Update app.py

app.py

@@ -1,70 +1,54 @@
-import os, json, tempfile
-import cv2, numpy as np, gradio as gr
+import gradio as gr
 from PIL import Image
-
-# ---------------------- Paths (hair/ first) ----------------------
-BASE_DIR = os.path.dirname(os.path.abspath(__file__))
-CANDIDATES = [
-    os.path.join(BASE_DIR, "hair"),                    # <- your folder
-    os.path.join(BASE_DIR, "assets", "hairstyles"),
-    os.path.join(BASE_DIR, "assets", "Hairstyles"),
-    os.path.join(BASE_DIR, "hairstyles"),
-]
-HAIR_DIR = None
-for p in CANDIDATES:
-    if os.path.isdir(p):
-        HAIR_DIR = p
-        break
-if HAIR_DIR is None:  # create canonical path if nothing exists yet
-    HAIR_DIR = os.path.join(BASE_DIR, "hair")
-    os.makedirs(HAIR_DIR, exist_ok=True)
-
-META_PATH = os.path.join(HAIR_DIR, "meta.json")  # optional per-style anchors
-
-# ---------------------- MediaPipe ----------------------
-try:
-    import mediapipe as mp
-except Exception as e:
-    raise RuntimeError(f"Mediapipe import failed. Check requirements.txt pins. Details: {e}")
-
-mp_face_mesh = mp.solutions.face_mesh
-mp_selfie_seg = mp.solutions.selfie_segmentation
-LM = {"left_eye_outer": 33, "right_eye_outer": 263, "mid_forehead": 10}
-
-# ---------------------- Helpers ----------------------
+import os
 def load_hairstyles():
-    try:
-        files = [f for f in os.listdir(HAIR_DIR) if f.lower().endswith(".png")]
-    except FileNotFoundError:
-        files = []
-    files.sort()
-    return files
-
-HAIR_FILES = load_hairstyles()
-
-def load_meta():
-    if os.path.exists(META_PATH):
-        try:
-            with open(META_PATH, "r") as f:
-                m = json.load(f)
-            return m if isinstance(m, dict) else {}
-        except Exception:
-            return {}
-    return {}
-META = load_meta()
-
-def detect_face_keypoints(img_bgr):
-    h, w = img_bgr.shape[:2]
-    with mp_face_mesh.FaceMesh(
-        static_image_mode=True, max_num_faces=1, refine_landmarks=True,
-        min_detection_confidence=0.6
-    ) as fm:
-        res = fm.process(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB))
-        if not res.multi_face_landmarks:
-            return None
-        lm = res.multi_face_landmarks[0].landmark
-        def xy(i): return np.array([lm[i].x*w, lm[i].y*h], dtype=np.float32)
-        return np.stack([xy(LM["left_eye_outer"]), xy(LM["right_eye_outer"]), xy(LM["mid_forehead"])])
-
-def person_mask(img_bgr):
-    with mp_selfie_seg.SelfieSegmentation(model_selection=1) as seg:
+folder = "hairstyles"
+if not os.path.exists(folder):
+return []
+return [
+Image.open(os.path.join(folder, f)).convert("RGBA")
+for f in sorted(os.listdir(folder)) if f.endswith(".png")
+]
+hairstyles = load_hairstyles()
+def apply_hairstyle(user_img, style_index, x_offset, y_offset, scale):
+if user_img is None or not hairstyles:
+return None
+    user_img = user_img.convert("RGBA")
+    base_w, base_h = user_img.size
+
+    hairstyle = hairstyles[style_index]
+
+    # Resize the hairstyle based on scale
+    new_size = (int(base_w * scale), int(hairstyle.height * (base_w * scale / hairstyle.width)))
+    hairstyle = hairstyle.resize(new_size)
+
+    # Create a blank transparent image to position the hairstyle
+    composite = Image.new("RGBA", user_img.size)
+    paste_x = int((base_w - new_size[0]) / 2 + x_offset)
+    paste_y = int(y_offset)
+    composite.paste(hairstyle, (paste_x, paste_y), hairstyle)
+
+    # Overlay it
+    result = Image.alpha_composite(user_img, composite)
+    return result.convert("RGB")
+
+with gr.Blocks() as demo:
+gr.Markdown("## 💇 Salon Virtual Hairstyle Try-On (Adjustable)")
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="📷 Upload an Image")
+            style_slider = gr.Slider(0, max(len(hairstyles)-1, 0), step=1, label="🎨 Select Hairstyle")
+            x_offset = gr.Slider(-200, 200, value=0, step=1, label="⬅️➡️ Move Left / Right")
+            y_offset = gr.Slider(-200, 200, value=0, step=1, label="⬆️⬇️ Move Up / Down")
+            scale = gr.Slider(0.3, 2.0, value=1.0, step=0.05, label="📏 Scale Hairstyle")
+            apply_btn = gr.Button("✨ Apply Hairstyle")
+        with gr.Column():
+            result_output = gr.Image(label="🔍 Result Preview")
+
+    apply_btn.click(
+        fn=apply_hairstyle,
+        inputs=[image_input, style_slider, x_offset, y_offset, scale],
+        outputs=result_output
+    )
+
+demo.launch()