Upload app.py

app.py

@@ -1,116 +1,116 @@
-import gradio as gr
-import requests
-import io
-import random
-import os
-import time
-from PIL import Image
-from deep_translator import GoogleTranslator
-import json
-
-# Project by Nymbo
-
-API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"
-API_TOKEN = os.getenv("HF_READ_TOKEN")
-headers = {"Authorization": f"Bearer {API_TOKEN}"}
-timeout = 100
-
-# Function to query the API and return the generated image
-def query(prompt, is_negative=False, steps=30, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024):
-    if prompt == "" or prompt is None:
-        return None
-
-    key = random.randint(0, 999)
-    
-    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
-    headers = {"Authorization": f"Bearer {API_TOKEN}"}
-    
-    # Translate the prompt from Russian to English if necessary
-    prompt = GoogleTranslator(source='my', target='en').translate(prompt)
-    print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')
-
-    # Add some extra flair to the prompt
-    prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
-    print(f'\033[1mGeneration {key}:\033[0m {prompt}')
-    
-    # Prepare the payload for the API call, including width and height
-    payload = {
-        "inputs": prompt,
-        "is_negative": is_negative,
-        "steps": steps,
-        "cfg_scale": cfg_scale,
-        "seed": seed if seed != -1 else random.randint(1, 1000000000),
-        "strength": strength,
-        "parameters": {
-            "width": width,  # Pass the width to the API
-            "height": height  # Pass the height to the API
-        }
-    }
-
-    # Send the request to the API and handle the response
-    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}")
-        if response.status_code == 503:
-            raise gr.Error(f"{response.status_code} : The model is being loaded")
-        raise gr.Error(f"{response.status_code}")
-    
-    try:
-        # Convert the response content into an image
-        image_bytes = response.content
-        image = Image.open(io.BytesIO(image_bytes))
-        print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
-        return image
-    except Exception as e:
-        print(f"Error when trying to open the image: {e}")
-        return None
-
-# CSS to style the app
-css = """
-#app-container {
-    max-width: 800px;
-    margin-left: auto;
-    margin-right: auto;
-}
-"""
-
-# Build the Gradio UI with Blocks
-with gr.Blocks(theme='Nymbo/Nymbo_Theme', css=css) as app:
-    # Add a title to the app
-    gr.HTML("<center><h1>Walone AI Image Pro Max</h1></center>")
-    
-    # Container for all the UI elements
-    with gr.Column(elem_id="app-container"):
-        # Add a text input for the main prompt
-        with gr.Row():
-            with gr.Column(elem_id="prompt-container"):
-                with gr.Row():
-                    text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=2, elem_id="prompt-text-input")
-                
-                # Accordion for advanced settings
-                with gr.Row():
-                    with gr.Accordion("Advanced Settings", open=False):
-                        negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What should not be in the image", value="(deformed, distorted, disfigured), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, misspellings, typos", lines=3, elem_id="negative-prompt-text-input")
-                        with gr.Row():
-                            width = gr.Slider(label="Width", value=1024, minimum=64, maximum=1216, step=32)
-                            height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, step=32)
-                        steps = gr.Slider(label="Sampling steps", value=4, minimum=1, maximum=100, step=1)
-                        cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=1)
-                        strength = gr.Slider(label="Strength", value=0.7, minimum=0, maximum=1, step=0.001)
-                        seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1) # Setting the seed to -1 will make it random
-                        method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
-
-        # Add a button to trigger the image generation
-        with gr.Row():
-            text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
-        
-        # Image output area to display the generated image
-        with gr.Row():
-            image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
-        
-        # Bind the button to the query function with the added width and height inputs
-        text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height], outputs=image_output)
-
-# Launch the Gradio app
+import gradio as gr
+import requests
+import io
+import random
+import os
+import time
+from PIL import Image
+from deep_translator import GoogleTranslator
+import json
+
+# Project by Nymbo
+
+API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"
+API_TOKEN = os.getenv("HF_READ_TOKEN")
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
+timeout = 100
+
+# Function to query the API and return the generated image
+def query(prompt, is_negative=False, steps=30, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024):
+    if prompt == "" or prompt is None:
+        return None
+
+    key = random.randint(0, 999)
+    
+    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
+    headers = {"Authorization": f"Bearer {API_TOKEN}"}
+    
+    # Translate the prompt from Russian to English if necessary
+    prompt = GoogleTranslator(source='my', target='en').translate(prompt)
+    print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')
+
+    # Add some extra flair to the prompt
+    prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
+    print(f'\033[1mGeneration {key}:\033[0m {prompt}')
+    
+    # Prepare the payload for the API call, including width and height
+    payload = {
+        "inputs": prompt,
+        "is_negative": is_negative,
+        "steps": steps,
+        "cfg_scale": cfg_scale,
+        "seed": seed if seed != -1 else random.randint(1, 1000000000),
+        "strength": strength,
+        "parameters": {
+            "width": width,  # Pass the width to the API
+            "height": height  # Pass the height to the API
+        }
+    }
+
+    # Send the request to the API and handle the response
+    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}")
+        if response.status_code == 503:
+            raise gr.Error(f"{response.status_code} : The model is being loaded")
+        raise gr.Error(f"{response.status_code}")
+    
+    try:
+        # Convert the response content into an image
+        image_bytes = response.content
+        image = Image.open(io.BytesIO(image_bytes))
+        print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
+        return image
+    except Exception as e:
+        print(f"Error when trying to open the image: {e}")
+        return None
+
+# CSS to style the app
+css = """
+#app-container {
+    max-width: 800px;
+    margin-left: auto;
+    margin-right: auto;
+}
+"""
+
+# Build the Gradio UI with Blocks
+with gr.Blocks(theme='Nymbo/Nymbo_Theme', css=css) as app:
+    # Add a title to the app
+    gr.HTML("<center><h1>AI Image Pro</h1></center>")
+    
+    # Container for all the UI elements
+    with gr.Column(elem_id="app-container"):
+        # Add a text input for the main prompt
+        with gr.Row():
+            with gr.Column(elem_id="prompt-container"):
+                with gr.Row():
+                    text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=2, elem_id="prompt-text-input")
+                
+                # Accordion for advanced settings
+                with gr.Row():
+                    with gr.Accordion("Advanced Settings", open=False):
+                        negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What should not be in the image", value="(deformed, distorted, disfigured), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, misspellings, typos", lines=3, elem_id="negative-prompt-text-input")
+                        with gr.Row():
+                            width = gr.Slider(label="Width", value=1216, minimum=64, maximum=1216, step=32)
+                            height = gr.Slider(label="Height", value=760, minimum=64, maximum=1216, step=32)
+                        steps = gr.Slider(label="Sampling steps", value=15, minimum=1, maximum=100, step=1)
+                        cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=1)
+                        strength = gr.Slider(label="Strength", value=0.7, minimum=0, maximum=1, step=0.001)
+                        seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1) # Setting the seed to -1 will make it random
+                        method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
+
+        # Add a button to trigger the image generation
+        with gr.Row():
+            text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
+        
+        # Image output area to display the generated image
+        with gr.Row():
+            image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
+        
+        # Bind the button to the query function with the added width and height inputs
+        text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height], outputs=image_output)
+
+# Launch the Gradio app
 app.launch(show_api=False, share=False)