Create app.py

app.py

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+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.responses import JSONResponse
+from PIL import Image
+import numpy as np
+import cv2
+import tensorflow as tf  # Assuming you're using TensorFlow for loading your model
+
+app = FastAPI()
+
+# Load your model
+model = tf.keras.models.load_model("Brain_tumor_pred_large.h5")
+
+def predict_tumor(image: Image.Image):
+    # Convert the PIL image to OpenCV format
+    opencv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+    img = cv2.resize(opencv_image, (128, 128))
+    img = img.reshape(1, 128, 128, 3)
+    
+    # Predict using the model
+    predictions = model.predict(img)[0]  # Get probabilities for each class
+    predicted_class = np.argmax(predictions)  # Index of the predicted class
+    confidence = predictions[predicted_class]  # Confidence of the predicted class
+    
+    # Determine if a tumor is present
+    if confidence < 0.20:
+        if confidence < 0.10:
+            result = "No Tumor"
+            confidence = 1.0
+        else:
+            result = "Uncertain"
+    else:
+        result = "No Tumor" if predicted_class == 1 else "Tumor Detected"
+    
+    return {"result": result, "confidence": f"{confidence:.2%}"}
+
+@app.post("/predict")
+async def predict(upload: UploadFile = File(...)):
+    try:
+        # Open and process the uploaded image file
+        image = Image.open(upload.file)
+        result = predict_tumor(image)
+        return JSONResponse(content=result)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# To run the FastAPI app, use the following command:
+# uvicorn app:app --reload