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.dockerignore

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+# Git
+.git
+.gitignore
+
+# Python
+__pycache__/
+*.pyc
+
+# Virtual env
+venv/
+
+BrainTumorDetectionModel/
+.env
+
+# ML artifacts (if regenerated)
+logs/
+checkpoints/
+*.pt
+*.pth
+*.ckpt
+
+# OS
+.DS_Store
+Thumbs.db

app.py

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+from fastapi import FastAPI, UploadFile, File
+from cnnmodels import classify_tumor
+
+app = FastAPI(title="Brain Tumor CNN Service")
+
+@app.post("/classify")
+async def classify(file: UploadFile = File(...)):
+    image_bytes = await file.read()
+    result = classify_tumor(image_bytes)
+    return result

cnnmodels.py

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+from keras.layers import TFSMLayer
+from PIL import Image
+import numpy as np
+import io
+
+CLASS_NAMES = ['glioma', 'meningioma', 'no tumor', 'pituitary']
+
+# Load models ONCE
+
+
+classification_model = TFSMLayer(
+    "BrainTumorClassificationModel/model",
+    call_endpoint="serving_default"
+)
+
+def preprocess_image_bytes(image_bytes, target_size=(224, 224)):
+    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    image = image.resize(target_size)
+    image = np.array(image).astype("float32") / 255.0
+    image = np.expand_dims(image, axis=0)
+    return image
+
+def classify_tumor(image_bytes):
+    # 1. Preprocess the image
+    image = preprocess_image_bytes(image_bytes)
+
+    # 2. Run Classification directly
+    # Removed the detection_model block entirely
+    class_pred_tensor = classification_model(image)["output_0"]
+    class_pred = class_pred_tensor.numpy()
+    
+    # 3. Get the index of the highest confidence score
+    idx = int(np.argmax(class_pred))
+    confidence = float(np.max(class_pred) * 100)
+    tumor_type = CLASS_NAMES[idx]
+
+    # 4. Determine has_tumor status based on the classification result
+    # We assume "no tumor" is a string in your CLASS_NAMES list
+    is_tumor_detected = tumor_type.lower() != "no tumor"
+
+    return {
+        "has_tumor": is_tumor_detected,
+        "tumor_type": tumor_type,
+        "confidence": confidence
+    }