Update app.py

app.py

@@ -4,55 +4,45 @@ import spaces
 import torch
 import random
 from PIL import Image
-
 from diffusers import FluxKontextPipeline
 from diffusers.utils import load_image
+import requests
 
 MAX_SEED = np.iinfo(np.int32).max
 
 pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to("cuda")
 
 @spaces.GPU
-def infer(input_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5, steps=28, progress=gr.Progress(track_tqdm=True)):
+def translate_albanian_to_english(text):
+    """Translate Albanian to English using sepioo-facebook-translation API."""
+    if not text.strip():
+        return ""
+    for attempt in range(2):
+        try:
+            response = requests.post(
+                "https://hal1993-mdftranslation1234567890abcdef1234567890-fc073a6.hf.space/v1/translate",
+                json={"from_language": "sq", "to_language": "en", "input_text": text},
+                headers={"accept": "application/json", "Content-Type": "application/json"},
+                timeout=5
+            )
+            response.raise_for_status()
+            translated = response.json().get("translate", "")
+            return translated
+        except Exception as e:
+            if attempt == 1:
+                raise gr.Error(f"Përkthimi dështoi: {str(e)}")
+    raise gr.Error("Përkthimi dështoi. Ju lutem provoni përsëri.")
+
+@spaces.GPU
+def infer(input_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5, steps=12, progress=gr.Progress(track_tqdm=True)):
     """
     Perform image editing using the FLUX.1 Kontext pipeline.
-    
-    This function takes an input image and a text prompt to generate a modified version
-    of the image based on the provided instructions. It uses the FLUX.1 Kontext model
-    for contextual image editing tasks.
-    
-    Args:
-        input_image (PIL.Image.Image): The input image to be edited. Will be converted
-            to RGB format if not already in that format.
-        prompt (str): Text description of the desired edit to apply to the image.
-            Examples: "Remove glasses", "Add a hat", "Change background to beach".
-        seed (int, optional): Random seed for reproducible generation. Defaults to 42.
-            Must be between 0 and MAX_SEED (2^31 - 1).
-        randomize_seed (bool, optional): If True, generates a random seed instead of
-            using the provided seed value. Defaults to False.
-        guidance_scale (float, optional): Controls how closely the model follows the
-            prompt. Higher values mean stronger adherence to the prompt but may reduce
-            image quality. Range: 1.0-10.0. Defaults to 2.5.
-        steps (int, optional): Controls how many steps to run the diffusion model for.
-            Range: 1-30. Defaults to 28.
-        progress (gr.Progress, optional): Gradio progress tracker for monitoring
-            generation progress. Defaults to gr.Progress(track_tqdm=True).
-    
-    Returns:
-        tuple: A 3-tuple containing:
-            - PIL.Image.Image: The generated/edited image
-            - int: The seed value used for generation (useful when randomize_seed=True)
-            - gr.update: Gradio update object to make the reuse button visible
-    
-    Example:
-        >>> edited_image, used_seed, button_update = infer(
-        ...     input_image=my_image,
-        ...     prompt="Add sunglasses",
-        ...     seed=123,
-        ...     randomize_seed=False,
-        ...     guidance_scale=2.5
-        ... )
     """
+    # Translate Albanian prompt to English
+    final_prompt = translate_albanian_to_english(prompt.strip()) if prompt.strip() else ""
+    if not final_prompt:
+        return None, seed, gr.Button(visible=False)
+
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     
@@ -60,17 +50,19 @@ def infer(input_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5
         input_image = input_image.convert("RGB")
         image = pipe(
             image=input_image, 
-            prompt=prompt,
+            prompt=final_prompt,
             guidance_scale=guidance_scale,
-            width = input_image.size[0],
-            height = input_image.size[1],
+            width=input_image.size[0],
+            height=input_image.size[1],
             num_inference_steps=steps,
             generator=torch.Generator().manual_seed(seed),
         ).images[0]
     else:
         image = pipe(
-            prompt=prompt,
+            prompt=final_prompt,
             guidance_scale=guidance_scale,
+            width=1024,
+            height=1024,
             num_inference_steps=steps,
             generator=torch.Generator().manual_seed(seed),
         ).images[0]
@@ -81,86 +73,94 @@ def infer_example(input_image, prompt):
     image, seed, _ = infer(input_image, prompt)
     return image, seed
 
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 960px;
-}
-"""
+with gr.Blocks() as demo:
+    gr.HTML("""
+    <style>
+    body::before {
+        content: "";
+        display: block;
+        height: 320px;
+        background-color: var(--body-background-fill);
+    }
+    button[aria-label="Fullscreen"], button[aria-label="Fullscreen"]:hover {
+        display: none !important;
+        visibility: hidden !important;
+        opacity: 0 !important;
+        pointer-events: none !important;
+    }
+    button[aria-label="Share"], button[aria-label="Share"]:hover {
+        display: none !important;
+    }
+    button[aria-label="Download"] {
+        transform: scale(3);
+        transform-origin: top right;
+        margin: 0 !important;
+        padding: 6px !important;
+    }
+    </style>
+    """)
 
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""# FLUX.1 Kontext [dev]
-Image editing and manipulation model guidance-distilled from FLUX.1 Kontext [pro], [[blog]](https://bfl.ai/announcements/flux-1-kontext-dev) [[model]](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev)
-        """)
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(label="Upload the image for editing", type="pil")
-                with gr.Row():
-                    prompt = gr.Text(
-                        label="Prompt",
-                        show_label=False,
-                        max_lines=1,
-                        placeholder="Enter your prompt for editing (e.g., 'Remove glasses', 'Add a hat')",
-                        container=False,
-                    )
-                    run_button = gr.Button("Run", scale=0)
-                with gr.Accordion("Advanced Settings", open=False):
-                    
-                    seed = gr.Slider(
-                        label="Seed",
-                        minimum=0,
-                        maximum=MAX_SEED,
-                        step=1,
-                        value=0,
-                    )
-                    
-                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    
-                    guidance_scale = gr.Slider(
-                        label="Guidance Scale",
-                        minimum=1,
-                        maximum=10,
-                        step=0.1,
-                        value=2.5,
-                    )       
-                    
-                    steps = gr.Slider(
-                        label="Steps",
-                        minimum=1,
-                        maximum=30,
-                        value=28,
-                        step=1
-                    )
-                    
-            with gr.Column():
-                result = gr.Image(label="Result", show_label=False, interactive=False)
-                reuse_button = gr.Button("Reuse this image", visible=False)
-        
-            
-        examples = gr.Examples(
-            examples=[
-                ["flowers.png", "turn the flowers into sunflowers"],
-                ["monster.png", "make this monster ride a skateboard on the beach"],
-                ["cat.png", "make this cat happy"]
-            ],
-            inputs=[input_image, prompt],
-            outputs=[result, seed],
-            fn=infer_example,
-            cache_examples="lazy"
+    gr.Markdown("# Krijo Imazhe")
+    gr.Markdown("Gjenero imazhe të reja nga përshkrimin yt me fuqinë e inteligjencës artificiale.")
+
+    with gr.Column():
+        input_image = gr.Image(label="Ngarko Imazhin për Editim", type="pil")
+        prompt = gr.Textbox(
+            label="Përshkrimi",
+            placeholder="Shkruani përshkrimin këtu",
+            lines=3
+        )
+        run_button = gr.Button(value="Gjenero")
+        reuse_button = gr.Button("Rivendos këtë imazh", visible=False)
+        # Hidden advanced settings
+        seed = gr.Slider(
+            minimum=0,
+            maximum=MAX_SEED,
+            step=1,
+            value=0,
+            visible=False
+        )
+        randomize_seed = gr.Checkbox(value=True, visible=False)
+        guidance_scale = gr.Slider(
+            minimum=1,
+            maximum=10,
+            step=0.1,
+            value=2.5,
+            visible=False
+        )
+        steps = gr.Slider(
+            minimum=1,
+            maximum=30,
+            value=12,
+            step=1,
+            visible=False
         )
-            
+
+    with gr.Row():
+        result = gr.Image(label="Imazhi i Gjeneruar", interactive=False)
+
+    gr.Examples(
+        examples=[
+            ["flowers.png", "ktheji lulet në luledielli"],
+            ["monster.png", "bëje këtë përbindësh të hipë në një skateboard në plazh"],
+            ["cat.png", "bëje këtë mace të lumtur"]
+        ],
+        inputs=[input_image, prompt],
+        outputs=[result, seed],
+        fn=infer_example,
+        cache_examples="lazy"
+    )
+
     gr.on(
         triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [input_image, prompt, seed, randomize_seed, guidance_scale, steps],
-        outputs = [result, seed, reuse_button]
+        fn=infer,
+        inputs=[input_image, prompt, seed, randomize_seed, guidance_scale, steps],
+        outputs=[result, seed, reuse_button]
     )
     reuse_button.click(
-        fn = lambda image: image,
-        inputs = [result],
-        outputs = [input_image]
+        fn=lambda image: image,
+        inputs=[result],
+        outputs=[input_image]
     )
 
 demo.launch(mcp_server=True)