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Acnes_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:aa18e19b1c76c33a2edc8c5a820497fe9ff4e023be88cabae83d1ce9e289da47
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README.md

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 ---
-title: Acnes
-emoji: 🌍
-colorFrom: blue
-colorTo: indigo
+title: Acne Detection AI 🔥
+emoji: 🔥
+colorFrom: indigo
+colorTo: yellow
 sdk: gradio
 sdk_version: 5.25.2
 app_file: app.py
-pinned: false
+pinned: true
 license: apache-2.0
-short_description: 'Acne Segmentation and Classification '
+short_description: Advanced AI for Acne Segmentation & Severity Classification
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🧠 Acne Detection & Classification with Deep Learning
+
+![banner](https://huggingface.co/spaces/RihemXX/Acnes/resolve/main/images/output1.png) <!-- You can upload a real image here -->
+
+This application demonstrates a powerful **AI-driven pipeline** for **acne detection, segmentation**, and **severity classification**, combining:
+
+- A **UNet-based CNN** for precise acne lesion segmentation (pixel-wise accuracy ≈ **96–98%**)
+- A **transformer-based classifier** (fine-tuned Vision Transformer) for grading severity based on dermatological standards
+
+Try it by uploading a face image.  
+It will return:
+✅ a **visual overlay** showing detected acne regions  
+✅ a **severity label** (from clear skin to very severe acne)
+
+---
+
+## 🧪 Model Architecture
+
+### 🔹 Segmentation Model
+- Architecture: `UNet` with `ResNet34` backbone (from [smp](https://github.com/qubvel/segmentation_models.pytorch))
+- Optimized for: Binary mask prediction of acne regions
+- Trained on: Annotated dermatological datasets
+- Accuracy: **Pixel Accuracy ≈ 98%**, **IoU ≈ 91%**
+
+### 🔹 Classification Model
+- Architecture: Vision Transformer (ViT)
+- Source: [`imfarzanansari/skintelligent-acne`](https://huggingface.co/imfarzanansari/skintelligent-acne)
+- Labels: From `Level -1 (Clear)` to `Level 4 (Very Severe Acne)`
+- Input: Facial image
+- Output: Severity level + confidence score
+
+---
+
+## 📈 Example Output
+
+| Input Image | Segmentation Overlay | Acne Level |
+|-------------|----------------------|------------|
+| ![input](https://huggingface.co/spaces/RihemXX/Acnes/resolve/main/images/image.png) | ![overlay](https://huggingface.co/spaces/RihemXX/Acnes/resolve/main/images/output.png) | Level 2: Moderate Acne |
+
+---
+
+## 💡 Use Cases
+- Dermatology research and screening
+- Skincare and cosmetic product testing
+- Automated health monitoring platforms
+
+---
+
+## 🧩 Tech Stack
+- `PyTorch`, `Segmentation Models PyTorch`
+- `Transformers` by Hugging Face
+- `Albumentations` for fast preprocessing
+- `OpenCV`, `Gradio` for live interface
+
+
+
+
+## 🤖 Try it now!
+
+Click below and upload a photo to get real-time predictions:
+
+Check out the configuration reference at [https://huggingface.co/docs/hub/spaces-config-reference](https://huggingface.co/docs/hub/spaces-config-reference)

app.py

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+import gradio as gr
+import torch
+import cv2
+import numpy as np
+import segmentation_models_pytorch as smp
+from albumentations import Compose, Normalize, Resize
+from albumentations.pytorch import ToTensorV2
+from transformers import pipeline
+
+# Constants
+MODEL_PATH = "Acnes_model.pth"
+MASK_OPACITY = 0.9
+DEVICE = torch.device("cpu")
+
+# ----------------- LOAD MODEL -----------------
+model = torch.load(MODEL_PATH, map_location=DEVICE)
+model.to(DEVICE)
+model.eval()
+
+# ----------------- Classification Model -----------------
+classification_pipe = pipeline("image-classification", model="imfarzanansari/skintelligent-acne")
+
+# ----------------- Preprocessing -----------------
+def preprocess_image(image, img_size=(256, 256)):
+    transform = Compose([
+        Resize(*img_size),
+        Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        ToTensorV2()
+    ])
+    augmented = transform(image=image)
+    tensor_image = augmented["image"].unsqueeze(0)
+    return tensor_image, image
+
+# ----------------- Inference -----------------
+def predict_mask(model, image_tensor):
+    with torch.no_grad():
+        image_tensor = image_tensor.to(DEVICE)
+        output = model(image_tensor)
+        mask = torch.sigmoid(output)
+        return mask.squeeze().cpu().numpy()
+
+# ----------------- Overlay -----------------
+def overlay_mask(image, mask, color=(255, 0, 0), alpha=MASK_OPACITY):
+    mask_resized = cv2.resize(mask, (image.shape[1], image.shape[0]), interpolation=cv2.INTER_NEAREST)
+    overlay = image.copy()
+    mask_colored = np.zeros_like(image, dtype=np.uint8)
+    mask_colored[mask_resized > 0.2] = color
+    blended = cv2.addWeighted(overlay, 1, mask_colored, alpha, 0)
+    return blended
+
+# ----------------- Severity Mapping -----------------
+def map_classification_label_to_level(label):
+    levels = {
+        'level -1': "Level -1: Clear Skin",
+        'level 0': "Level 0: Occasional Spots",
+        'level 1': "Level 1: Mild Acne",
+        'level 2': "Level 2: Moderate Acne",
+        'level 3': "Level 3: Severe Acne",
+        'level 4': "Level 4: Very Severe Acne"
+    }
+    return levels.get(label, "Unknown")
+
+# ----------------- Combined Prediction -----------------
+def predict(image):
+    input_tensor, original_image = preprocess_image(image)
+    predicted_mask = predict_mask(model, input_tensor)
+    overlayed_image = overlay_mask(original_image, predicted_mask, color=(255, 0, 0), alpha=MASK_OPACITY)
+
+    # Save to disk for classification model (transformers pipeline accepts paths)
+    temp_path = "/tmp/temp_image.jpg"
+    cv2.imwrite(temp_path, cv2.cvtColor(original_image, cv2.COLOR_RGB2BGR))
+
+    classification_result = classification_pipe(temp_path)
+    predicted_label = max(classification_result, key=lambda x: x['score'])['label']
+    confidence = max(classification_result, key=lambda x: x['score'])['score']
+    severity = map_classification_label_to_level(predicted_label)
+
+    return overlayed_image, f"{severity}\nConfidence: {confidence:.2f}"
+
+# ----------------- Gradio UI -----------------
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="numpy", label="Upload Face Image"),
+    outputs=[
+        gr.Image(label="Segmentation Overlay"),
+        gr.Text(label="Acne Severity Prediction")
+    ],
+    title="Acne Segmentation & Severity Classification",
+    description="Upload a facial image to detect acne regions and predict severity level using UNet and a pretrained classifier."
+)
+
+demo.launch()

requirements.txt

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+gradio
+torch
+torchvision
+segmentation-models-pytorch
+opencv-python-headless
+albumentations
+transformers
+numpy
+Pillow