/mawc-cc")
+                with gr.Accordion("Advanced Settings", open=False):
+                    width = gr.Slider(label="Width", value=1024, minimum=64, maximum=1216, step=8)
+                    height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, step=8)
+                    seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=4294967296, step=1)
+                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+                    steps = gr.Slider(label="Sampling steps", value=28, minimum=1, maximum=100, step=1)
+                    cfg = gr.Slider(label="CFG Scale", value=3.5, minimum=1, maximum=20, step=0.5)
+
+        text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
+        image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
+        seed_output = gr.Textbox(label="Seed Used", show_copy_button=True, elem_id="seed-output")
         gr.Markdown(article_text)
         
-        gr.Examples(
-            examples = examples,
-            inputs = [text_prompt],
-        )
+        gr.Examples(examples=examples, inputs=[text_prompt])
 
-        
-        text_button.click(query, inputs=[custom_lora, text_prompt, steps, cfg, randomize_seed, seed, width, height], outputs=[image_output,seed_output, seed])
+        text_button.click(query, inputs=[custom_lora, text_prompt, steps, cfg, randomize_seed, seed, width, height], outputs=[image_output, seed_output, seed])
 
 app.launch(show_api=False, share=True)