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src/fal_api.py

@@ -3,7 +3,7 @@ from src.utils import numpy_to_base64
 from src.helpers import resize_image
 
 def fal_ipadapter_api(input_image,ip_image,seg_prompt):
-  print(input_image,ip_image,seg_prompt)
+  #print(input_image,ip_image,seg_prompt)
 
   handler = fal_client.submit(
         "comfy/JarvisSan22/cloth_ipadapter",
@@ -13,10 +13,10 @@ def fal_ipadapter_api(input_image,ip_image,seg_prompt):
             "groundingdinosamsegment (segment anything)_prompt":seg_prompt
         },
     )
-  print(handler)
+  #print(handler)
   result= handler.get()
   #image_urls=[]
-  print(result["outputs"])
+  #print(result["outputs"])
   """
   for k,item in result["outputs"].items():
     if "images" in item:
@@ -28,11 +28,11 @@ def fal_ipadapter_api(input_image,ip_image,seg_prompt):
   return result["outputs"]["20"]["images"][0]["url"]
 
 def fal_faceswap_api(input_image,face_image):
-  print(input_image.shape,face_image.shape,type(face_image))
+  #print(input_image.shape,face_image.shape,type(face_image))
   #face_image.resize((1024, 1024))
   input_image=resize_image(input_image)
   face_image=resize_image(face_image)
-  print(input_image.shape,face_image.shape)
+  #print(input_image.shape,face_image.shape)
   handler = fal_client.submit(
       "fal-ai/face-swap",
       arguments={
@@ -42,5 +42,5 @@ def fal_faceswap_api(input_image,face_image):
   )
 
   result = handler.get()
-  print(result)
+  #print(result)
   return result["image"]["url"]

src/helpers.py

@@ -1,43 +1,43 @@
-import numpy as np
-from PIL import Image
-
-def resize_image(image_array, max_size=1048576):
-    """
-    Resizes a NumPy image array to a maximum total size of 1048576 pixels while preserving the aspect ratio.
-
-    Args:
-        image_array (numpy.ndarray): The input image array.
-        max_size (int): The maximum total size of the image in pixels (default is 1048576).
-
-    Returns:
-        numpy.ndarray: The resized image array.
-    """
-    # Get the current size of the image
-    height, width, _ = image_array.shape
-
-    # Calculate the total size of the image
-    total_size = height * width
-
-    # If the image is already smaller than the max size, return the original image
-    if total_size <= max_size:
-        return image_array
-
-    # Calculate the aspect ratio
-    aspect_ratio = width / height
-
-    # Calculate the new dimensions while preserving the aspect ratio
-    if aspect_ratio > 1:
-        new_width = int(np.sqrt(max_size * aspect_ratio))
-        new_height = int(new_width / aspect_ratio)
-    else:
-        new_height = int(np.sqrt(max_size / aspect_ratio))
-        new_width = int(new_height * aspect_ratio)
-
-    # Resize the image using Pillow
-    image = Image.fromarray(image_array)
-    image = image.resize((new_width, new_height), resample=Image.BICUBIC)
-
-    # Convert the resized image back to a NumPy array
-    resized_image = np.array(image)
-
+import numpy as np
+from PIL import Image
+
+def resize_image(image_array, max_size=1048576):
+    """
+    Resizes a NumPy image array to a maximum total size of 1048576 pixels while preserving the aspect ratio.
+
+    Args:
+        image_array (numpy.ndarray): The input image array.
+        max_size (int): The maximum total size of the image in pixels (default is 1048576).
+
+    Returns:
+        numpy.ndarray: The resized image array.
+    """
+    # Get the current size of the image
+    height, width, _ = image_array.shape
+
+    # Calculate the total size of the image
+    total_size = height * width
+
+    # If the image is already smaller than the max size, return the original image
+    if total_size <= max_size:
+        return image_array
+
+    # Calculate the aspect ratio
+    aspect_ratio = width / height
+
+    # Calculate the new dimensions while preserving the aspect ratio
+    if aspect_ratio > 1:
+        new_width = int(np.sqrt(max_size * aspect_ratio))
+        new_height = int(new_width / aspect_ratio)
+    else:
+        new_height = int(np.sqrt(max_size / aspect_ratio))
+        new_width = int(new_height * aspect_ratio)
+
+    # Resize the image using Pillow
+    image = Image.fromarray(image_array)
+    image = image.resize((new_width, new_height), resample=Image.BICUBIC)
+
+    # Convert the resized image back to a NumPy array
+    resized_image = np.array(image)
+
     return resized_image

src/mail.py

@@ -0,0 +1,48 @@
+
+import smtplib
+import numpy as np
+from email.mime.text import MIMEText
+from email.mime.image import MIMEImage
+from email.mime.multipart import MIMEMultipart
+from email.utils import formatdate
+
+def send_email(email, selected_images):
+    # Email configuration (replace with your SMTP server details)
+    smtp_server = "smtp.gmail.com"
+    smtp_port = 587
+    # Create the email message
+    msg = MIMEMultipart()
+    msg['Subject'] = 'Selected Image'
+    msg['From'] = os.getenv("sender_email")
+    msg['To'] = email
+    msg['Date'] = formatdate()
+
+    # Add text to the email
+    text = MIMEText(f"""
+    今日はブースを訪れてくれてありがとうございました。。
+  　以下は、あなたが生成した{len(selected_images)}枚の画像です。
+    """,_charset="utf-8")
+    msg.attach(text)
+    print(selected_images)
+    for i,image in enumerate(selected_images):
+      image=np.array(image)[0] 
+      print(image)
+      with open(image, 'rb') as f:
+          img_data = f.read()
+      image = MIMEImage(img_data, name=f"image_{i+1}.jpg")
+      msg.attach(image)
+
+    # Send the email
+    try:
+        with smtplib.SMTP(smtp_server, smtp_port) as server:
+            server.starttls()
+            server.login( os.getenv("sender_email"), os.getenv("sender_password"))
+            server.send_message(msg)
+        return "Email sent successfully!"
+    except smtplib.SMTPAuthenticationError:
+        raise Exception("SMTP Authentication failed. Please check your email and password.")
+    except smtplib.SMTPException as e:
+        raise Exception(f"SMTP error occurred: {str(e)}")
+    except Exception as e:
+        raise Exception(f"Failed to send email: {str(e)}")
+    

src/rep_api.py

@@ -89,9 +89,26 @@ def generate_image_replicate(prompt,lora_model,api_path,aspect_ratio,model,lora_
   )
   print(output)
   return output[0]
-
+def replicate_bgcontrolnet(img,prompt,background_prompt, sampler_name= "DPM++ SDE Karras",
+        negative_prompt="(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.4), (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation"
+  ):
+    img=image_to_base64(img)
+    prompt=prompt+" ," +background_prompt
+    output=replicate.run(
+        "wolverinn/realistic-background:9f020c55e037529bf20ed1cb799d7aa290404cfbd45157686717ffc7ee511eab",
+    input={
+        "seed": -1,
+        "image":img,
+        "prompt":prompt,
+        "sampler_name":sampler_name,
+        "negative_prompt":negative_prompt
+    }
+    ) 
+    print(output["output"]["image"])
+    return output["output"]["image"]
 
 def replicate_caption_api(image,model,context_text):
+  print(model,context_text)
   base64_image = image_to_base64(image)
   if model=="blip":
     output = replicate.run(

src/utils.py

@@ -68,6 +68,12 @@ def add_to_prompt(existing_prompt, new_prompt):
         return f"{existing_prompt}, {new_prompt}"
     else:
         return new_prompt
+    
+def update_gallery(img,gallery_list):
+    img=convert_to_pil(img)
+    
+    gallery_list.append(img)
+    return gallery_list