Update app.py

app.py

@@ -1,95 +1,57 @@
 import gradio as gr
 import numpy as np
+
 import os
 from PIL import Image
 import requests
 from io import BytesIO
 import io
 import base64
-import json  # لإضافة دعم قراءة ملف config.json
 
-# الحصول على التوكن من متغير البيئة
-hf_token = os.environ.get("HF_TOKEN_API_DEMO")
-if not hf_token:
-    raise ValueError("HF_TOKEN_API_DEMO is not set. Please set it as an environment variable.")
+hf_token = os.environ.get("HF_TOKEN_API_DEMO") # we get it from a secret env variable, such that it's private
 auth_headers = {"api_token": hf_token}
 
-# تحميل إعدادات النموذج من ملف config.json
-def load_config(config_path="config.json"):
-    if not os.path.exists(config_path):
-        raise FileNotFoundError(f"Config file not found at {config_path}")
-    with open(config_path, 'r') as file:
-        config = json.load(file)
-    print("Config loaded successfully:", config)
-    return config
-
-# تحويل الصورة إلى Base64
 def convert_mask_image_to_base64_string(mask_image):
     buffer = io.BytesIO()
-    mask_image.save(buffer, format="PNG")
+    mask_image.save(buffer, format="PNG")  # You can choose the format (e.g., "JPEG", "PNG")
+    # Encode the buffer in base64
     image_base64_string = base64.b64encode(buffer.getvalue()).decode('utf-8')
-    return f",{image_base64_string}"
+    return f",{image_base64_string}" # for some reason the funciton which downloads image from base64 expects prefix of "," which is redundant in the url
 
-# تنزيل الصورة من رابط
 def download_image(url):
     response = requests.get(url)
-    if response.status_code != 200:
-        raise ValueError(f"Failed to download image from {url}. Status code: {response.status_code}")
     return Image.open(BytesIO(response.content)).convert("RGB")
 
-# استدعاء API الخاص بالإزالة
 def eraser_api_call(image_base64_file, mask_base64_file, mask_type):
+
     url = "http://engine.prod.bria-api.com/v1/eraser"
+    
     payload = {
-        "file": image_base64_file,
-        "mask_file": mask_base64_file,
-        "mask_type": mask_type,
+    "file": image_base64_file,
+    "mask_file": mask_base64_file,
+    "mask_type": mask_type,
     }
-    print(f"Payload being sent: {payload}")  # طباعة البيانات المرسلة
     response = requests.post(url, json=payload, headers=auth_headers)
-    print(f"API Response Code: {response.status_code}")  # طباعة رمز الاستجابة
-    if response.status_code != 200:
-        print(f"Error Response: {response.text}")  # طباعة نص الخطأ إذا حدث
-        raise ValueError(f"API request failed: {response.status_code}")
-    try:
-        response_json = response.json()
-        print("Response JSON:", response_json)  # طباعة الرد JSON
-    except ValueError:
-        raise ValueError("Invalid JSON response from API")
-    
-    if "result_url" not in response_json:
-        raise KeyError("The key 'result_url' is missing in the response JSON")
+    response = response.json()
+    res_image = download_image(response["result_url"])
     
-    res_image = download_image(response_json["result_url"])
     return res_image
 
-# دالة التنبؤ
-def predict(dict):
-    print("Predict function called.")  # للتأكد من استدعاء الدالة
-    print("Received Data Keys:", dict.keys())  # طباعة المفاتيح المستلمة
 
-    if 'background' not in dict or 'layers' not in dict:
-        raise ValueError("Invalid input format. Missing 'background' or 'layers' keys.")
+def predict(dict):
 
-    init_image = Image.fromarray(dict['background'][:, :, :3], 'RGB')
-    mask = Image.fromarray(dict['layers'][0][:, :, 3], 'L')
+    init_image = Image.fromarray(dict['background'][:, :, :3], 'RGB') #dict['background'].convert("RGB")#.resize((1024, 1024))
+    mask = Image.fromarray(dict['layers'][0][:,:,3], 'L') #dict['layers'].convert("RGB")#.resize((1024, 1024))
     
-    print("Initial image and mask created.")  # تأكيد نجاح إنشاء الصور
-
     image_base64_file = convert_mask_image_to_base64_string(init_image)
     mask_base64_file = convert_mask_image_to_base64_string(mask)
     
     mask_type = "manual"
-    print("Sending API call...")  # تأكيد استدعاء API
     gen_img = eraser_api_call(image_base64_file, mask_base64_file, mask_type)
-    print("API call completed.")  # تأكيد انتهاء استدعاء API
     
     return gen_img
 
-# قراءة إعدادات config.json (إذا لزم الأمر)
-config = load_config()
 
-# CSS مخصص
 css = '''
 .gradio-container{max-width: 1100px !important}
 #image_upload{min-height:400px}
@@ -137,24 +99,49 @@ div#share-btn-container > div {flex-direction: row;background: black;align-items
 #image_upload{border-bottom-left-radius: 0px;border-bottom-right-radius: 0px}
 '''
 
-# واجهة Gradio
 image_blocks = gr.Blocks(css=css, elem_id="total-container")
 with image_blocks as demo:
     with gr.Column(elem_id="col-container"):
         gr.Markdown("## BRIA Eraser API")
-        gr.HTML('''<p>Demo description here...</p>''')
+        gr.HTML('''
+          <p style="margin-bottom: 10px; font-size: 94%">
+            This demo showcases the BRIA Eraser capability, which allows users to remove specific elements or objects from images.<br>
+            The pipeline comprises multiple components, including <a href="https://huggingface.co/briaai/BRIA-2.3" target="_blank">briaai/BRIA-2.3</a>, 
+            <a href="https://huggingface.co/briaai/BRIA-2.3-ControlNet-Inpainting" target="_blank">briaai/BRIA-2.3-ControlNet-Inpainting</a>, 
+            and <a href="https://huggingface.co/briaai/BRIA-2.3-FAST-LORA" target="_blank">briaai/BRIA-2.3-FAST-LORA</a>, all trained on licensed data.<br>
+            This ensures full legal liability coverage for copyright and privacy infringement.<br>
+            Notes:<br>
+            - High-resolution images may take longer to process.<br>
+            - For multiple masks, results are better if all masks are included in inference.<br><br>
+          </p>
+          <p style="margin-bottom: 10px; font-size: 94%">
+          API Endpoint available on: <a href="https://fal.ai/models/fal-ai/bria/eraser" target="_blank">fal.ai</a><br>
+          ComfyUI node is available here: <a href="https://github.com/Bria-AI/ComfyUI-BRIA-API" target="_blank">ComfyUI Node</a>
+          </p>  
+        ''')
     with gr.Row():
         with gr.Column():
-            image = gr.ImageEditor(
-                sources=["upload"],
-                layers=False,
-                transforms=[],
-                brush=gr.Brush(colors=["#000000"], color_mode="fixed")
-            )
+            image = gr.ImageEditor(sources=["upload"], layers=False, transforms=[], 
+                                   brush=gr.Brush(colors=["#000000"], color_mode="fixed"),
+                                   )
             with gr.Row(elem_id="prompt-container", equal_height=True):
-                btn = gr.Button("Erase!", elem_id="run_button")
+                with gr.Column():  # Wrap the button inside a Column
+                    btn = gr.Button("Erase!", elem_id="run_button")
+        
         with gr.Column():
             image_out = gr.Image(label="Output", elem_id="output-img")
-    btn.click(fn=predict, inputs=image, outputs=image_out, api_name='run')
-    gr.HTML("<div class='footer'><p>Footer text here...</p></div>")
-image_blocks.queue(max_size=25, api_open=False).launch(show_api=False)
+
+    # Button click will trigger the inpainting function (no prompt required)
+    btn.click(fn=predict, inputs=[image], outputs=[image_out], api_name='run')
+    
+
+    gr.HTML(
+        """
+            <div class="footer">
+                <p>Model by <a href="https://huggingface.co/diffusers" style="text-decoration: underline;" target="_blank">Diffusers</a> - Gradio Demo by 🤗 Hugging Face
+                </p>
+            </div>
+        """
+    )
+
+image_blocks.queue(max_size=25,api_open=False).launch(show_api=False)