Update app.py

app.py

@@ -8,7 +8,8 @@ from io import BytesIO
 import html
 import re
 
-
+# Free Google Translate API endpoint (without API key)
+GOOGLE_TRANSLATE_API_URL = "https://translate.googleapis.com/translate_a/single?client=gtx&sl=auto&tl=en&dt=t&q="
 
 class Prodia:
     def __init__(self, api_key, base=None):
@@ -70,20 +71,21 @@ class Prodia:
 
         return response
 
+# Function to translate text using Google Translate API
+def translate_prompt(prompt):
+    url = GOOGLE_TRANSLATE_API_URL + requests.utils.quote(prompt)
+    response = requests.get(url)
+    translated_text = json.loads(response.text)[0][0][0]
+    return translated_text
 
 def image_to_base64(image):
-    # Convert the image to bytes
     buffered = BytesIO()
-    image.save(buffered, format="PNG")  # You can change format to PNG if needed
-    
-    # Encode the bytes to base64
+    image.save(buffered, format="PNG")
     img_str = base64.b64encode(buffered.getvalue())
-
-    return img_str.decode('utf-8')  # Convert bytes to string
-
+    return img_str.decode('utf-8')
 
 def remove_id_and_ext(text):
-    text = re.sub(r'\[.*\]$', '', text)
+    text = re.sub(r'.*$', '', text)
     extension = text[-12:].strip()
     if extension == "safetensors":
         text = text[:-13]
@@ -91,7 +93,6 @@ def remove_id_and_ext(text):
         text = text[:-4]
     return text
 
-
 def get_data(text):
     results = {}
     patterns = {
@@ -119,11 +120,8 @@ def get_data(text):
         results['h'] = None
     return results
 
-
 def send_to_txt2img(image):
-
     result = {tabs: gr.update(selected="t2i")}
-
     try:
         text = image.info['parameters']
         data = get_data(text)
@@ -140,13 +138,10 @@ def send_to_txt2img(image):
         else:
             result[model] = gr.update()
         return result
-
     except Exception as e:
         print(e)
-
         return result
 
-
 prodia_client = Prodia(api_key=os.getenv("PRODIA_API_KEY"))
 model_list = prodia_client.list_models()
 model_names = {}
@@ -155,10 +150,10 @@ for model_name in model_list:
     name_without_ext = remove_id_and_ext(model_name)
     model_names[name_without_ext] = model_name
 
-
 def txt2img(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed):
+    translated_prompt = translate_prompt(prompt)  # Translate the prompt to English
     result = prodia_client.generate({
-        "prompt": prompt,
+        "prompt": translated_prompt,
         "negative_prompt": negative_prompt,
         "model": model,
         "steps": steps,
@@ -168,17 +163,15 @@ def txt2img(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, he
         "height": height,
         "seed": seed
     })
-
     job = prodia_client.wait(result)
-
     return job["imageUrl"]
 
-
 def img2img(input_image, denoising, prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed):
+    translated_prompt = translate_prompt(prompt)  # Translate the prompt to English
     result = prodia_client.transform({
         "imageData": image_to_base64(input_image),
         "denoising_strength": denoising,
-        "prompt": prompt,
+        "prompt": translated_prompt,
         "negative_prompt": negative_prompt,
         "model": model,
         "steps": steps,
@@ -188,12 +181,9 @@ def img2img(input_image, denoising, prompt, negative_prompt, model, steps, sampl
         "height": height,
         "seed": seed
     })
-
     job = prodia_client.wait(result)
-
     return job["imageUrl"]
 
-
 css = """
 #generate {
     height: 100%;
@@ -229,99 +219,125 @@ with gr.Blocks(css=css) as demo:
     
                         with gr.Row():
                             with gr.Column(scale=1):
-                                width = gr.Slider(label="Width", maximum=1024, value=512, step=8)
-                                height = gr.Slider(label="Height", maximum=1024, value=512, step=8)
-                            
-                            with gr.Column(scale=1):
-                                batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
-                                batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)
-    
-                        cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1)
-                        seed = gr.Number(label="Seed", value=-1)
+     # Function for translating the prompt using the free Google Translate API
+def translate_prompt(prompt, target_language="en"):
+    try:
+        # Set up the translation endpoint and parameters
+        url = "https://translate.googleapis.com/translate_a/single"
+        params = {
+            "client": "gtx",
+            "sl": "auto",  # Source language is automatically detected
+            "tl": target_language,  # Target language
+            "dt": "t",  # Request translation only
+            "q": prompt,  # The text to translate
+        }
 
-                with gr.Column(scale=2):
-                    image_output = gr.Image(value="https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png")
+        # Make the GET request to the translation API
+        response = requests.get(url, params=params)
+
+        # Extract the translated text from the response
+        translated_text = response.json()[0][0][0]
+        return translated_text
+
+    except Exception as e:
+        print(f"Error translating prompt: {e}")
+        return prompt  # In case of error, return the original prompt
+
+
+# Modify txt2img function to include prompt translation
+def txt2img(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed):
+    translated_prompt = translate_prompt(prompt)
+    translated_negative_prompt = translate_prompt(negative_prompt)
     
-            text_button.click(txt2img, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height,
-                                               seed], outputs=image_output, concurrency_limit=64)
-        
-        with gr.Tab("img2img", id='i2i'):
+    result = prodia_client.generate({
+        "prompt": translated_prompt,
+        "negative_prompt": translated_negative_prompt,
+        "model": model,
+        "steps": steps,
+        "sampler": sampler,
+        "cfg_scale": cfg_scale,
+        "width": width,
+        "height": height,
+        "seed": seed
+    })
+
+    job = prodia_client.wait(result)
+
+    return job["imageUrl"]
+
+
+# Modify img2img function to include prompt translation
+def img2img(input_image, denoising, prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed):
+    translated_prompt = translate_prompt(prompt)
+    translated_negative_prompt = translate_prompt(negative_prompt)
+
+    result = prodia_client.transform({
+        "imageData": image_to_base64(input_image),
+        "denoising_strength": denoising,
+        "prompt": translated_prompt,
+        "negative_prompt": translated_negative_prompt,
+        "model": model,
+        "steps": steps,
+        "sampler": sampler,
+        "cfg_scale": cfg_scale,
+        "width": width,
+        "height": height,
+        "seed": seed
+    })
+
+    job = prodia_client.wait(result)
+
+    return job["imageUrl"]
+
+# Add a translation option in the UI
+with gr.Blocks(css=css) as demo:
+    with gr.Row():
+        with gr.Column(scale=6):
+            model = gr.Dropdown(interactive=True, value="absolutereality_v181.safetensors [3d9d4d2b]", show_label=True,
+                                label="Stable Diffusion Checkpoint", choices=prodia_client.list_models())
+        with gr.Column(scale=1):
+            gr.Markdown(elem_id="powered-by-prodia",
+                        value="AUTOMATIC1111 Stable Diffusion Web UI.<br>Powered by [Prodia](https://prodia.com).<br>For more features and faster generation times check out our [API Docs](https://docs.prodia.com/reference/getting-started-guide).")
+
+    with gr.Tabs() as tabs:
+        with gr.Tab("txt2img", id='t2i'):
             with gr.Row():
                 with gr.Column(scale=6, min_width=600):
-                    i2i_prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", placeholder="Prompt", show_label=False, lines=3)
-                    i2i_negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly")
+                    prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", placeholder="Prompt",
+                                        show_label=False, lines=3)
+                    negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3,
+                                                 value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly")
+                    translate_option = gr.Checkbox(label="Translate Prompt to English", value=True)
                 with gr.Column():
-                    i2i_text_button = gr.Button("Generate", variant='primary', elem_id="generate")
-                    
+                    text_button = gr.Button("Generate", variant='primary', elem_id="generate")
+
             with gr.Row():
                 with gr.Column(scale=3):
                     with gr.Tab("Generation"):
-                        i2i_image_input = gr.Image(type="pil")
-
                         with gr.Row():
                             with gr.Column(scale=1):
-                                i2i_sampler = gr.Dropdown(value="Euler a", show_label=True, label="Sampling Method", choices=prodia_client.list_samplers())
-                                
+                                sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method",
+                                                      choices=prodia_client.list_samplers())
+
                             with gr.Column(scale=1):
-                                i2i_steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=25, value=20, step=1)
+                                steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=25, value=20, step=1)
 
                         with gr.Row():
                             with gr.Column(scale=1):
-                                i2i_width = gr.Slider(label="Width", maximum=1024, value=512, step=8)
-                                i2i_height = gr.Slider(label="Height", maximum=1024, value=512, step=8)
-                            
+                                width = gr.Slider(label="Width", maximum=1024, value=512, step=8)
+                                height = gr.Slider(label="Height", maximum=1024, value=512, step=8)
+
                             with gr.Column(scale=1):
-                                i2i_batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
-                                i2i_batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)
-    
-                        i2i_cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1)
-                        i2i_denoising = gr.Slider(label="Denoising Strength", minimum=0, maximum=1, value=0.7, step=0.1)
-                        i2i_seed = gr.Number(label="Seed", value=-1)
+                                batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
+                                batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)
+
+                        cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1)
+                        seed = gr.Number(label="Seed", value=-1)
 
                 with gr.Column(scale=2):
-                    i2i_image_output = gr.Image(value="https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png")
-    
-            i2i_text_button.click(img2img, inputs=[i2i_image_input, i2i_denoising, i2i_prompt, i2i_negative_prompt,
-                                                   model, i2i_steps, i2i_sampler, i2i_cfg_scale, i2i_width, i2i_height,
-                                                   i2i_seed], outputs=i2i_image_output, concurrency_limit=64)
-        
-        with gr.Tab("PNG Info"):
-            def plaintext_to_html(text, classname=None):
-                content = "<br>\n".join(html.escape(x) for x in text.split('\n'))
-    
-                return f"<p class='{classname}'>{content}</p>" if classname else f"<p>{content}</p>"
-    
-    
-            def get_exif_data(image):
-                items = image.info
-    
-                info = ''
-                for key, text in items.items():
-                    info += f"""
-                    <div>
-                    <p><b>{plaintext_to_html(str(key))}</b></p>
-                    <p>{plaintext_to_html(str(text))}</p>
-                    </div>
-                    """.strip()+"\n"
-    
-                if len(info) == 0:
-                    message = "Nothing found in the image."
-                    info = f"<div><p>{message}<p></div>"
-    
-                return info
-    
-            with gr.Row():
-                with gr.Column():
-                    image_input = gr.Image(type="pil")
-                    
-                with gr.Column():
-                    exif_output = gr.HTML(label="EXIF Data")
-                    send_to_txt2img_btn = gr.Button("Send to txt2img")
-    
-            image_input.upload(get_exif_data, inputs=[image_input], outputs=exif_output)
-            send_to_txt2img_btn.click(send_to_txt2img, inputs=[image_input], outputs=[tabs, prompt, negative_prompt,
-                                                                                      steps, seed, model, sampler,
-                                                                                      width, height, cfg_scale],
-                                      concurrency_limit=32)
+                    image_output = gr.Image(
+                        value="https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png")
 
-demo.queue(max_size=40, api_open=False).launch(max_threads=128, show_api=False)
+            text_button.click(fn=txt2img, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed],
+                              outputs=image_output, concurrency_limit=64) 
+            demo.queue(max_size=40, api_open=False).launch(max_threads=128, show_api=False)