helo

.gitattributes

@@ -33,4 +33,3 @@ 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

@@ -1,12 +0,0 @@
----
-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

@@ -1,155 +0,0 @@
-import warnings
-from fastai.vision.all import *
-import gradio as gr
-import pathlib
-
-# Suppress the pickle warning for demo purposes
-warnings.filterwarnings("ignore", category=UserWarning, module="fastai.learner")
-
-# Load the model
-learn = load_learner("export.pkl")
-labels = learn.dls.vocab
-
-def predict(img):
-    """
-    Predict skin lesion classification for the given image.
-    
-    Args:
-        img: PIL Image object from Gradio
-        
-    Returns:
-        dict: Classification probabilities for each class
-    """
-    try:
-        # Convert to PILImage if needed
-        img = PILImage.create(img)
-        pred, pred_idx, probs = learn.predict(img)
-        
-        # Return as dictionary with float probabilities for JSON serialization
-        return {labels[i]: float(probs[i]) for i in range(len(labels))}
-    except Exception as e:
-        raise gr.Error(f"Error processing image: {str(e)}")
-
-# App metadata
-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>"
-
-# Example images
-examples = ['img1.jpg', 'img2.jpg', 'img3.jpg'] if all(pathlib.Path(f).exists() for f in ['img1.jpg', 'img2.jpg', 'img3.jpg']) else None
-
-# Create the modern Gradio interface
-def create_interface():
-    """Create and return the Gradio interface"""
-    
-    with gr.Blocks(
-        title=title,
-        theme=gr.themes.Soft(),
-        css=".gradio-container {max-width: 700px; margin: auto;}"
-    ) as demo:
-        
-        gr.Markdown(f"# {title}")
-        gr.Markdown(description)
-        
-        with gr.Row():
-            with gr.Column():
-                image_input = gr.Image(
-                    label="Upload Skin Lesion Image",
-                    type="pil",
-                )
-                
-                predict_btn = gr.Button(
-                    "Classify Lesion", 
-                    variant="primary",
-                )
-            
-            with gr.Column():
-                output_label = gr.Label(
-                    label="Classification Results",
-                )
-        
-        # Add examples if available
-        if examples:
-            gr.Examples(
-                examples=examples,
-                inputs=image_input,
-                outputs=output_label,
-                fn=predict,
-                cache_examples=True
-            )
-        
-        # Event handlers
-        predict_btn.click(
-            fn=predict,
-            inputs=image_input,
-            outputs=output_label,
-            show_progress=True
-        )
-        
-        # Also trigger on image upload
-        image_input.upload(
-            fn=predict,
-            inputs=image_input,
-            outputs=output_label,
-            show_progress=True
-        )
-        
-        gr.Markdown(article)
-    
-    return demo
-
-if __name__ == '__main__':
-    # Create and launch the interface
-    demo = create_interface()
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False,
-        show_error=True
-    )
-
-
-
-
-
-
-
-
-
-
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-
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-
-
-# 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()

export.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ddee7a008adf2aad9f0c445aa6358b109026116573b66f5b3fba2a18e219c804
-size 103303797

requirements.txt

@@ -1,17 +0,0 @@
-# Core dependencies
-fastai>=2.7.10,<2.8.0
-torch>=1.13.0
-torchvision>=0.14.0
-
-# Gradio - modern version
-gradio>=4.0.0,<5.0.0
-
-# Image processing
-Pillow>=9.0.0
-scikit-image>=0.19.0
-
-# Utilities
-pathlib-abc>=0.1.0
-
-# Optional: For better performance
-uvicorn[standard]>=0.18.0