Upload folder using huggingface_hub

.gitattributes

@@ -46,3 +46,7 @@ images/[email protected]_gallery1.png filter=lfs diff=lfs merge=lfs -text
 images/[email protected]_gallery2.png filter=lfs diff=lfs merge=lfs -text
 images/[email protected]_gallery3.png filter=lfs diff=lfs merge=lfs -text
 images/[email protected]_gallery4.png filter=lfs diff=lfs merge=lfs -text
+images/yashviii@qq_gallery1.png filter=lfs diff=lfs merge=lfs -text
+images/yashviii@qq_gallery2.png filter=lfs diff=lfs merge=lfs -text
+images/yashviii@qq_gallery3.png filter=lfs diff=lfs merge=lfs -text
+images/yashviii@qq_gallery4.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,5 +1,5 @@
 ---
-title: IDfy-Avatarify
+title: Idfy-Avatarify
 app_file: gradio_demo/app.py
 sdk: gradio
 sdk_version: 4.38.1

gradio_demo/app.py

@@ -29,6 +29,26 @@ from style_template import styles
 from pipeline_stable_diffusion_xl_instantid_full import StableDiffusionXLInstantIDPipeline
 from model_util import load_models_xl, get_torch_device, torch_gc
 
+import os
+
+# try:
+#     # Send a GET request to the URL
+#     response = requests.get("https://storage.googleapis.com/idfy-gff-public/idfy-gff-public%40idfy-eve-ml-training.iam.gserviceaccount.com.json")
+
+#     # Raise an exception if the request was unsuccessful
+#     response.raise_for_status()
+
+#     # Save the file to the specified path
+#     with open("serviceaccount.json", 'wb') as file:
+#         file.write(response.content)
+
+#     print(f"Service account JSON file successfully downloaded")
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "serviceaccount.json"
+
+# except requests.exceptions.RequestException as e:
+#     print(f"Failed to download the service account JSON file: {e}")
+
+
 
 # global variable
 MAX_SEED = np.iinfo(np.int32).max
@@ -54,17 +74,10 @@ logo = logo.resize((100, 100))
 from cv2 import imencode
 import base64
 
-# def encode_pil_to_base64_new(pil_image):
-#     print("AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA")
-#     image_arr = np.asarray(pil_image)[:,:,::-1]
-#     _, byte_data = imencode('.png', image_arr)        
-#     base64_data = base64.b64encode(byte_data)
-#     base64_string_opencv = base64_data.decode("utf-8")
-#     return "data:image/png;base64," + base64_string_opencv
-
 import gradio as gr
+from google.cloud import storage
+from io import BytesIO
 
-# gr.processing_utils.encode_pil_to_base64 = encode_pil_to_base64_new
 
 def main(pretrained_model_name_or_path="wangqixun/YamerMIX_v8", enable_lcm_arg=False):
 
@@ -112,16 +125,8 @@ def main(pretrained_model_name_or_path="wangqixun/YamerMIX_v8", enable_lcm_arg=F
     pipe.disable_lora()
     
     def remove_tips():
-        print("GG")
         return gr.update(visible=False)
 
-    
-    # prompts = [
-    #     ["superman","Vibrant Color"], ["japanese anime character with white/neon hair","Watercolor"], 
-    #     # ["Suited professional","(No style)"], 
-    #     ["Scooba diver","Line art"], ["eskimo","Snow"]
-    # ]
-
     def convert_from_cv2_to_image(img: np.ndarray) -> Image:
         return Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
 
@@ -146,12 +151,25 @@ def main(pretrained_model_name_or_path="wangqixun/YamerMIX_v8", enable_lcm_arg=F
         # if email != "":
         p,n = styles.get(style, styles.get(STYLE_NAMES[1]))
         return generate_image(face_file, p[3], n)
-    
-#     def validate_and_process(face_file, style, email):
-    
-#     # Your processing logic here
-#     gallery1, gallery2, gallery3, gallery4 = run_for_prompts1(face_file, style), run_for_prompts2(face_file, style), run_for_prompts3(face_file, style), run_for_prompts4(face_file, style)
-#     return gallery1, gallery2, gallery3, gallery4
+        
+    def upload_pil_image_to_gcs(image, destination_blob_name):
+        bucket_name="idfy-gff-public"
+        # Convert PIL image to byte stream
+        image_byte_array = BytesIO()
+        image.save(image_byte_array, format='PNG')  # Save image in its original format
+        image_byte_array.seek(0)
+
+        # Initialize a GCP client
+        storage_client = storage.Client()
+
+        # Get the bucket
+        bucket = storage_client.bucket(bucket_name)
+
+        # Create a blob object from the filename
+        blob = bucket.blob(destination_blob_name)
+
+        # Upload the image to GCS
+        blob.upload_from_file(image_byte_array, content_type=f'image/png')
 
     def draw_kps(image_pil, kps, color_list=[(255,0,0), (0,255,0), (0,0,255), (255,255,0), (255,0,255)]):
         stickwidth = 4
@@ -185,7 +203,6 @@ def main(pretrained_model_name_or_path="wangqixun/YamerMIX_v8", enable_lcm_arg=F
                 pad_to_max_side=True, mode=PIL.Image.BILINEAR, base_pixel_number=64):
 
             w, h = input_image.size
-            print(f"Original Size --> {input_image.size}")
             if size is not None:
                 w_resize_new, h_resize_new = size
             else:
@@ -203,62 +220,27 @@ def main(pretrained_model_name_or_path="wangqixun/YamerMIX_v8", enable_lcm_arg=F
                 offset_y = (max_side - h_resize_new) // 2
                 res[offset_y:offset_y+h_resize_new, offset_x:offset_x+w_resize_new] = np.array(input_image)
                 input_image = Image.fromarray(res)
-            
-            print(f"Final modified image size --> {input_image.size}")
             return input_image
 
     # def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]:
     #     p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
     #     return p.replace("{prompt}", positive), n + ' ' + negative
     
-    def store_images(email, gallery1, gallery2, gallery3, gallery4,consent):
+    def store_images(email, gallery1, gallery2, gallery3, gallery4,consent,style):
+        if not email:
+            raise gr.Error("Email Id not provided")
         if not consent:
             raise gr.Error("Consent not provided")
-        galleries = []
         for i, img in enumerate([gallery1, gallery2, gallery3, gallery4], start=1):
-            if isinstance(img, np.ndarray):
-                img = Image.fromarray(img)
-            print(f"Gallery {i} type after conversion: {type(img)}")
-            galleries.append(img)
-        # Create the images directory if it doesn't exist
-        if not os.path.exists('images'):
-            os.makedirs('images')
-
-        # Define image file paths
-        image_paths = []
-        for i, img in enumerate(galleries, start=1):
-            img_path = f'images/{email}_gallery{i}.png'
-            print("saving image")
-            img.save(img_path)
-            image_paths.append(img_path)
-            print("saved image at path ",img_path)
-
-        # Define the CSV file path
-        csv_file_path = 'image_data.csv'
-
-        # Create a DataFrame for the email and image paths
-        df = pd.DataFrame({
-            'email': [email],
-            'img1_path': [image_paths[0]],
-            'img2_path': [image_paths[1]],
-            'img3_path': [image_paths[2]],
-            'img4_path': [image_paths[3]],
-        })
-
-        print("created df ",df)
-
-        # Write to CSV (append if the file exists, create a new one if it doesn't)
-        if not os.path.isfile(csv_file_path):
-            df.to_csv(csv_file_path, index=False)
-        else:
-            df_check = pd.read_csv(csv_file_path)
-            print(f"CSV content before append:\n{df_check}")
-            df.to_csv(csv_file_path, mode='a', header=False, index=False)
-            print("writing to csv",csv_file_path)
-            df_check = pd.read_csv(csv_file_path)
-            print(f"CSV content before append:\n{df_check}")
-        
+            try:
+                if isinstance(img, np.ndarray):
+                    img = Image.fromarray(img)
+                dest = f'{email}/img{i}@{style}.png'
+                upload_pil_image_to_gcs(img,dest)
+            except Exception as e:
+                print()
         gr.Info("Thankyou!! Your avatar is on the way to your inbox")
+        return None,None,None,None,None
     
     def add_watermark(image, watermark=logo, opacity=128, position="bottom_right", padding=10):
         # Convert NumPy array to PIL Image if needed
@@ -311,10 +293,6 @@ def main(pretrained_model_name_or_path="wangqixun/YamerMIX_v8", enable_lcm_arg=F
         num_steps = 15
         guidance_scale = 5
         seed = random.randint(0, MAX_SEED)
-        print(f"Seed --> {seed}")
-        
-        # negative_prompt = ""
-        # negative_prompt += neg
         enhance_face_region = True
         if enable_LCM:
             pipe.enable_lora()
@@ -325,14 +303,6 @@ def main(pretrained_model_name_or_path="wangqixun/YamerMIX_v8", enable_lcm_arg=F
     
         if face_image is None:
             raise gr.Error(f"Cannot find any input face image! Please upload the face image")
-        
-        # if prompt is None:
-        #     prompt = "a person"
-        
-        # apply the style template
-        # prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)
-        
-        # face_image = load_image(face_image_path)
         face_image = resize_img(face_image)
         face_image_cv2 = convert_from_image_to_cv2(face_image)
         height, width, _ = face_image_cv2.shape
@@ -373,9 +343,6 @@ def main(pretrained_model_name_or_path="wangqixun/YamerMIX_v8", enable_lcm_arg=F
                         
         generator = torch.Generator(device=device).manual_seed(seed)
         
-        print("Start inference...")
-        print(f"[Debug] Prompt: {prompt}, \n[Debug] Neg Prompt: {negative_prompt}")
-        
         pipe.set_ip_adapter_scale(adapter_strength_ratio)
         images = pipe(
             prompt=prompt,
@@ -408,21 +375,6 @@ def main(pretrained_model_name_or_path="wangqixun/YamerMIX_v8", enable_lcm_arg=F
     article = r""""""
 
     tips = r""""""
-    # css = '''
-    # .gradio-container {
-    #     width: 95% !important; 
-    #     background-image: url('./InstantID/gradio_demo/logo.png'); 
-    #     background-size: cover;
-    #     background-position: center;
-    # } 
-    # .image-gallery {
-    #     height: 100vh !important; 
-    #     overflow: auto;
-    # }
-    # .gradio-row .gradio-element { 
-    #     margin: 0 !important; 
-    # }
-    # '''
     css = '''
     .gradio-container {width: 100% !important; color: white; background: linear-gradient(135deg, #1C43B9, #254977, #343434);} 
     .gradio-row .gradio-element { margin: 0 !important; }
@@ -431,7 +383,7 @@ def main(pretrained_model_name_or_path="wangqixun/YamerMIX_v8", enable_lcm_arg=F
     justify-content: center;
     align-items: center;
     width: 100%;}
-    #sub-btn,#store-btn {
+    #submit-btn, #store-btn {
     background: linear-gradient(to right, #ffffff, #f2bb13); !important;
     color: #254977 !important;
     }
@@ -442,47 +394,23 @@ def main(pretrained_model_name_or_path="wangqixun/YamerMIX_v8", enable_lcm_arg=F
         gr.Markdown(title)
         with gr.Column():
             with gr.Row():
-                gr.Image("./gradio_demo/logo.png", scale=0, min_width=50, show_label=False, show_download_button=False)
+                gr.Image("./gradio_demo/logo.png", scale=0, min_width=50, show_label=False, show_download_button=False, show_share_button=False)
                 gr.Markdown(description)
             style = gr.Dropdown(label="Choose your STYLE", choices=STYLE_NAMES)
             with gr.Row(equal_height=True):  # Center the face file
                 with gr.Column(elem_id="centered-face", elem_classes=["centered-column"]):  # Use CSS class for centering
                     face_file = gr.Image(label="Upload a photo of your face", type="pil", height=400, width=500)
-            submit = gr.Button("Submit", variant="primary",elem_id="sub-btn")
+            submit = gr.Button("Submit", variant="primary",elem_id="submit-btn")
             with gr.Column():
                 with gr.Row():
-                    gallery1 = gr.Image(label="Generated Images")
-                    gallery2 = gr.Image(label="Generated Images")
+                    gallery1 = gr.Image(label="Generated Images", interactive=False, height=640, width=640)
+                    gallery2 = gr.Image(label="Generated Images", interactive=False, height=640, width=640)
                 with gr.Row():
-                    gallery3 = gr.Image(label="Generated Images")
-                    gallery4 = gr.Image(label="Generated Images")
+                    gallery3 = gr.Image(label="Generated Images", interactive=False, height=640, width=640)
+                    gallery4 = gr.Image(label="Generated Images", interactive=False, height=640, width=640)
             email = gr.Textbox(label="Email", info="Enter your email address", value="")
-            consent = gr.Checkbox(label="I am giving my consent to use my data to share my AI Avtar and IDfy relevant information from time to time")
-            submit1 = gr.Button("SUBMIT",elem_id="store-btn")
-#     with gr.Blocks(css=css) as demo:
-
-#         # description
-#         gr.Markdown(title)
-#         with gr.Column():
-#             with gr.Row():
-#                 gr.Image("./gradio_demo/logo.png",scale=0,min_width=50,show_label=False,show_download_button=False)
-#                 gr.Markdown(description)
-#             style = gr.Dropdown(label="Choose your STYLE", choices=STYLE_NAMES)
-#             face_file = gr.Image(label="Upload a photo of your face", type="pil",sources="webcam", height=400, width=500)
-#             submit = gr.Button("Submit", variant="primary")
-#             with gr.Column():
-#                 with gr.Row():
-#                     gallery1 = gr.Image(label="Generated Images")
-#                     gallery2 = gr.Image(label="Generated Images")
-#                 with gr.Row():
-#                     gallery3 = gr.Image(label="Generated Images")
-#                     gallery4 = gr.Image(label="Generated Images")
-#             email = gr.Textbox(label="Email",
-#                     info="Enter your email address",
-#                     value="")
-#             consent = gr.Checkbox(label="I am giving my consent to use my data to share my AI Avtar and IDfy relevant information from time to time")
-#             submit1 = gr.Button("STORE", variant="primary")
-#                 # submit1 = gr.Button("Store")
+            consent = gr.Checkbox(label="I am giving my consent to use my data to share my AI Avtar and IDfy relevant information from time to time", value=True)
+            submit1 = gr.Button("SUBMIT", variant = "primary", elem_id="store-btn")
             usage_tips = gr.Markdown(label="Usage tips of InstantID", value=tips ,visible=False)
                 
             face_file.upload(
@@ -534,127 +462,14 @@ def main(pretrained_model_name_or_path="wangqixun/YamerMIX_v8", enable_lcm_arg=F
             
             submit1.click(
                 fn=store_images,
-                inputs=[email,gallery1,gallery2,gallery3,gallery4,consent],
-                outputs=None)
+                inputs=[email,gallery1,gallery2,gallery3,gallery4,consent,style],
+                outputs=[face_file,gallery1,gallery2,gallery3,gallery4])
             
         
         
         gr.Markdown(article)
 
     demo.launch(share=True)
-
-#     with gr.Blocks(css=css, js=js) as demo:
-
-#         # description
-#         gr.Markdown(title)
-#         with gr.Row():
-#             gr.Image("./gradio_demo/logo.png",scale=0,min_width=50,show_label=False,show_download_button=False)
-#             gr.Markdown(description)
-#         with gr.Row():
-#             with gr.Column():
-#                 style = gr.Dropdown(label="Choose your STYLE", choices=STYLE_NAMES)
-#                 face_file = gr.Image(label="Upload a photo of your face", type="pil",sources="webcam")
-#                 submit = gr.Button("Submit", variant="primary")
-#             with gr.Column():
-#                 with gr.Row():
-#                     gallery1 = gr.Image(label="Generated Images")
-#                     gallery2 = gr.Image(label="Generated Images")
-#                 with gr.Row():
-#                     gallery3 = gr.Image(label="Generated Images")
-#                     gallery4 = gr.Image(label="Generated Images")
-#                 email = gr.Textbox(label="Email",
-#                         info="Enter your email address",
-#                         value="")
-            
-#             usage_tips = gr.Markdown(label="Usage tips of InstantID", value=tips ,visible=False)
-#             # identitynet_strength_ratio = gr.Slider(
-#             #     label="IdentityNet strength (for fidelity)",
-#             #     minimum=0,
-#             #     maximum=1.5,
-#             #     step=0.05,
-#             #     value=0.95,
-#             # )
-#             # adapter_strength_ratio = gr.Slider(
-#             #     label="Image adapter strength (for detail)",
-#             #     minimum=0,
-#             #     maximum=1.5,
-#             #     step=0.05,
-#             #     value=0.60,
-#             # )
-#             # negative_prompt = gr.Textbox(
-#             #     label="Negative Prompt", 
-#             #     placeholder="low quality",
-#             #     value="(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
-#             # )
-#             # num_steps = gr.Slider( 
-#             #     label="Number of sample steps",
-#             #     minimum=15,
-#             #     maximum=100,
-#             #     step=1,
-#             #     value=5 if enable_lcm_arg else 15,
-#             # )
-#             # guidance_scale = gr.Slider(
-#             #     label="Guidance scale",
-#             #     minimum=0.1,
-#             #     maximum=10.0,
-#             #     step=0.1,
-#             #     value=0 if enable_lcm_arg else 8.5,
-#             # )
-#             # if email is None:
-#             #     print("STOPPPP")
-#             #     raise gr.Error("Email ID is compulsory")
-#             face_file.upload(
-#                 fn=remove_tips,
-#                 outputs=usage_tips,
-#                 queue=True,
-#                 api_name=False,
-#                 show_progress = "full"
-#             ).then(
-#                 fn=run_for_prompts1,
-#                 inputs=[face_file,style],
-#                 outputs=[gallery1]
-#             ).then(
-#                 fn=run_for_prompts2,
-#                 inputs=[face_file,style],
-#                 outputs=[gallery2]
-#             ).then(
-#                 fn=run_for_prompts3,
-#                 inputs=[face_file,style],
-#                 outputs=[gallery3]
-#             ).then(
-#                 fn=run_for_prompts4,
-#                 inputs=[face_file,style],
-#                 outputs=[gallery4]
-#             )
-#             submit.click(
-#                 fn=remove_tips,
-#                 outputs=usage_tips,
-#                 queue=True,
-#                 api_name=False,
-#                 show_progress = "full"
-#             ).then(
-#                 fn=run_for_prompts1,
-#                 inputs=[face_file,style],
-#                 outputs=[gallery1]
-#             ).then(
-#                 fn=run_for_prompts2,
-#                 inputs=[face_file,style],
-#                 outputs=[gallery2]
-#             ).then(
-#                 fn=run_for_prompts3,
-#                 inputs=[face_file,style],
-#                 outputs=[gallery3]
-#             ).then(
-#                 fn=run_for_prompts4,
-#                 inputs=[face_file,style],
-#                 outputs=[gallery4]
-#             )
-        
-        
-#         gr.Markdown(article)
-
-#     demo.launch(share=True)
-
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--pretrained_model_name_or_path", type=str, default="wangqixun/YamerMIX_v8")

image_data.csv

@@ -13,3 +13,4 @@ yashvi,images/yashvi_gallery1.png,images/yashvi_gallery2.png,images/yashvi_galle
 [email protected],images/[email protected]_gallery1.png,images/[email protected]_gallery2.png,images/[email protected]_gallery3.png,images/[email protected]_gallery4.png
 kartik@prof,images/kartik@prof_gallery1.png,images/kartik@prof_gallery2.png,images/kartik@prof_gallery3.png,images/kartik@prof_gallery4.png
 yashvii@proffffff,images/yashvii@proffffff_gallery1.png,images/yashvii@proffffff_gallery2.png,images/yashvii@proffffff_gallery3.png,images/yashvii@proffffff_gallery4.png
+yashviii@qq,images/yashviii@qq_gallery1.png,images/yashviii@qq_gallery2.png,images/yashviii@qq_gallery3.png,images/yashviii@qq_gallery4.png

requirements.txt

@@ -16,4 +16,5 @@ gradio==4.38.1
 controlnet_aux==0.0.9
 gdown==5.2.0
 peft==0.11.1
-setuptools==71.1.0
+setuptools==71.1.0
+google-cloud-storage==2.18.2

serviceaccount.json

@@ -0,0 +1,13 @@
+{
+  "type": "service_account",
+  "project_id": "idfy-eve-ml-training",
+  "private_key_id": "10af299eab9393498e6b78641f698091bb173208",
+  "private_key": "-----BEGIN PRIVATE KEY-----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\nMW27iQwCQHrxCYA6JA+n98U=\n-----END PRIVATE KEY-----\n",
+  "client_email": "[email protected]",
+  "client_id": "104779890112284608471",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/idfy-gff-public%40idfy-eve-ml-training.iam.gserviceaccount.com",
+  "universe_domain": "googleapis.com"
+}