Initial commit

.gitignore

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+venv

app.py

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+import gradio as gr
+from transformers import AutoModelForImageClassification, AutoImageProcessor
+import torch
+from PIL import Image
+
+# Define model repository
+model_name = "Aya-Ch/brain-tumor-classifier"
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+model = AutoModelForImageClassification.from_pretrained(model_name)
+
+
+# Define brain tumor classes
+tumor_classes = ['meningioma', 'glioma', 'pituitary tumor']
+
+def predict(image):
+    try:
+        # Process the image using the processor
+        processed_image = processor(images=image, return_tensors="pt")['pixel_values']
+        
+        with torch.no_grad():
+            outputs = model(processed_image)
+            logits = outputs.logits  # Get classification scores
+            probs = torch.nn.functional.softmax(logits, dim=-1)
+        
+        # Convert tensor outputs to Python numbers
+        results = {tumor_classes[i]: float(probs[0, i]) for i in range(len(tumor_classes))}
+        return results
+    
+    except Exception as e:
+        return {"Error": f"Failed to process image: {str(e)}"}
+
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=predict, 
+    inputs=gr.Image(type="pil"),  # Accepts image input
+    outputs=gr.Label(label="Tumor Classification"), 
+    title="Brain Tumor Classifier",
+    description="Upload an MRI scan to classify the type of brain tumor(meningioma, glioma or pituitary tumor)",
+    allow_flagging="never"
+)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+transformers
+torch
+Pillow
+
+
+
+
+
+# This is only needed for local deployment
+gradio