Upload 8 files

app.py

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+from pathlib import Path
+from fastai.vision.all import *
+import gradio as gr
+
+examples = [
+    ["project/WBC-Benign-017.jpg"],  # Replace with the actual paths to your images
+    ["project/WBC-Benign-030.jpg"],
+    ["project/WBC-Malignant-Early-027.jpg"],
+    ["project/WBC-Malignant-Pre-019.jpg"],
+    ["project/WBC-Malignant-Pro-027.jpg"]
+]
+# Correctly format the path for Windows
+model_path = Path(r'efficientnet_b3_model.pkl')
+
+# Load the model
+learn = load_learner(model_path, cpu=True)
+
+# Define the prediction function
+def classify_image(image):
+    pred, idx, probs = learn.predict(image)
+    return {learn.dls.vocab[i]: float(probs[i]) for i in range(len(probs))}
+
+# Set up the Gradio interface
+interface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title="EfficientNet B3 Image Classifier",
+    examples= examples,
+    description="Upload an image to classify using the trained EfficientNet B3 model.",
+)
+
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch(share=True)

efficientnet_b3_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:634b7af7b0aa16fa0439e862e0db3fed5289675b10b18a843cef65c2354d232c
+size 49892893

requirements.txt

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+gradio
+fastai
+timm==0.6.13