Create app.py

app.py

@@ -0,0 +1,55 @@
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.responses import JSONResponse
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+import logging
+from PIL import Image
+import io
+
+# Configure logging
+logging.basicConfig(level=logging.DEBUG)
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Load your trained model
+model = load_model('model.h5')
+class_names = ['melanoma', 'nevus', 'seborrheic_keratosis']
+
+def preprocess_image(img, target_size):
+    """Resize and preprocess the image for the model."""
+    if img.mode != "RGB":
+        img = img.convert("RGB")
+    img = img.resize(target_size)
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    return img_array
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    if not file:
+        raise HTTPException(status_code=400, detail="No file provided")
+    try:
+        # Read the file's content into a BytesIO object
+        img_bytes = io.BytesIO(await file.read())
+        
+        # Use PIL to open the image
+        img = Image.open(img_bytes)
+        img_array = preprocess_image(img, (224, 224))
+        
+        # Make prediction
+        predictions = model.predict(img_array)
+
+        predicted_class = np.argmax(predictions, axis=1)
+        
+        # Return the prediction
+        predictions = {
+            'class': class_names[predicted_class[0]],
+            'confidence': float(predictions[0][predicted_class[0]])
+        }
+        return JSONResponse(content=predictions)
+    except Exception as e:
+        logging.debug(f"Error processing the file: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Error processing the file: {str(e)}")
+