init

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README.md

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+---
+title: Skin Disease Classifier
+emoji: 🚀
+colorFrom: pink
+colorTo: blue
+sdk: gradio
+sdk_version: 5.25.2
+app_file: model.py
+pinned: false
+license: mit
+short_description: To identify the skin disease, finetuned convnext model
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

convnext_base_finetuned.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6c8dadf0c017fd3749a0dc291a1d9249bdba618c6351964d1fe65a85c07a578b
+size 350500802

model.py

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+import torch
+import gradio as gr
+from transformers import ConvNextForImageClassification, AutoImageProcessor
+from PIL import Image
+
+class_names = ['Acne and Rosacea Photos', 'Actinic Keratosis Basal Cell Carcinoma and other Malignant Lesions', 'Atopic Dermatitis Photos',
+             'Bullous Disease Photos', 'Cellulitis Impetigo and other Bacterial Infections', 'Eczema Photos', 'Exanthems and Drug Eruptions', 'Hair Loss Photos Alopecia and other Hair Diseases',
+               'Herpes HPV and other STDs Photos', 'Light Diseases and Disorders of Pigmentation', 'Lupus and other Connective Tissue diseases', 'Melanoma Skin Cancer Nevi and Moles',
+                 'Nail Fungus and other Nail Disease', 'Poison Ivy Photos and other Contact Dermatitis',
+               'Psoriasis pictures Lichen Planus and related diseases', 'Scabies Lyme Disease and other Infestations and Bites', 'Seborrheic Keratoses and other Benign Tumors',
+                 'Systemic Disease', 'Tinea Ringworm Candidiasis and other Fungal Infections', 'Urticaria Hives', 'Vascular Tumors', 'Vasculitis Photos', 'Warts Molluscum and other Viral Infections']
+
+model = ConvNextForImageClassification.from_pretrained("facebook/convnext-base-224")
+
+# Redefine classifier for 23 classes
+model.classifier = torch.nn.Linear(in_features=1024, out_features=23)
+
+# Load model configuration and weights manually
+model.load_state_dict(torch.load("convnext_base_finetuned.pth", map_location="cpu"))  # Load your finetuned weights
+model.eval()
+
+# Load the processor
+processor = AutoImageProcessor.from_pretrained("facebook/convnext-base-224")
+
+# Define a function to predict the class from an image
+def predict(image):
+    # Preprocess the image
+    inputs = processor(images=image, return_tensors="pt")
+    
+    # Perform inference
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predicted_class = torch.argmax(outputs.logits, dim=1).item()
+    return predicted_class, class_names[predicted_class]
+
+
+# Create Gradio interface for user input
+iface = gr.Interface(fn=predict, inputs=gr.Image(type="pil"), outputs=gr.Textbox())
+
+iface.launch()

requirements.txt

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+torch==1.13.1
+transformers==4.27.0
+gradio==3.0.1
+numpy<2