Update app.py

app.py

@@ -1,72 +1,275 @@
+import os
+import cv2
 import gradio as gr
+import numpy as np
+import random
+import base64
 import requests
-from PIL import Image
-from io import BytesIO
-
-# Hugging Face Inference API'de çalışan örnek modeller
-MODELS = {
-    "Face Detection": "https://api-inference.huggingface.co/models/retinaface",      # örnek
-    "Background Removal": "https://api-inference.huggingface.co/models/briaai/RMBG-1.4",
-    "Beautification (GAN)": "https://api-inference.huggingface.co/models/beautygan", # örnek
-    "Virtual Try-On": "https://api-inference.huggingface.co/models/cp-vton",         # örnek
-    "Style Transfer": "https://api-inference.huggingface.co/models/cyclegan"         # örnek
-}
+import json
+import time
 
-API_TOKEN = "BURAYA_HF_TOKENINI_YAZ"  # Settings → Access Tokens
 
-headers = {"Authorization": f"Bearer {API_TOKEN}",
-           "Content-Type": "application/octet-stream"}
+def tryon(person_img, garment_img, seed, randomize_seed):
+    post_start_time = time.time()
+    if person_img is None or garment_img is None:
+        gr.Warning("Empty image")
+        return None, None, "Empty image"
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    encoded_person_img = cv2.imencode('.jpg', cv2.cvtColor(person_img, cv2.COLOR_RGB2BGR))[1].tobytes()
+    encoded_person_img = base64.b64encode(encoded_person_img).decode('utf-8')
+    encoded_garment_img = cv2.imencode('.jpg', cv2.cvtColor(garment_img, cv2.COLOR_RGB2BGR))[1].tobytes()
+    encoded_garment_img = base64.b64encode(encoded_garment_img).decode('utf-8')
 
-def query(model_url, pil_image: Image.Image) -> Image.Image:
-    """Bir modeli HF Inference API üzerinden çağır."""
+    url = "http://" + os.environ['tryon_url'] + "Submit"
+    token = os.environ['token']
+    cookie = os.environ['Cookie']
+    referer = os.environ['referer']
+    headers = {'Content-Type': 'application/json', 'token': token, 'Cookie': cookie, 'referer': referer}
+    data = {
+        "clothImage": encoded_garment_img,
+        "humanImage": encoded_person_img,
+        "seed": seed
+    }
     try:
-        buf = BytesIO()
-        pil_image.save(buf, format="PNG")
-        resp = requests.post(model_url, headers=headers, data=buf.getvalue(), timeout=60)
-        resp.raise_for_status()
+        response = requests.post(url, headers=headers, data=json.dumps(data), timeout=50)
+        # print("post response code", response.status_code)
+        if response.status_code == 200:
+            result = response.json()['result']
+            status = result['status']
+            if status == "success":
+                uuid = result['result']
+                # print(uuid)
+    except Exception as err:
+        print(f"Post Exception Error: {err}")
+        raise gr.Error("Too many users, please try again later")
+    post_end_time = time.time()
+    print(f"post time used: {post_end_time-post_start_time}")
 
-        # Birçok görsel modeli doğrudan görüntü döndürür
-        content = resp.content
+    get_start_time =time.time()
+    time.sleep(9)
+    Max_Retry = 12
+    result_img = None
+    info = ""
+    err_log = ""
+    for i in range(Max_Retry):
         try:
-            return Image.open(BytesIO(content)).convert("RGBA")
-        except Exception:
-            # Bazı modeller JSON/base64 döndürebilir -> desteklenmiyorsa orijinali ver
-            return pil_image
-    except Exception:
-        # Model hazır değil/uygun değilse kırılmasın, olduğu gibi geçsin
-        return pil_image
-
-def pipeline(image, cloth=None):
-    # 1) Face/Body Detection (çıktıyı sadece akışa sokuyoruz)
-    img1 = query(MODELS["Face Detection"], image)
-
-    # 2) Background Removal
-    img2 = query(MODELS["Background Removal"], img1)
-
-    # 3) Beautification
-    img3 = query(MODELS["Beautification (GAN)"], img2)
-
-    # 4) Virtual Try-On (kıyafet opsiyonel)
-    if cloth is not None:
-        img4 = query(MODELS["Virtual Try-On"], img3)
-    else:
-        img4 = img3
-
-    # 5) Style Transfer
-    img5 = query(MODELS["Style Transfer"], img4)
-
-    return img5
-
-demo = gr.Interface(
-    fn=pipeline,
-    inputs=[
-        gr.Image(type="pil", label="Fotoğraf (zorunlu)"),
-        gr.Image(type="pil", label="Kıyafet (opsiyonel)")
-    ],
-    outputs=gr.Image(type="pil", label="Çıktı"),
-    title="AI Fashion & Beauty Pipeline",
-    description="Fotoğrafını yükle → Yüz algılama → Arka plan temizleme → Güzelleştirme → (İsteğe bağlı) Kıyafet → Stil filtresi"
-)
-
-if __name__ == "__main__":
-    demo.launch()
+            url = "http://" + os.environ['tryon_url'] + "Query?taskId=" + uuid
+            response = requests.get(url, headers=headers, timeout=20)
+            # print("get response code", response.status_code)
+            if response.status_code == 200:
+                result = response.json()['result']
+                status = result['status']
+                if status == "success":
+                    result = base64.b64decode(result['result'])
+                    result_np = np.frombuffer(result, np.uint8)
+                    result_img = cv2.imdecode(result_np, cv2.IMREAD_UNCHANGED)
+                    result_img = cv2.cvtColor(result_img, cv2.COLOR_RGB2BGR)
+                    info = "Success"
+                    break
+                elif status == "error":
+                    err_log = f"Status is Error"
+                    info = "Error"
+                    break
+            else:
+                # print(response.text)
+                err_log = "URL error, pleace contact the admin"
+                info = "URL error, pleace contact the admin"
+                break
+        except requests.exceptions.ReadTimeout:
+            err_log = "Http Timeout"
+            info = "Http Timeout, please try again later"
+        except Exception as err:
+            err_log = f"Get Exception Error: {err}"
+        time.sleep(1)
+    get_end_time = time.time()
+    print(f"get time used: {get_end_time-get_start_time}")
+    print(f"all time used: {get_end_time-get_start_time+post_end_time-post_start_time}")
+    if info == "":
+        err_log = f"No image after {Max_Retry} retries"
+        info = "Too many users, please try again later"
+    if info != "Success":
+        print(f"Error Log: {err_log}")
+        gr.Warning("Too many users, please try again later")
+
+    return result_img, seed, info
+
+def start_tryon(person_img, garment_img, seed, randomize_seed):
+    start_time = time.time()
+    if person_img is None or garment_img is None:
+        return None, None, "Empty image"
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    encoded_person_img = cv2.imencode('.jpg', cv2.cvtColor(person_img, cv2.COLOR_RGB2BGR))[1].tobytes()
+    encoded_person_img = base64.b64encode(encoded_person_img).decode('utf-8')
+    encoded_garment_img = cv2.imencode('.jpg', cv2.cvtColor(garment_img, cv2.COLOR_RGB2BGR))[1].tobytes()
+    encoded_garment_img = base64.b64encode(encoded_garment_img).decode('utf-8')
+
+    url = "http://" + os.environ['tryon_url']
+    token = os.environ['token']
+    cookie = os.environ['Cookie']
+    referer = os.environ['referer']
+
+    headers = {'Content-Type': 'application/json', 'token': token, 'Cookie': cookie, 'referer': referer}
+    data = {
+        "clothImage": encoded_garment_img,
+        "humanImage": encoded_person_img,
+        "seed": seed
+    }
+
+    result_img = None
+    try:
+        session = requests.Session()
+        response = session.post(url, headers=headers, data=json.dumps(data), timeout=60)
+        print("response code", response.status_code)
+        if response.status_code == 200:
+            result = response.json()['result']
+            status = result['status']
+            if status == "success":
+                result = base64.b64decode(result['result'])
+                result_np = np.frombuffer(result, np.uint8)
+                result_img = cv2.imdecode(result_np, cv2.IMREAD_UNCHANGED)
+                result_img = cv2.cvtColor(result_img, cv2.COLOR_RGB2BGR)
+                info = "Success"
+            else:
+                info = "Try again latter"
+        else:
+            print(response.text)
+            info = "URL error, pleace contact the admin"
+    except requests.exceptions.ReadTimeout:
+        print("timeout")
+        info = "Too many users, please try again later"
+        raise gr.Error("Too many users, please try again later")
+    except Exception as err:
+        print(f"其他错误: {err}")
+        info = "Error, pleace contact the admin"
+    end_time = time.time()
+    print(f"time used: {end_time-start_time}")
+
+    return result_img, seed, info
+
+MAX_SEED = 999999
+
+example_path = os.path.join(os.path.dirname(__file__), 'assets')
+
+garm_list = os.listdir(os.path.join(example_path,"cloth"))
+garm_list_path = [os.path.join(example_path,"cloth",garm) for garm in garm_list]
+
+human_list = os.listdir(os.path.join(example_path,"human"))
+human_list_path = [os.path.join(example_path,"human",human) for human in human_list]
+
+css="""
+#col-left {
+    margin: 0 auto;
+    max-width: 430px;
+}
+#col-mid {
+    margin: 0 auto;
+    max-width: 430px;
+}
+#col-right {
+    margin: 0 auto;
+    max-width: 430px;
+}
+#col-showcase {
+    margin: 0 auto;
+    max-width: 1100px;
+}
+#button {
+    color: blue;
+}
+"""
+
+def load_description(fp):
+    with open(fp, 'r', encoding='utf-8') as f:
+        content = f.read()
+    return content
+
+def change_imgs(image1, image2):
+    return image1, image2
+
+with gr.Blocks(css=css) as Tryon:
+    gr.HTML(load_description("assets/title.md"))
+    with gr.Row():
+        with gr.Column(elem_id = "col-left"):
+            gr.HTML("""
+            <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
+                <div>
+                Step 1.  Upload a person image ⬇️
+                </div>
+            </div>
+            """)
+        with gr.Column(elem_id = "col-mid"):
+            gr.HTML("""
+            <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
+                <div>
+                Step 2. Upload a garment image ⬇️
+                </div>
+            </div>
+            """)
+        with gr.Column(elem_id = "col-right"):
+            gr.HTML("""
+            <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
+                <div>
+                Step 3. Press “Run” to get try-on results
+                </div>
+            </div>
+            """)
+    with gr.Row():
+        with gr.Column(elem_id = "col-left"):
+            imgs = gr.Image(label="Person image", sources='upload', type="numpy")
+            # category = gr.Dropdown(label="Garment category", choices=['upper_body', 'lower_body', 'dresses'],  value="upper_body")
+            example = gr.Examples(
+                inputs=imgs,
+                examples_per_page=12,
+                examples=human_list_path
+            )
+        with gr.Column(elem_id = "col-mid"):
+            garm_img = gr.Image(label="Garment image", sources='upload', type="numpy")
+            example = gr.Examples(
+                inputs=garm_img,
+                examples_per_page=12,
+                examples=garm_list_path
+            )
+        with gr.Column(elem_id = "col-right"):
+            image_out = gr.Image(label="Result", show_share_button=False)
+            with gr.Row():
+                seed = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=MAX_SEED,
+                    step=1,
+                    value=0,
+                )
+                randomize_seed = gr.Checkbox(label="Random seed", value=True)
+            with gr.Row():
+                seed_used = gr.Number(label="Seed used")
+                result_info = gr.Text(label="Response")
+            # try_button = gr.Button(value="Run", elem_id="button")
+            test_button = gr.Button(value="Run", elem_id="button")
+
+
+    # try_button.click(fn=start_tryon, inputs=[imgs, garm_img, seed, randomize_seed], outputs=[image_out, seed_used, result_info], api_name='tryon',concurrency_limit=10)
+    test_button.click(fn=tryon, inputs=[imgs, garm_img, seed, randomize_seed], outputs=[image_out, seed_used, result_info], api_name=False, concurrency_limit=45)
+
+    with gr.Column(elem_id = "col-showcase"):
+        gr.HTML("""
+        <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
+            <div> </div>
+            <br>
+            <div>
+            Virtual try-on examples in pairs of person and garment images
+            </div>
+        </div>
+        """)
+        show_case = gr.Examples(
+            examples=[
+                ["assets/examples/model2.png", "assets/examples/garment2.png", "assets/examples/result2.png"],
+                ["assets/examples/model3.png", "assets/examples/garment3.png", "assets/examples/result3.png"],
+                ["assets/examples/model1.png", "assets/examples/garment1.png", "assets/examples/result1.png"],
+            ],
+            inputs=[imgs, garm_img, image_out],
+            label=None
+        )
+
+Tryon.queue(api_open=False).launch(show_api=False)