Update app.py

app.py

@@ -3,10 +3,8 @@ import requests
 import io
 import random
 import os
-import time
 from PIL import Image
 from deep_translator import GoogleTranslator
-import json
 
 API_TOKEN = os.getenv("HF_READ_TOKEN")
 headers = {"Authorization": f"Bearer {API_TOKEN}"}
@@ -25,22 +23,25 @@ article_text = """
 </div>
 """
 
-def query(lora_id, prompt, steps=28, cfg_scale=3.5, randomize_seed=True, seed=-1, width=1024, height=1024):
-    if not prompt:
+def query(lora_id, prompt, steps=28, cfg_scale=3.5, randomize_seed=True, seed=-1, width=1024, height=1024, output_format="PNG"):
+    if prompt == "" or prompt == None:
         return None
 
-    lora_id = lora_id.strip() or "black-forest-labs/FLUX.1-dev"
+    if lora_id.strip() == "" or lora_id == None:
+        lora_id = "black-forest-labs/FLUX.1-dev"
+
     key = random.randint(0, 999)
 
-    API_URL = f"https://api-inference.huggingface.co/models/{lora_id}"
+    API_URL = "https://api-inference.huggingface.co/models/" + lora_id.strip()
     
-    # Uncomment the line below to enable Myanmar-to-English prompt translation
-    prompt = GoogleTranslator(source='my', target='en').translate(prompt)
+    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
+    headers = {"Authorization": f"Bearer {API_TOKEN}"}
 
     prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
 
     # If seed is -1, generate a random seed and use it
-    seed = random.randint(1, 4294967296) if randomize_seed else seed
+    if randomize_seed:
+        seed = random.randint(1, 4294967296)
     
     payload = {
         "inputs": prompt,
@@ -55,7 +56,8 @@ def query(lora_id, prompt, steps=28, cfg_scale=3.5, randomize_seed=True, seed=-1
 
     response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
     if response.status_code != 200:
-        print(f"Error: Failed to get image. Status: {response.status_code} - {response.text}")
+        print(f"Error: Failed to get image. Response status: {response.status_code}")
+        print(f"Response content: {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}")
@@ -64,7 +66,12 @@ def query(lora_id, prompt, steps=28, cfg_scale=3.5, randomize_seed=True, seed=-1
         image_bytes = response.content
         image = Image.open(io.BytesIO(image_bytes))
         print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
-        return image, seed, seed
+
+        # Convert to specified format (JPEG or PNG)
+        img_byte_arr = io.BytesIO()
+        image.save(img_byte_arr, format=output_format)
+        img_byte_arr.seek(0)
+        return Image.open(img_byte_arr), seed, seed
     except Exception as e:
         print(f"Error when trying to open the image: {e}")
         return None
@@ -89,23 +96,36 @@ 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"):
-                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")
+                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)
+                        output_format = gr.Dropdown(label="Output Format", choices=["JPEG", "PNG"], value="PNG")
+
+        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")
+
         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, output_format], outputs=[image_output, seed_output, seed])
 
 app.launch(show_api=False, share=True)