Initial commit

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text

README copy.md

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+---
+title: Skinlesion
+emoji: 🌍
+colorFrom: green
+colorTo: blue
+sdk: gradio
+sdk_version: 4.29.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from fastai.learner import *
+from fastai.vision.all import *
+import gradio as gr
+
+learn = load_learner("export.pkl")
+labels = learn.dls.vocab
+
+def predict(img):
+    # Ensure img is a PILImage object; if it's already PIL, create won't harm it.
+    # If it's a numpy array (e.g. from Gradio's Image component with type="numpy"),
+    # PILImage.create will convert it.
+    img = PILImage.create(img)
+    pred, pred_idx, probs = learn.predict(img)
+    # Ensure probs are float for JSON serialization if necessary
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+title = "Skin Lesion Classifier [RESNET 50]"
+description = "A skin lesion classifier trained on the ISIC2019 dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
+article = "<p style='text-align: center'><a href='https://challenge.isic-archive.com/data/' target='_blank'>Link to ISIC Dataset</a></p>"
+examples = ['img1.jpg', 'img2.jpg', 'img3.jpg']
+
+# Updated Gradio Interface
+# gr.inputs.Image becomes gr.Image
+# gr.outputs.Label becomes gr.Label
+# 'shape' parameter for gr.Image is deprecated. Image size is handled by the component or by resizing in the predict function if needed.
+# 'type="pil"' ensures the input to predict() is a PIL Image, aligning with PILImage.create().
+# 'num_top_classes' is a valid parameter for gr.Label.
+# 'interpretation' parameter is deprecated and removed.
+# 'enable_queue' is True by default, so explicit setting is often not needed but kept for clarity.
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload Skin Lesion Image"),
+    outputs=gr.Label(num_top_classes=3, label="Classification Results"),
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+    # interpretation='default', # Removed
+    enable_queue=True
+)
+
+if __name__ == '__main__':
+    iface.launch()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+# import gradio as gr
+# from fastai.vision.all import *
+# import skimage
+# #Importing necessary libraries
+# import gradio as gr
+# #import scikit-learn as sklearn
+# from fastai.vision.all import *
+# from sklearn.metrics import roc_auc_score
+
+# learn = load_learner('export.pkl')
+
+# labels = learn.dls.vocab
+# def predict(img):
+#     img = PILImage.create(img)
+#     pred,pred_idx,probs = learn.predict(img)
+#     return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+
+# examples = ['img1.jpg','img2.jpg','img3.jpg']
+
+# #Launching the gradio application
+# gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),
+#              outputs=gr.outputs.Label(num_top_classes=1),
+#              title=title,
+#              description=description,article=article,
+#              examples=examples,
+#              enable_queue=enable_queue).launch(inline=False)
+
+# #gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(224, 224)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()

requirements.txt

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+fastai~=2.7.10
+gradio~=4.0
+scikit-image
+Werkzeug